This paper presents an optimization method for hybrid energy systems based on Model Predictive Control (MPC), Long Short-Term Memory (LSTM) networks, and Kolmogorov–Arnold Networks (KANs). The proposed method is applied to a high-altitude wind energy work umbrella control system, where it aims to enhance the stability and efficiency of energy utilization. The work umbrella system integrates wind and solar energy sources, with energy stored in a battery and used to control the umbrella’s operations. The MPC framework is employed to optimize control actions by solving a finite-horizon optimization problem, ensuring the battery State of Charge (SOC) remains within an optimal range. The LSTM network provides accurate predictions of environmental conditions, including wind speed and solar irradiance, which are essential for MPC’s decision-making process. To address complex nonlinearities in the system, the KAN is utilized to model and approximate these dynamics, refining the LSTM predictions. The integration of these advanced control strategies enables the system to handle varying operational conditions and maintain optimal performance. The case study demonstrates the effectiveness of the MPC-LSTM-KAN approach, revealing improvements in the SOC stability, energy efficiency, and operational endurance of the high-altitude wind energy work umbrella system. The results indicate that this hybrid optimization method offers a robust solution for managing hybrid energy systems in dynamic environments.

With the application of inspection robots, the demand for automatic detection and identification of pointers in pumping stations, substations, and laboratories has increased. This paper proposes the YOLOX-CAlite meter detection algorithm to address the lack of performance in detecting targets involved in the specific meter detection process. The core of the YOLOX-CAlite is improving the drawbacks of the original algorithm with its large backbone network, a large number of parameters, and large calculation volume. It mainly consists of using data augmentation on the input side. We removed the Focus structure from the original YOLOX and replace it with a convolutional pooling form. The backbone was replaced with Shufflenet v2 and the Ghost module was introduced into the neck to improve real-time performance. Meanwhile, the neck was changed to BiFPN structure to enhance feature fusion. coordinate attention was introduced in front of the detection head to enhance feature extraction. Finally, the binary cross-entropy loss in YOLOX was replaced by focal loss, and the multi-task joint loss of the algorithm was optimized using the complete-IoU loss function. The experimental results on our dataset showed the improved YOLOX-CAlite achieved an AP of 90.4%. Compared to the YOLOX-s and YOLOv5, YOLOX-CAlite improved the AP by 1.4% and 2.2%. And the gflops have been reduced massively, from 26.6 gflops to 8.8 gflops, a reduction of about 68%. The detection speed increased by 25% while the number of parameter sizes is reduced. The model size has been reduced from 17.2 M in YOLOX-s to 4.89 M, a reduction of about 71%. The inspection robots can identify the meter better in the following works.

The energy managment standards specify in-factory rules of operation control,measurement and recording keeping, and maintenance and inspection points and frequencies of energy using equipment for purpose of improving energy saving. However, it is the researches on energy standard that have been neglected in China. As a result, the boiler operator in factory actually don't know how to take action toward energy conservation and how to improve the operation of boilers. All of these should attribute to their having no specified rules ,applied to energy saving ,to guide their operation. Japanese energy management system of the boiler have been the leader of the world. Not only have they established a series of complete energy management standards[1],but also they have made it possess legal effect and force enterprises to observe. Based on these,this thesis will analyze Japanese energy management standards and propose some ideas for energy saving in China.

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework.

Automated defect inspection for transparent optical lenses remains challenging because specular reflections can obscure weak defects and produce glare-induced false responses. This paper presents a confidence-driven end-to-end active inspection framework for online quality control in computer-integrated manufacturing. The framework integrates a Transformer-enhanced You Only Look Once (YOLO) detector (TE-YOLO), a confidence-driven proximal policy optimization (PPO) controller (CDR-PPO), and a time-sequential camera viewpoint adjustment process. TE-YOLO introduces a C3 module with a Transformer encoder (C3TR) before the detection head to combine convolutional local feature extraction with global context modeling for scratch and foreign particle detection. In the proposed architecture, the detector outputs both visual features and defect confidence scores, and these outputs are converted into a threshold-aware reward signal that directly participates in PPO advantage estimation and policy optimization, thereby coupling visual perception with discrete camera pose control. Unlike active vision methods whose rewards are based on geometric coverage or model uncertainty, CDR-PPO trains the viewpoint policy against the same detector threshold that accepts an observation. The resulting closed loop repeatedly performs image acquisition, defect perception, reward construction, policy update, camera adjustment, and re-observation; the detector and the policy are trained separately. The lens images are acquired on a physical inspection apparatus with an industrial camera on an adjustable support. TE-YOLO is evaluated on 208 physical lens images, where it reaches an average precision at an intersection-over-union threshold of 0.50 (AP50) of 0.995 and an average precision averaged over thresholds from 0.50 to 0.95 (AP50:95) of 0.865 on a near-duplicate-grouped split, the same accuracy as that of a plain C3 variant without the Transformer encoder. Physical acquisition sequences on the apparatus show that changing the camera configuration shifts the glare relative to the lens and the defect. CDR-PPO is trained and evaluated in a simulated inspection environment that reproduces the geometry of the apparatus and generates the confidence and the state features. On held-out test scenarios, it reaches the confidence threshold in 87.3% of episodes with 5.1 observations per episode, compared with 42.4% for a fixed viewpoint and 22.1 observations per episode for a greedy confidence search. Within a budget of 10 observations, CDR-PPO recovers 77.8% of the episodes that start below the threshold, whereas the greedy search recovers none. These results support the integration of adaptive visual inspection into automated manufacturing quality control workflows.