The integration of electricity, gas, and heat (cold) in the integrated energy system (IES) breaks the limitation of every single energy source, which is the development trend of future energy systems. To realize the coordinated planning of “source-network-load-storage,” the IES has to be conducive to improving energy efficiency, bringing economic and environmental benefit, and achieving sustainable development of energy. In this paper, the techniques and methods involved in IES planning are summarized. First, the structure and characteristics of the IES are briefly introduced. Second, the key findings of the IES planning are summarized from four perspectives: source, network, load, and storage. Then, the modeling methods of the IES collaborative planning are summarized, and the optimization methods for solving complex planning problems are analyzed. Compared with previous reviews, this paper focuses on the modeling of multi-energy coupling of each part of source-network-load-storage and modeling of the overall collaborative planning. Finally, the future research direction of IES planning is forecast.

Chip pad inspection is of great practical importance for chip alignment inspection and correction. It is one of the key technologies for automated chip inspection in semiconductor manufacturing. When applying deep learning methods for chip pad inspection, the main problem to be solved is how to ensure the accuracy of small target pad detection and, at the same time, achieve a lightweight inspection model. The attention mechanism is widely used to improve the accuracy of small target detection by finding the attention region of the network. However, conventional attention mechanisms capture feature information locally, which makes it difficult to effectively improve the detection efficiency of small targets from complex backgrounds in target detection tasks. In this paper, an OCAM (Object Convolution Attention Module) attention module is proposed to build long-range dependencies between channel features and position features by constructing feature contextual relationships to enhance the correlation between features. By adding the OCAM attention module to the feature extraction layer of the YOLOv5 network, the detection performance of chip pads is effectively improved. In addition, a design guideline for the attention layer is proposed in the paper. The attention layer is adjusted by network scaling to avoid network characterization bottlenecks, balance network parameters, and network detection performance, and reduce the hardware device requirements for the improved YOLOv5 network in practical scenarios. Extensive experiments on chip pad datasets, VOC datasets, and COCO datasets show that the approach in this paper is more general and superior to several state-of-the-art methods.

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