The simulation of the gas flow field and electrostatic field in the photoionization detector by COMSOL was conducted based on principle investigation in the present study. Under the guidance of simulation results, structural optimization was carried out to significantly reduce the dead volume of the ionization chamber, and finally, the relationship between offset voltage and collection efficiency was obtained which led to a remarkable increase in the collection efficiency of charged ions in the photoionization detector. Then an ionization chamber with low interference and fast response was developed. Then experiment was performed with toluene as a VOCs gas under the condition of optimal gas flow rate of 50 ml, UV lamp ionization energy of 10.86 eV. The results showed that the ion collection efficiency reached 91% at a bias voltage of 150 V. Moreover, a preferred linearity of 99.99% was obtained, and a ppb level of LOD can be achieved. The determination results well-fitted the relationship between offset voltage and the response value obtained in the simulation.

In this paper, we propose an image enhancement algorithm combining non-subsampled shearlet transform and gradient-domain guided filtering to address the problems of low resolution, noise amplification, missing details, and weak edge gradient retention in the X-ray image enhancement process. First, we decompose histogram equalization and nonsubsampled shearlet transform to the original image. We get a low-frequency sub-band and several high-frequency sub-bands. Adaptive gamma correction with weighting distribution is used for the low-frequency sub-band to highlight image contour information and improve the overall contrast of the image. The gradient-domain guided filtering is conducted for the high-frequency sub-bands to suppress image noise and highlight detail and edge information. Finally, we reconstruct all the effectively processed sub-bands by the inverse non-subsampled shearlet transform and obtain the final enhanced image. The experimental results show that the proposed algorithm has good results in X-ray image enhancement, and its objective index also has evident advantages over some classical algorithms.

Thermal battery is an ideal power supply for military applications such as artillery and ship equipment. Due to the sheet-type process of the thermal battery, various installation error defects occur in the assembly of thermal battery. Aiming at the problems of low efficiency and low defect-recognition rate of thermal battery detection, a thermal battery defect detection model is proposed based on residual network. First, the squeeze-and-excitation networks (SENet) structure based on the attention mechanism is introduced into residual block of the residual neural network, the connection between the feature extraction channels is established, and the improved deep residual network I-ResNet50 is obtained; Second, in order to prevent overfitting, the defect images processed in the production line and the laboratory are data-enhanced and labeled. Transfer learning strategy is introduced into the recognition model I-ResNet50, and then the training set data samples are input into the recognition model I-ResNet50 for training, and the activation function LReLu and Dropout skills are introduced to improve the classification ability of the I-ResNet50 model; Finally, the recognition model I-ResNet50 is applied to the test set and validation set, and each defect of the thermal battery are output. Comparison experiments are tested under different migration strategies and different optimizers and learning rates, and comparison experiments with the five classic network structures of ResNet50, YOLOV3, MobileNetV2, VGG16, and YOLOV4 are also tested. The test data show that the recognition accuracy rates of qualified images and the three types of defective images (Qualified Assembly, Missing Current Plate, Wrong Number of Stacks, and Reverse Stack) can reach 99.64%, 98.17%, 99.11%, and 95.40%, respectively, the overall recognition accuracy rate can reach 98.10%. The test results illustrate the model can detect thermal battery defects more accurately and quickly, and has good defect diagnosis ability, which is nearly 5% higher than the traditional method, and a new solution for defect detection in practical industrial scenarios of thermal battery is provided.

Odor pollution did not only disturb the human normal life but also aroused the attention of environmental researchers and environmental protection departments. Therefore, the research on odor detecting method and instrument is important to theory and application. On this basis, the self-developed microfluidic chip capillary column is used in our odor detecting system. In this paper, lead the chip column into the chromatography separation system, with its small size, high efficiency, easy integration, and other characteristics to replace the original traditional column. The chip column was used in many gas experiments for several typical VOCs. At different carrier gas flow rates, the baseline value, toluene response, and toluene and methyl sulfide mixed gas separation were compared to verify the experiment to determine the optimal carrier gas flow rate in accordance with its response and separation degree. Under the premise of ensuring column efficiency as high as possible, it is determined that the optimal carrier gas flow rate is 6 ml/min. This paper shows the most proper carrier gas flow rate of our odor detecting system with the self-developed microfluidic chip capillary column.

The structure of a photoionization detector was optioned and researched. In order to solve the problem of the photoionization detector’ lamp surface residue interference, a new structure of the self-cleaning double-UV detector was adopted. At the same time, the air flow field of the detector was simulated by the finite element method. Through analyzing the results of the simulation experiment, further optimization of the gas channel for the microdetector was carried out, and the ionization chamber with axial flow structure was finally chosen. The new nanomaterial, graphene oxide was used to modify the surface of the collector plate of detector to improve the gas sensitivity and sensitivity of the photoionization detector. Through the experimental analysis, the performance indexes of detector were described in detail. The minimum detection limit of the detector is . The linearity response of the detector was analyzed, and the linear correlation coefficient reaches 0.993. The experimental results show that the double-UV detector can improve the overall gas sensing characteristics and provide an ideal detection unit for volatile organic compound (VOC) gas detection.

Aiming at the lack of large public single molten salt battery data sets, to reduce the labour-consuming, to improve the insufficient learning ability of traditional diagnostic methods in the production of single molten salt battery, an image recognition model for molten salt battery defects based on transfer learning is proposed. First, some pre-processing operations and image enhancement on the single molten salt battery image are performed. Second, the backbone of recognition model is built based on VGG16 network, and the selective kernel (SK) convolution module is adopted after the bottleneck layer, convolution kernel with an appropriate size can be selected adaptively through input feature map; Third, the FC is taken the place of a GAP layer, a dropout layer, and other fine-tuning operations are added, a simplified model called V-VGGNet is got; Finally, the weight parameters obtained from the pre-training on the ImageNet data set are transferred to the single molten salt battery image recognition model V-VGGNet. For different network structures and different training strategies, comparative experiments of performance tests are conducted. The test data manifest that the accuracy rates of V-VGGNet network for three categories of defective images (Missing Negative Electrode, Broken Tab, and Missing Current Collector) and Assembly Normal images can reach 95.14%, 98.79%, 98.21%, and 99.41%, the average accuracy can achieve 97.91%, good performance improvement of the single molten salt battery is improved, it is about 3% higher compared to other well-knows networks, which verified the feasibility of V-VGGNet model and the effectiveness of the improvement.