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Incorporating green and low-carbon building targets into the rural planning management system requires scientific and quantitative methods for assessing carbon emissions from rural land use. At present, the research in this field mainly focuses on urban areas, but there are fewer theoretical and practical studies on the assessment of carbon emissions from rural land use. This paper proposes a new carbon emission assessment method based on land use modes, and the model can not only assess carbon emissions but also directly reflect the carbon emission intensity of different land use spaces in rural areas and guide the carbon emission control of construction land in village planning. In this paper, we take suburban rural areas in Hunan Province as an example and establish a land use carbon emission assessment model with 13 indicators in five dimensions: total carbon emission, carbon emission efficiency, carbon emission intensity per unit of land use, carbon emission structure of land use, and carbon emission intensity of buildings, based on the bottom-up field research data. We practised our method in Jinlong Town, Hunan Province, and gave examples of model applications. It was found that the carbon emission calculation method based on the carbon emission intensity of land use can be used to calculate the current status of carbon emissions in different villages in Jinlong Town. At the same time, the carbon emission assessment results can be used as a scientific basis for carbon emission control in detailed village planning in Jinlong Town. In general, the carbon emission assessment model can complete the assessment of land carbon emissions in rural areas and provide a low-carbon land use management tool for the government.

With the implementation of China’s rural revitalization strategy, the societies and economies of villages have been comprehensively developed, but the carbon emissions in rural areas have also been increasing year by year. Therefore, low-carbon control of the rural spatial form has become an important element of rural revitalization. This paper takes 18 suburban integrated villages in the plain terrain within Hunan Province, China, as the research object, quantifies the spatial morphology indicators of the overall rural community and the neighborhood building groups, and investigates the relationship between rural spatial form and carbon emissions in plain terrain, aiming to clarify the content of low-carbon control in rural spatial planning. The main conclusions are as follows. (1) The correlation between spatial form and carbon emissions at different levels of suburban integrated villages is “total volume form > neighborhood building groups combination form > overall layout form > neighborhood connection form”. When the scale of the villages is fixed, the spatial layout of the neighborhood building groups has a more direct influence on the carbon emissions of the residents. (2) The building density in the overall spatial form of the village has the greatest influence on the carbon emissions of the suburban integrated villages, and it is positively correlated. (3) There is a negative correlation between the form of neighborhood building groups and carbon emissions within a certain range. When the distance between the front and back of a building is 8–12 m, the carbon emissions of the building decrease with the increase in the degree of aggregation on the building, but when the distance between the front and back of a building reaches 12 m or more, the influence of the group layout form on the carbon emissions of the building is weakened. (4) Finally, based on the principle of “macro-control quantity and meso-control shape”, this paper proposes new control content and indicators for Hunan’s rural territorial space planning, which can provide a reference for low-carbon control in rural space form planning with suburban integration.

Machine vision can prevent additional stress on yarn caused by contact measurement, as well as the risk of hairiness and breakage. However, the speed of the machine vision system is limited by image processing, and the tension detection method based on the axially moving model does not take into account the disturbance on yarn caused by motor vibrations. Thus, an embedded system combining machine vision with a tension observer is proposed. The differential equation for the transverse dynamics of the string is established using Hamilton’s principle and then solved. A field-programmable gate array (FPGA) is used for image data acquisition, and the image processing algorithm is implemented using a multi-core digital signal processor (DSP). To obtain the yarn vibration frequency in the axially moving model, the brightest centreline grey value of the yarn image is put forward as a reference to determine the feature line. The calculated yarn tension value is then combined with the value obtained using the tension observer based on an adaptive weighted data fusion method in a programmable logic controller (PLC). The results show that the accuracy of the combined tension is improved compared with the original two non-contact methods of tension detection at a faster update rate. The system alleviates the problem of inadequate sampling rate using only machine vision methods and can be applied to future real-time control systems.

A new solution to the high-quality 3D reverse modeling problem of complex surfaces for fine workpieces is presented using a laser line-scanning sensor. Due to registration errors, measurement errors, deformations, etc., a fast and accurate method is important in machine vision measurement. This paper builds a convenient and economic multi-view stereo (MVS) measurement system based on a linear stage and a rotary stage to reconstruct the measured object surface completely and accurately. In the proposed technique, the linear stage is used to generate the trigger signal and synchronize the laser sensor scanning; the rotary stage is used to rotate the object and obtain multi-view point cloud data, and then the multi-view point cloud data are registered and integrated into a 3D model. The measurement results show a measurement accuracy of 0.075 mm for a 360° reconstruction in 34 s, and some evaluation experiments were carried out to demonstrate the validity and practicability of the proposed technique.

This paper proposes a deep convolutional neural network (CNN) -based technique for the detection of micro defects on metal screw surfaces. The defects we consider include surface damage, surface dirt, and stripped screws. Images of metal screws with different types of defects are collected using industrial cameras, which are then employed to train the designed deep CNN. To enable efficient detection, we first locate screw surfaces in the pictures captured by the cameras, so that the images of screw surfaces can be extracted, which are then input to the CNN-based defect detector. Experiment results show that the proposed technique can achieve a detection accuracy of 98%; the average detection time per picture is 1.2 s. Comparisons with traditional machine vision techniques, e.g., template matching-based techniques, demonstrate the superiority of the proposed deep CNN-based one.

Phase retrieval from single frame projection fringe patterns, a fundamental and challenging problem in fringe projection measurement, attracts wide attention and various new methods have emerged to address this challenge. Many phase retrieval methods are based on the decomposition of fringe patterns into a background part and a fringe part, and then the phase is obtained from the decomposed fringe part. However, the decomposition results are subject to the selection of model parameters, which is usually performed manually by trial and error due to the lack of decomposition assessment rules under a no ground truth data situation. In this paper, we propose a cross-correlation index to assess the decomposition and phase retrieval results without the need of ground truth data. The feasibility of the proposed metric is verified by simulated and real fringe patterns with the well-known Fourier transform method and recently proposed Shearlet transform method. This work contributes to the automatic phase retrieval and three-dimensional (3D) measurement with less human intervention, and can be potentially employed in other fields such as phase retrieval in digital holography.

The non-contact three-dimensional measurement and reconstruction techniques have played a significant role in the packaging and transportation of precious cultural relics. This paper develops a structured light based three-dimensional measurement system, with a low-cost for cultural relics packaging. The structured light based system performs rapid measurements and generates 3D point cloud data, which is then denoised, registered and merged to achieve accurate 3D reconstruction for cultural relics. The multi-frequency heterodyne method and the method in this paper are compared. It is shown that the relative accuracy of the proposed low-cost system can reach a level of 1/1000. The high efficiency of the system is demonstrated through experimental results.

In an attempt to explore a new class of epidermal growth factor receptor (EGFR) inhibitors, novel 4-stilbenylamino quinazoline derivatives were synthesized through a Dimorth rearrangement reaction and characterized via IR, 1H-NMR, 13C-NMR, and HRMS. Methoxyl, methyl, halogen, and trifluoromethyl groups on stilbeneamino were detected. These synthesized compounds were evaluated for antitumor activity in vitro against eight human tumor cell lines with an MTS assay. Most synthesized compounds exhibited more potent activity (IC50 = ~2.0 μM) than gefitinib (IC50 > 10.0 μM) against the A431, A549, and BGC-823 cell lines. Docking methodology of compound 6c and 6i binding into the ATP site of EGFR was carried out. The results showed that fluorine and trifluoromethyl played an important role in efficient cell activity.