Thermal Imaging Technology Research Articles

Experimental study on the effect of Mongolian medicine warming acupuncture on proBDNF-tPA-BDNF balance in depression rats based on thermal imaging monitoring

Depression is a kind of mental illness affecting the whole world. Its pathological mechanism is complex, involving neurobiological and psychosocial factors.The depression model rats were randomly divided into control group, warm acupuncture group and drug treatment group. In order to evaluate the local thermal effect of warm acupuncture and moxibustion, real-time temperature changes of rat forebrain were monitored by thermal imaging technology. After the experiment, immunohistochemistry and Western blot were used to detect the expression levels of tPA and BDNF in the forebrain of each group of rats, and the relationship between tPA and BDNF was analyzed. The results of thermal imaging monitoring showed that local forebrain temperature in warm acupuncture group was significantly increased compared with control group (p < 0.05). The ratio of tPA to BDNF was significantly improved in the warm acupuncture and moxibustion group, suggesting that warm acupuncture and moxibustion can effectively regulate the balance of TPA-BDNF. The experimental results based on thermal imaging monitoring suggest that warm acupuncture can improve the balance of TPA-BDNF in the forebrain of depressed rats by promoting the expression of BDNF and tPA in the forebrain, which may provide a new method and mechanism basis for the treatment of depression.

Development of a high-strength lightweight geopolymer concrete for structural and thermal insulation applications

Investigating the thermal insulation properties of lightweight geopolymer concrete is essential. This paper aims to develop a lightweight aggregate geopolymer concrete (LWAGC) with good density, compressive mechanical and thermal insulation properties. The dry density of LWAGC was adjusted by incorporating expanded perlite (EP). The variation in physical and mechanical properties of LWAGC with different EP content was discussed, including dry density, P-wave velocity, ultimate compression strength and elastic modulus. Based on infrared thermal imaging technology, a rapid measurement method was proposed to simulate the indoor temperature change of buildings at high ambient temperatures. The thermal insulation performance of the LWAGC with different EP contents was further evaluated. The findings show that as the EP content in LWAGC increases, there is a corresponding decrease in P-wave velocity, dry density, ultimate compressive stress, and elastic modulus. The lowest dry density of LWAGC reaches 1209kg/m3 while the ultimate compressive stress is still larger than 25.0MPa, which can used as LC25 building materials in load-bearing structures. The results also show that the addition of EP can improve the thermal insulation properties of LWAGC. The LWAGC with 50% EP content has the highest reduction rate of temperature and can maintain the indoor temperature lower than 35 °C under high ambient temperature, which have potential application prospects in the load-bearing structures and thermal insulation of tall buildings.

In-site experimental study on the effects of infrared thermal imaging technology on levee leakage detection

Excessive seepage significantly affects the stability of levees during flood seasons. Recently, infrared thermal imaging technology has been proposed to detect the seepage leakage of levees for its simplicity, high efficiency and non-contact. However, previous studies mainly focused on theoretical and laboratory tests to verify its effect, which involve many assumptions and simplification. This paper aims to study the practical application effects of infrared thermal imaging technology on levee leakage detection. A typical embankment in Xiangyin County, Hunan Province is selected as the studied area, where a specific point containing water is considered as the potential leakage point. Then, DJI M300RTK UAV equipped with Zenith H20T gimbal is adopted to collect the visible and infrared images of the embankment’s leaking point. The visible and infrared image data collected at different times and at different measurement heights are analyzed to investigate the detection accuracy of infrared thermal imaging technology. Results show that the infrared thermal imaging technology can accurately identify the potential seepage leakage point of levees. Compared with the visible light recognition method, the infrared thermal imaging technology can identify the location and scope of leakage hazards more clearly and accurately at night. During the daytime, infrared detection is less effective than visible light method. Moreover, the measurement altitude between the drone and the target leakage points significantly affects the detection accuracy of infrared thermal imaging technology.

Bruise detection of apples based on passive thermal imaging technology

Bruise detection of apples based on passive thermal imaging technology

Study on infrared thermal imaging for surface deterioration investigation and evaluation of stone cultural relics based on temperature variation

This study leverages infrared thermal imaging technology to advance the field of stone cultural relics preservation by introducing a novel evaluation index—temperature variation (Tv)—that quantifies the degree of surface deterioration. Unlike previous studies that have focused primarily on detecting a single deterioration type, this study pioneered a comprehensive analysis of the interactions of multiple deterioration in stone cultural relic. A deeper understanding of the weathering mechanisms of stone cultural relics has been gained by examining the Tv on the surface of stone cultural relics during heating and cooling, and successfully relating these changes to the degree of weathering of the cultural relic. This study developed a linear fitting method for systematically assessing the degree of weathering of stone cultural relic, which is a significant improvement over traditional assessment techniques. Our approach not only enhances the accuracy and applicability of Tv as an evaluation index but also integrates environmental temperature considerations, ensuring a more reliable assessment under varying conditions. The study's findings validate the effectiveness of infrared thermal imaging in identifying deterioration types and evaluating their severity, setting a new standard for the protection and restoration of stone cultural relics.

Research on the discrimination and classification of multi-modal fault bearing based on multi-attribute decision-making mechanism

In order to solve the problem of low fault diagnosis rate of friction torque in upright placement, this paper proposes a fault diagnosis method based on multi-attribute decision-making mechanism to accurately evaluate the fault damage degree of bearing friction torque test bench. We detect defects by utilizing non-destructive, non-contact infrared thermal imaging technology to perform data acquisition and testing in six situations, including non-destructive, damaged inner ring, damaged outer ring, marble-defect, lack of lubrication, and damaged inner and outer ring bearings. Then, the infrared data of different bearings are transformed in a standardized form, and the decision data is mapped to the Pythagorean fuzzy information space. The correlation between the decision data are considering, the PA operator and the BM operator are used for data processing to achieve the accurate distinction of different faulty bearings. Finally, qualitative and quantitative methods are used to analyze the decision results, and the effectiveness of the method is explained. The variation of the overall singular value of each object in the decision result of the proposed method is less than 0.002, while the data fluctuation of WA, WG, HM and DHM related decision methods is 0.005, and there is a singular value greater than 0.02. In addition, sensitivity analysis provides a feasible scheme for parameter optimization and optimal parameter determination.

Research on identification and detection of coal and gangue based on infrared thermal imaging technology and improved YOLOv8n

ABSTRACT Considering that traditional image recognition methods for coal and gangue are susceptible to environmental factors such as light and dust, this paper proposes an image recognition and detection method for the coal and gangue based on infrared thermal imaging technology and an improved YOLOv8n. First, we established a thermal infrared imaging platform for coal and gangue. We used the YOLOv8n model to analyze the differences between coal and gangue at various temperatures, determining the optimal temperature for effective recognition of the coal and gangue. Next, to facilitate the practical deployment of the detection model for coal and gangue, we incorporated the Ghost module into the original YOLOv8n model to reduce its complexity and storage requirements. Unfortunately, incorporating the Ghost module led to a slight decrease in the model’s detection performance. Next, we introduced the DAttention module into the model to enhance its feature extraction capabilities. At the same time, we replaced the CIoU loss function with DIoU, which further improved the model’s performance metrics. Finally, we compared the improved YOLOv8n model with Faster-RCNN model and SSD model to validate its feasibility and accuracy further and provide a new approach for the detection of coal and gangue.

Thermal radiation imaging technology and sensor assisted calculation of sports training technology simulation

With the development of sports training science and technology, the real-time monitoring of athletes’ physical condition and training effect is becoming more and more important. As a non-contact measurement method, thermal radiation imaging technology can capture the changes of athletes’ body surface temperature efficiently and in real time, and provide relevant information about physiological state and performance indicators. This paper discusses the application of thermal radiation imaging technology in sports training to help measure athletes’ physical state and training effect, so as to provide scientific basis for improving training efficiency and reducing sports injuries. In this paper, the high resolution thermal imaging equipment is used to systematically analyze the thermal radiation imaging of athletes, and the physiological index monitoring and data processing technology are combined to dynamically observe the temperature distribution and change characteristics of athletes under different training states. Through comparative analysis, the correlation between the data provided by thermal radiation imaging technology in the training process and the traditional physiological indicators was evaluated. The results show that thermal radiation imaging technology can effectively reflect the heat distribution and local body temperature changes of athletes during training. Compared to traditional physiological feedback data, the technology has significant advantages in quickly identifying excessive fatigue and potential injuries in athletes, especially during high-intensity training, and can provide early warning of associated risks. The application of sensor-assisted measurement system will further enhance the accuracy and effectiveness of sports scientific research and provide important support for athletes’ scientific training and health management.

Research on burnup depth measurement of spent fuel board based on thermal neutron imaging technology

The detection equipment for measuring the burnup depth in spent fuel rods is a vital component of the spent fuel management system. which can determine the burnup depth of spent fuel board and plays a crucial role in safeguarding the safety, economic efficiency, and structural integrity of the fuel assembly. This study introduces an innovative technical approach for assessing the burnup depth of spent fuel veneers, utilizing transmitted thermal neutron imaging technology. We have significantly enhanced the design of a thermal neutron moderator collimator, leading to remarkable improvements in the quality of the thermal neutron beam. Following moderation by the collimator, the ultimate thermal neutron injection rate at the designated sample location exceeds 103 n/cm2, with thermal neutrons comprising over 74% of the collimated neutron beam. This advanced measurement system enables us to obtain a detailed two-dimensional distribution map of thermal neutrons transmitted through spent fuel boards with varying burnup depths. By analyzing the grayscale intensity patterns in these maps, we can accurately evaluate the burnup degree within the simulated spent fuel plate. Furthermore, we establish a correlation between the transmitted thermal neutron count in the imaging field and the burnup depth of the spent fuel veneer. This allows for precise determination of burnup depth through the analysis of the two-dimensional distribution of transmission thermal neutron intensity. Our findings demonstrate the feasibility of a scheme for detecting the burnup depth in spent fuel boards based on transmission thermal neutron imaging technology, and obtained a linear relationship between the neutron transmission count and the burnup depth of the spent fuel plate, laying a solid theoretical foundation for future research and development of testing equipment to assess burnup in spent fuel veneers.

Detection of unmanned aerial vehicles using thermal imaging technology

The detection of UAVs can be carried out using their information features in various ranges: acoustic, radio, optical. Acoustic (ultrasonic) sensors, radar, radio frequency sensors, laser locators, electron-optical / IR sensors (direction finders) are used for their registration. This article will consider the possibilities of detecting UAVs using thermal imagers, pay attention to the characteristics of the resolution of thermal imaging systems, the signal-to-noise ratio, the possibilities of polarization processing of recorded thermal radiation, and the results of some foreign studies on the detection (recognition) of UAVs using thermal imaging cameras.

Research on the thermal energy environment and process parameters of milling process in ceramic art design

In ceramic art design, milling technology is the key processing technology, which directly affects the quality and artistic expression of ceramic products. However, the thermal energy environment generated in the milling process has a significant impact on the material characteristics and machining results. This study aims to explore the influence of the thermal energy environment on the ceramic art design during the milling process, optimize the process parameters, and improve the processing efficiency and product quality. Experiments with different milling speed, feed rate and cutting depth were designed to compare and analyze the change of thermal environment by combining experiment and simulation. The influence of thermal energy on ceramic machining quality was evaluated by monitoring the temperature distribution in the cutting process with thermal imaging technology and combining with the experimental data of material properties. The experimental results show that the temperature increase in milling process is closely related to the cutting speed and feed rate, and proper control of the thermal energy environment can significantly reduce the crack generation rate and improve the surface finish of ceramic materials. The optimized process parameters make the temperature control in the milling process within the ideal range, thus realizing the processing of high quality ceramic products. Through reasonable optimization of process parameters, the heat energy in the processing process can be effectively controlled, which helps to improve the overall quality and artistic value of ceramic works.

A review of potential fault detection methods for traditional collector stations and sensor fault diagnosis in wind farm doubly-fed generator converters

Collector stations sometimes face situations where existing relay protection systems may fail, making potential fault monitoring technology increasingly important. This technology plays a critical role in ensuring the stability of power supply and enhancing economic benefits. Simultaneously, in response to the nation's "dual carbon goals," the construction and operation of large-scale renewable energy power stations are booming. Therefore, potential fault monitoring technology for new power stations should not be overlooked. For traditional power stations, the application and development trends of infrared thermal imaging technology, vibration analysis and acoustic detection technology, chemical analysis, and oil quality detection technology will be compared and discussed. Regarding new power stations, methods for diagnosing sensor faults in doubly-fed generator converters in wind power will be analyzed and discussed.

An experimental investigation on frosting characteristics and performance of microchannel evaporators with varied aspect ratios

Microchannel evaporators are susceptible to frosting, which can significantly impair heat transfer performance. To provide a basis for microchannel evaporators' effective design and broad application, frosting experiments were conducted in this study. Leveraging infrared thermal imaging and digital image processing technology, the impact of different aspect ratios on refrigerant distribution was quantitatively measured, and the microchannel evaporators' frosting behavior and performance under frosting conditions were analyzed. The results showed that a higher aspect ratio for Evaporator C led to reduced uniformity in the refrigerant distribution, resulting in an increased frost height, a larger frosting coverage area, and greater frost accumulation. Consequently, as frost accumulated, Evaporator C exhibited the most discernible reductions in the heat transfer rate, evaporation pressure, and refrigerant pressure loss, which were 61.07 %, 31.7 %, and 52.83 %, respectively. Therefore, microchannel evaporators with small aspect ratios are recommended where practical field installation conditions permit. And the results are anticipated to provide useful guidance for selecting microchannel evaporators with the optimal aspect ratio, tailored to operate efficiently under frosting conditions.

Study on the delamination damage prediction of wind turbine blade spar cap based on morphology feature recognition

In order to study the degradation of local mechanical properties caused by delamination defects of wind turbine blades, a progressive damage prediction method of blade spar cap based on quantitative identification of delamination morphology features by infrared thermal image was proposed in this paper. Firstly, based on the thermal conduction theory of laminate plates, infrared thermal imaging technology was used to realize quantitatively identify delamination morphology features. Then, based on the cohesive zone model (CZM) and the classical laminate theory (CLT), a numerical analysis model of unidirectional laminate with delamination defects was established, and the accuracy of the numerical model was verified by comparison with the experimental results of axial tensile and compressive displacement loads. Finally, a 2 MW-45.3 blade spar cap with maximum chord cross-section was used as a prediction model, and the effect of different delamination sizes and depths on the failure mode, failure strength and damage evolution of the blade spar cap was successfully predicted by using the equivalent fatigue load. The research shows that the delamination depth has a more profound influence on the damage of spar cap compared with the delamination size. With the increase of delamination depth, the delamination failure modes undergo the evolution process of non-buckling, local buckling, delamination expansion and strength failure. Shallow delamination causes local buckling and strength failure of blade spar cap. When the delamination depth reaches half of the spar cap thickness, the delamination extends to intralaminar and interlaminar, the local failure changes to global failure.

Radio Frequency Vacuum Drying Study on the Drying Characteristics and Quality of Cistanche Slices and Analysis of Heating Uniformity.

To fully leverage the advantages of both hot air drying and radio frequency vacuum drying, a segmented combination drying technique was applied to post-harvest Cistanche. This new drying method involves using hot air drying in the initial stage to remove the majority of free water, followed by radio frequency vacuum drying in the later stage to remove the remaining small amount of free water and bound water. During the radio frequency vacuum drying (RFV) phase, the effects of temperature (45, 55, and 65 °C), vacuum pressure (0.020, 0.030, and 0.040 MPa), plate spacing (65, 75, and 85 mm), and slice thickness (4, 5, and 6 mm) on the drying characteristics, quality, and microstructure of Cistanche slices were investigated. Additionally, infrared thermal imaging technology was used to examine the surface temperature distribution of the material during the drying process. The results showed that compared to radio frequency vacuum drying alone, the hot air-radio frequency combined drying significantly shortened the drying time. Under conditions of lower vacuum pressure (0.020 MPa), plate spacing (65 mm), and higher temperature (65 °C), the drying time was reduced and the drying rate increased. Infrared thermal imaging revealed that in the early stages of hot air-radio frequency vacuum combined drying, the center temperature of Cistanche was higher than the edge temperature. As drying progressed, the internal moisture of the material diffused from the inside out, resulting in higher edge temperatures compared to the center and the formation of overheating zones. Compared to natural air drying, the hot air-radio frequency vacuum combined drying effectively preserved the content of active components such as polysaccharides (275.56 mg/g), total phenols (38.62 mg/g), total flavonoids (70.35 mg/g), phenylethanoid glycosides, and iridoids. Scanning electron microscopy observed that this combined drying method reduced surface collapse and cracking of the material. This study provides theoretical references for future drying processes of Cistanche.

A Review of Five Existing Hornet-Tracking Methods.

Hornet is a general term for insects of the genus Vespa (Hymenoptera: Vespidae). Hornets are predatory insects distributed worldwide. They often appear at apiaries in groups to prey on honey bees, and cause incalculable losses in the honey bee industry. In the face of hornet intrusion, tracking a homing hornet to find its nest is the most efficient way to discover and eliminate the hornets around an apiary. Here, five hornet-tracking methods (hornet tag tracking, triangulation, thermal imaging technology, harmonic radar, and radio telemetry) are reviewed. The advantages, disadvantages and feasibility of each method are discussed to improve the strategies for tracking hornets. Therefore, this review provides ideas for the development of hornet-tracking technology and for improving honey bee protection.

Pencil-drawn tunable electrical resistance and Joule heating demonstration using a smartphone-based thermal imaging camera

This paper delves into the educational potential of pencil circuits for teaching key electrical concepts. Using both practical experiments and theoretical examinations, this study explores the application of pencil circuits in demonstrating concepts related to series and parallel resistance, Ohm’s law, and Joule’s heating law. By leveraging the simplicity and accessibility of pencil-based setups, students can gain a practical understanding of these fundamental principles. Additionally, the integration of thermal imaging technology enhances the visualization of Joule’s heating law, further enriching the learning experience.

From sunburn detection to optimal cooling: A review of recent applications of thermal imaging to improve preharvest and postharvest handling of fruit and vegetables

Fruit and vegetables (FV) are susceptible to adverse physiological and biochemical changes due to temperature abuse during preharvest and postharvest handling. Rapid, reliable, and non-invasive technologies such as infrared (IR) cameras are new, cost-effective tools that can record temperature profiles of FV in real-time. In this article, the basic principles of thermal imaging (TI) technology and its recent applications in preharvest and postharvest handling of FV as a temperature monitoring tool are concisely highlighted. Detection of preharvest and postharvest quality of FV (wetness, immaturity, bruise, etc.) based on thermal images are also summarized. Additionally, different modeling and simulation methods, including statistical modeling, computer simulation, machine learning (MLR), and deep learning (DLR), are briefly introduced into the preharvest and postharvest handling of FV, and applications of TI technique as a temperature validation tool in these methods are also discussed. This review article summarized that non-contact TI technique exhibits great potential to monitor temperature profiles on the field and during the postharvest handling of FV in real-time. Quality attributes, such as sunburn, immaturity, and bruises in FV, can be detected by using thermal images. Therefore, yields can be increased in the field, and food losses reduced during the postharvest handling of FV using TI technology. Furthermore, TI technology is an effective validation tool for simulation studies of foods. Future research should focus on developing low-cost TI systems and integrating multiple imaging techniques to facilitate the decision-making process in the FV industry.

Graphene-based tunable broadband metamaterial absorber for terahertz waves

In order to overcome the limitations exhibited by traditional absorbers in electromagnetic wave absorption, such as the extremely narrow absorption bandwidth and uncontrollable absorption results, this paper proposes a novel tunable broadband metamaterial absorber based on graphene. Compared to other terahertz absorbers, the absorber designed in this study exhibits simpler structural design, compact form, thin thickness, efficient absorption performance, and a broader bandwidth. This absorber adopts a three-layer structure, including a patterned monolayer graphene metasurface, a dielectric layer, and a metal substrate. The structure of this patterned graphene consists of four triangular graphene resonators, achieving both high absorption efficiency and maintaining an exceptionally wide absorption bandwidth. The simulation results indicate that at a Fermi energy level Ef of 0.45 eV, the absorber achieves a broadband absorption rate of over 90 % in the 3.287 THz to 5.247 THz frequency range under normal terahertz wave incidence conditions. By analyzing different quantities of the triangular graphene structure on the top of the absorber, we found that the wide absorption bandwidth of the absorber is mainly attributed to the structural coupling of the top-layer graphene in the absorber. Furthermore, through the study of electric field amplitude and current distribution, we stumbled upon that when terahertz waves are incident on the absorber, due to the structural coupling between the graphene layers, electric resonance and magnetic resonance occur within the absorber, resulting in the absorption effect. Finally, through the study of geometric parameters, different polarization angles and oblique incidence angles of the incident waves, as well as different Fermi energy levels of graphene, we discovered that by adjusting the Fermi energy level of graphene, the bandwidth and absorption performance of the absorber can be flexibly tuned. In addition, this absorber exhibits wide-angle absorption characteristics and polarization-insensitive properties, providing potential application prospects in fields such as terahertz thermal imaging and stealth technology.

A graph learning approach for automatic subsurface defects segmentation in building façade using RGB and infrared images

Infrared thermal imaging technology provides a non-destructive and automated inspection solution for analyzing building facades. However, conventional methods that rely on detecting subsurface defects through abnormal heat loss often suffer from the limitation of misidentifying other materials present on the building facade. To address this challenge, this study proposes a graph learning approach for segmentation of subsurface defects by integrating both visual and thermal information. The proposed methodology encompasses several key steps. Firstly, the infrared image is partitioned into internally connected superpixels with similar characteristics using a linear iterative clustering algorithm. Subsequently, these superpixels are employed as nodes to construct a weighted undirected graph. The edges of the graph are defined based on the connectivity of superpixels in the image plane, while the weights are determined by the Wasserstein distance between each node. Furthermore, a multimodal graph reuse technique is introduced to construct a new graph based on an RGB image, thereby incorporating additional visual information. By defining the defect as tightly connected in the RGB graph but loosely connected in the infrared graph, the segmentation of defects involving multiple materials is formulated as a multi-graph N-cut problem. The feasibility and effectiveness of the proposed method are demonstrated through laboratory testing conducted on concrete specimens, as well as on-site monitoring carried out on a tiled building façade.