With the significant increase in demand for artificial intelligence, environmental map reconstruction has become a research hotspot for obstacle avoidance navigation, unmanned operations, and virtual reality. The quality of the map plays a vital role in positioning, path planning, and obstacle avoidance. This review starts with the development of SLAM (Simultaneous Localization and Mapping) and proceeds to a review of V-SLAM (Visual-SLAM) from its proposal to the present, with a summary of its historical milestones. In this context, the five parts of the classic V-SLAM framework—visual sensor, visual odometer, backend optimization, loop detection, and mapping—are explained separately. Meanwhile, the details of the latest methods are shown; VI-SLAM (Visual inertial SLAM) is reviewed and extended. The four critical techniques of V-SLAM and its technical difficulties are summarized as feature detection and matching, selection of keyframes, uncertainty technology, and expression of maps. Finally, the development direction and needs of the V-SLAM field are proposed.

Air is usually compressed adiabatically in the compressor. As the operating speed of compressors can be several thousand rpm, heat generated during compression cannot be sufficiently transmitted to the environment in such a short time. It is for this reason that compressor efficiency is limited. Isothermal compression could be an alternative choice applied on industrial compressor and compressed air energy storage (CAES). This paper proposed a new kind of piston to perform isothermal compression. Surface area of such isothermal piston structure is larger. A certain amount of fluid at the chamber bottom absorbs the heat from the isothermal piston. Heat transfer between piston and fluid during compression is investigated. Air pressure is measured to validate the effectiveness of this proposed piston structure in heat transfer. Compression work of the proposed isothermal piston and conventional one is compared. One issue of this comparison is that air-liquid dissolution can affect the pressure and compression work. The influence of dissolution is quantified with Henry’s Law. Quantitative analysis is performed to determine that heat transfer is the dominant factor affecting the pressure and compression work. Some simple experiments are described in this paper, which shed light on that heat transfer could be significantly improved adopting this proposed isothermal piston.

Because of its cleanness, safety, explosion proof, and other characteristics, pneumatic technologies have been applied in numerous industrial automation fields. As a key controlling element of a pneumatic system, electric-pneumatic pressure proportional valves have attracted the attention of many scholars in recent years. In this paper, in order to illustrate the research status and the development trend of electric-pneumatic pressure proportional valves, firstly, several related technologies will be introduced, for example, simulation methods and experimental modes. In addition, controlling methods, structural styles, and feedback forms are also compared in several types of pressure proportional valves. Moreover, the controlling strategy, as a significant relevant factor affecting the efficiency of valves, will be discussed in this paper. At the end, the conclusion and worksof electric-pneumatic pressure proportional valves in the future will bediscussed to achieve the electrical integration.

Reducing carbon emissions is an urgent problem around the world while facing the energy and environmental crises. Whatever progress has been made in renewable energy research, efforts made to energy-saving technology is always necessary. The energy consumption from fluid power systems of industrial processes is considerable, especially for pneumatic systems. A novel isothermal compression method was proposed to lower the energy consumption of compressors. A porous medium was introduced to compose an isothermal piston. The porous medium was located beneath a conventional piston, and gradually immerged into the liquid during compression. The compression heat was absorbed by the porous medium, and finally conducted with the liquid at the chamber bottom. The heat transfer can be significantly enhanced due to the large surface area of the porous medium. As the liquid has a large heat capacity, the liquid temperature can maintain constant through circulation outside. This create near-isothermal compression, which minimizes energy loss in the form of heat, which cannot be recovered. There will be mass loss of the air due to dissolution and leakage. Therefore, the dissolution and leakage amount of gas are compensated for in this method. Gas is dissolved into liquid with the pressure increasing, which leads to mass loss of the gas. With a pressure ratio of 4:1 and a rotational speed of 100 rpm, the isothermal piston decreased the energy consumption by 45% over the conventional reciprocation piston. This gain was accomplished by increasing the heat transfer during the gas compression by increasing the surface area to volume ratio in the compression chamber. Frictional forces between the porous medium and liquid was presented. Work to overcome the frictional forces is negligible (0.21% of the total compression work) under the current operating condition.

Surface defect detection of strip steel is an important guarantee for improving the production quality of strip steel. However, due to the diverse types, scales, and texture structures of surface defects on strip steel, as well as the irregular distribution of defects, it is difficult to achieve rapid and accurate detection of strip steel surface defects with existing methods. This article proposes a real-time and high-precision surface defect detection algorithm for strip steel based on YOLOv7. Firstly, Partial Conv is used to replace the conventional convolution blocks of the backbone network to reduce the size of the network model and improve the speed of detection; Secondly, The CA attention mechanism module is added to the ELAN module to enhance the ability of the network to extract detect features and improve the effectiveness of detect detection in complex environments; Finally, The SPD convolution module is introduced at the output end to improve the detection performance of small targets with surface defects on steel. The experimental results on the NEU-DET dataset indicate that the mean average accuracy (mAP@IoU = 0.5) is 80.4%, which is 4.0% higher than the baseline network. The number of parameters is reduced by 8.9%, and the computational load is reduced by 21.9% (GFLOPs). The detection speed reaches 90.9 FPS, which can well meet the requirements of real-time detection.