Application of Dual-Energy X-Ray Image Detection of Dangerous Goods Based on YOLOv7

X-ray security equipment is currently a more commonly used dangerous goods detection tool, due to the increasing security work tasks, the use of target detection technology to assist security personnel to carry out work has become an inevitable trend. With the development of deep learning, object detection technology is becoming more and more mature, and object detection framework based on convolutional neural networks has been widely used in industrial, medical and military fields. In order to improve the efficiency of security staff, reduce the risk of dangerous goods missed detection. Based on the data collected in X-ray security equipment, this paper uses a method of inserting dangerous goods into an empty package to balance all kinds of dangerous goods data and expand the data set. The high-low energy images are combined using the high-low energy feature fusion method. Finally, the dangerous goods target detection technology based on the YOLOv7 model is used for model training. After the introduction of the above method, the detection accuracy is improved by 6% compared with the direct use of the original data set for detection, and the speed is 93FPS, which can meet the requirements of the online security system, greatly improve the work efficiency of security personnel, and eliminate the security risks caused by missed detection.

Research on the Pedestrian Re-Identification Method Based on Local Features and Gait Energy Images

The appearance of pedestrians can vary greatly from image to image,and different pedestrians may look similar in a given image.Such similarities and variabilities in the appearance and clothing of individuals make the task of pedestrian re-identification very challenging.Here,a pedestrian re-identification method based on the fusion of local features and gait energy image(GEI)features is proposed.In this method,the human body is divided into four regions according to joint points.The color and texture of each region of the human body are extracted as local features,and GEI features of the pedestrian gait are also obtained.These features are then fused with the local and GEI features of the person.Independent distance measure learning using the cross-view quadratic discriminant analysis(XQDA)method is used to obtain the similarity of the metric function of the image pairs,and the final similarity is acquired by weight matching.Evaluation of experimental results by cumulative matching characteristic(CMC)curves reveals that,after fusion of local and GEI features,the pedestrian re-identification effect is improved compared with existing methods and is notably better than the recognition rate of pedestrian re-identification with a single feature.

Ultrahard X-ray multifunctional application beamline at the SSRF

The ultrahard X-ray multifunctional application beamline(BL12SW)is a phase-II beamline project at the Shanghai Syn-chrotron Radiation Facility.The primary X-ray techniques used at the beamline are high-energy X-ray diffraction and imaging using white and monochromatic light.The main scientific objectives of ultrahard X-ray beamlines are focused on two research areas.One is the study of the structural properties of Earth’s interior and new materials under extreme high-temperature and high-pressure conditions,and the other is the characterization of materials and processes in near-real service environments.The beamline utilizes a superconducting wiggler as the light source,with two diamond windows and SiC discs to filter out low-energy light(primarily below 30 keV)and a Cu filter assembly to control the thermal load entering the subsequent optical components.The beamline is equipped with dual monochromators.The first was a meridional bending Laue monochromator cooled by liquid nitrogen,achieving a full-energy coverage of 30-162 keV.The second was a sagittal bending Laue monochromator installed in an external building,providing a focused beam in the horizontal direction with an energy range of 60-120 keV.There were four experimental hutches:two large-volume press experimental hutches(LVP1 and LVP2)and two engineering material experimental hutches(ENG1 and ENG2).Each hutch was equipped with various near-real service conditions to satisfy different requirements.For example,LVP1 and LVP2 were equipped with a 200-ton DDIA press and a 2000-ton dual-mode(DDIA and Kawai)press,respectively.ENG1 and ENG2 provide in situ tensile,creep,and fatigue tests as well as high-temperature conditions.Since June 2023,the BL12SW has been in trial operation.It is expected to officially open to users by early 2024.

Simulation study on reconstruction model of three-dimensional temperature distribution within visible range in furnace

This paper presents a reconstruction model of three-dimensional temperature distribution in furnace based on radiative energy images captured by charge-coupled device （CCD） cameras within the visible wavelength range. Numerical simulation case was used in this study and a zigzag eccentric temperature distribution was assumed to verify the model. Least square QR-factorization （LSQR） method was introduced to deal with reconstruction equation. It is found that the reconstructed temperature distributions in low-temperature areas had some fluctuations and high-temperature areas were reconstructed well. The whole reconstruction relative error was mainly due to errors in low-temperature areas and the relative error for highest-temperature reconstruction was quite small.

Relationship between Serum Uric Acid Levels and Kidney Arteriole Resistance in Patients with Type 2 Diabetes Associated with Hypertension

Objectives To investigate the relationship between serum uric acid levels and arteriole resistance detected with the color Doppler energy imaging (CDEI) and to explore the risks of impaired regulation of vascular tone by uric acid in kidneys of hypertension patients. Methods In 12 healthy control cases, 28 non-diabetic hypertension (nNIDDM +H) cases and 25 type 2 diabetic hypertension (NIDDM+H) cases, uric acid (UA) levels were measured with uricase-peroxidase method. Arteriole resistance index (RI) and pulsate index (PI) in separate sections of renal artery included MAR, SRA, IRA were detected using CDEI with 2.1~4.2 Hz Doppler transducer in kidneys. Results In comparison, UA was significantly higher in non-diabetic hyper- tension group and diabetic hypertension group than in control group (P<0.01, separately). UA levels was also significantly higher in NIDDM+H group than in nNIDDM+H, P < 0.029. RI in separate sections of renal artery was significantly higher in nNIDDM+H, or NIDDM+H group than in control group ( all P < 0.01) , and it was significantly higher in NIDDM +H than nNIDDM+H groups (P < 0.05 and P < 0.01). In nNIDDM +H and NIDDM +H groups UA levels and IRA-RI could be elevated significantly following with the impaired heart function being aggravated (χ2 = 13.028, P=0.005, χ2=13.29, P=0.004); the dosage of HCT being increased (χ2 =14.216, P=0.001, χ2 = 14.661, P=0.001); the levels of GHbA1 being excessed unnormally (P=0.000). The correlation between UA and IRA-RI in both hypertension groups were directly related, in nNIDDM+H group r=0.842, P=0.000, in NIDDM+H group, r=0.797, P=0.000. Conclusions Uric acid levels and IRA-RI in hyper-tension patients were directly related. Uric acid levels and IRA-RI could be partly dependent on the severity of heart dysfunction, diuretic dose, and serum glucose status of diabete patients in long-term. Uric acid and the xanthine oxidase metabolic pathway may contribute to impaired regulation of arteriole tone in hypertension patients.

A partition approach for robust gait recognition based on gait template fusion

Gait recognition has significant potential for remote human identification,hut it is easily influenced by identity-unrelated factors such as clothing,carrying conditions,and view angles.Many gait templates have been presented that can effectively represent gait features.Each gait template has its advantages and can represent different prominent information.In this paper,gait template fusion is proposed to improve the classical representative gait template(such as a gait energy image)which represents incomplete information that is sensitive to changes in contour.We also present a partition method to reflect the different gait habits of different body parts of each pedestrian.The fused template is cropped into three parts(head,trunk,and leg regions)depending on the human body,and the three parts are then sent into the convolutional neural network to learn merged features.We present an extensive empirical evaluation of the CASIA-B dataset and compare the proposed method with existing ones.The results show good accuracy and robustness of the proposed method for gait recognition.