Overcoming the inefficiency in the procurement of railway wagon components is a crucial step in promoting the modernization of railway enterprises' governance capabilities. This study formulates components purchasing for railway wagon as a two-stage Markov Decision Process (MDP) that jointly optimizes planned purchase quantities and supplier selection. Inputs to the MDP (demand signals and inventory) are treated as pre-processed operational forecasts from the enterprise data platform; the paper therefore focuses on the design of state/action spaces, reward construction, and the value-iteration solution for producing actionable purchasing decision. Using real business records from a large railway equipment group, simulation results demonstrate that the MDP-based decision reduces total purchasing cost, shortens purchasing lead time, and improves supplier quality metrics compared with legacy manual strategies.
In intelligent welding, achieving the automation of weld quality inspection is a significant challenge, and weld seam marking is of crucial importance. For this purpose, a method based on edge detection using binary image preprocessing was developed on the MATLAB platform. Compared with the traditional multi-sensor fusion approach, this method does not require complex sensor integration, simplifying the implementation process. Compared with neural network methods, it is more flexible and simpler. The method first preprocesses the image into a binary image and then compares the weld seam feature marking with the Roberts, Prewitt, Sobel, and Canny operators. The results show that the Canny operator demonstrates a significant performance advantage in the comparison of four indicators: point sharpness, entropy, average gradient, and Quality Assessment of Blended Features. Its performance is 3 to 25 times that of other operators, and it performs best in weld seam feature texture detection, showing high robustness.
This study proposes a wave-induced pneumatic energy converter that transforms vertical wave motion into airflow to produce electrical power. A laboratory-scale prototype was fabricated and tested by means of a Scotch-yoke mechanism that replicates wave-induced displacement. Owing to its compact architecture and reduced component count, the device can be manufactured at relatively low cost. Under regular-wave conditions (height = 0.5 m; period = 1 s), the mean outlet air velocity reached 30.72 ms-1 and the peak pneumatic power was 43.34 W. The measured energy-conversion efficiency was 25.71%, corresponding to a volumetric energy density of approximately 875 Wm-3. Although the experiments were restricted to laboratory scale, the results confirm proof-of-concept. The observed performance and characteristics indicate potential for deployment in near-shore and off-shore environments; nevertheless, validation for full-scale sea conditions remains outside the scope of the present work.
The global information perception of power equipment is the key to supporting the efficient and stable operation of the new power system. This paper adopts the digital twin technology and constructs a new framework for panoramic perception of transformer vibration status. To address the difficulties in obtaining power defect samples, the dominance of normal samples, and the reliance on large-scale data of multi-modal large models, a power defect data enhancement method based on diffusion models is proposed. Under the premise of ensuring the rationality of the generated image structure, this method utilizes the trained multi-modal large model Qwen-VL-Max to extract the high-order semantic information of real power scene images and combines the prompt engineering technology to generate synthetic images with power defect features and high quality. Moreover, for the common problem of data sparsity in multi-modal recommendation systems, a multi-modal fusion recommendation algorithm based on collaborative self-supervised learning is proposed. This algorithm effectively enhances the representation ability of multi-modal data through the joint learning of the deep features of the data, thereby alleviating the problem of performance decline in recommendations caused by data sparsity. Experimental results show that, compared with the current mainstream multi-modal recommendation algorithms, this algorithm has significant improvements in multiple recommendation evaluation indicators.
The financial sector is undergoing a profound transformation with the integration of artificial intelligence (AI) and cloud computing technologies. A notable advancement is the deployment of a deep learning classification system that integrates Bidirectional Gated Recurrent Units (BiGRU) with the Fruit Fly Optimization Algorithm (FOA) to enhance complex banking operations. The BiGRU model efficiently analyzes financial transactions, customer profiles, and risk patterns by processing sequential data with long-term dependencies. FOA, inspired by the foraging behavior of fruit flies, optimizes the network's performance and computational efficiency. A cloud-based implementation of the BiGRU-FOA framework ensures scalability, real-time processing, and seamless integration with existing banking infrastructure. Experimental results demonstrate that BiGRU-FOA outperforms traditional machine learning techniques and standalone deep learning models in financial dataset classification, achieving superior accuracy, precision, and recall. This model enhances fraud detection, customer segmentation, and credit risk assessment, paving the way for more efficient and intelligent banking operations. By leveraging this advanced AI-driven framework, banks can improve decision-making processes, enhance operational efficiency, and offer personalized financial services. This research highlights the potential of deep learning and optimization technologies in revolutionizing the banking sector, enabling a more secure, efficient, and customer-centric approach.
