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The accurate measurement of cooking vessel temperatures in induction hobs is crucial for ensuring optimal cooking performance and safety. To achieve this, improvements in existing measurement methods such as thermocouples, thermistors, and infrared (IR) temperature sensors are being explored. However, traditional IR sensors are sensitive to interference from the heated glass ceramic, severely affecting accuracy. This challenge is addressed by introducing a new sensor system with an optical filter designed to match the glass ceramic’s optical characteristics. The theoretical model presented here proposes the separation of the total radiation reaching the IR sensor into components emitted by the cooking vessel and the glass ceramic. However, the radiation component originating from the glass ceramic mentioned here is significantly higher than the radiation component of the cooking vessel, which creates difficulties in measuring the temperature of the cooking vessel. Simulations and real cooking experiments validate the model and demonstrate that the optic filter significantly increases the contribution of pot radiation to the sensor measurement. This causes a more accurate reflection of the actual cooking vessel temperature, leading to improved temperature control and enhanced cooking experiences in domestic induction hob appliances. This research contributes to the field by innovatively addressing challenges in real-time temperature control for induction cooking appliances. The elimination of pot dependence and improved accuracy have significant implications for cooking efficiency, safety and food quality.

Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction models. With the objective of assessing the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based LST measurements, intensive field experiments were conducted at NOAA’s Air Resources Laboratory (ARL)’s Atmospheric Turbulence and Diffusion Division (ATDD) in Oak Ridge, Tennessee, USA, from October 2015 to January 2016. The results of the comparison of LSTs retrieved by three narrow angle broadband infrared temperature sensors (IRT), hemispherical longwave radiation (LWR) measurements by pyrgeometers, forward looking infrared camera with direct LSTs by multiple thermocouples (TC), and near surface air temperature (AT) are presented here. The brightness temperature (BT) measurements by the IRTs agreed well with a bias of <0.23 °C, and root mean square error (RMSE) of <0.36 °C. The daytime LST(TC) and LST(IRT) showed better agreement (bias = 0.26 °C and RMSE = 0.67 °C) than with LST(LWR) (bias > 1.1 and RMSE > 1.46 °C). In contrast, the difference between nighttime LSTs by IRTs, TCs, and LWR were <0.47 °C, whereas nighttime AT explained >81% of the variance in LST(IRT) with a bias of 2.64 °C and RMSE of 3.6 °C. To evaluate the annual and seasonal differences in LST(IRT), LST(LWR) and AT, the analysis was extended to four grassland sites in the USA. For the annual dataset of LST, the bias between LST (IRT) and LST (LWR) was <0.7 °C, except at the semiarid grassland (1.5 °C), whereas the absolute bias between AT and LST at the four sites were <2 °C. The monthly difference between LST (IRT) and LST (LWR) (or AT) reached up to 2 °C (5 °C), whereas half-hourly differences between LSTs and AT were several degrees in magnitude depending on the site characteristics, time of the day and the season.

The paper presents the study and implementation of a thermal scanning system that collects and transmits in real time the thermal data of scanned objects for their constant monitoring using an infrared temperature sensor and an Arduino Nano board. The application is made with the Arduino IDE, with the help of which the control of the Arduino board, sensors, screen, and LEDs connected to the platform is conducted. The sensors create the overall image of the thermal scan and send it to the Liquid-Crystal Display Thin-Film Transistor (LCD TFT) screen, and if an anomaly occurs, the red LED lights up.

Much research has been invested in infrared temperature (IRT)-based methods for cotton ( Gossypium hirsutism L.) water stress detection using in-field sensors, but adoption of these is low, perhaps due to logistical challenges. Alternatively, the Water Deficit Index (WDI) was developed for crop water stress assessment using remote sensors not embedded in the canopy. The objective of this research was to evaluate the performance of a sensor package—including modern IRT and normalized difference vegetation index (NDVI) sensors facing downward at 45˚, and a mini weather station—attached unintrusively to a center pivot irrigation system for detecting cotton water stress using WDI. Sensor packages were evaluated in a two-year field study that included four irrigation treatments (0, 30, 60, and 90% ET replacement) and in two production cotton fields. Overall, the tested system was effective at distinguishing crop water stress among irrigation rates. Comparison of the results to a ground-based station and simulations indicated that WDI overestimated water stress at the highest irrigation rate, but performed well otherwise. Accuracy of the system could be improved by measuring canopy coverage ( Fc ) from the same vantage point as the IRT and NDVI sensors (from the pivot, downward at a 45˚ angle).

In the injection molding process, weld line regions occur when a molten polymer flow front is first separated and then rejoined. The position, length, and angle of weld lines are dependent on the gate location, injection speed, injection pressure, mold temperature, and, especially, the direction and degree of the polymer melt velocity in the mold-filling process. However, the wall surface velocity of the thermoset melt in the mold-filling process is not zero, which is not found for thermoplastic injection molding. The main reason leading to this difference is the slip phenomenon in the filling phase between the thermoset melt and the wall surface, which is directly affected by the filler content. In this study, commercial thermoset phenolic injection molding compounds with different amounts of filler were employed to investigate not only the mechanism of weld line formation and development behind an obstacle in the injection molding process but also the flow disturbance of the thermoset melt in the spiral flow part. In addition, the effect of the wall slip phenomenon on the flow disturbance characterization and the mechanism of weld lines of selected thermoset materials was carefully considered in this research. Furthermore, the generated material data sheet with the optimal developed reactive viscosity and curing kinetics model was imported into a commercial injection molding tool to predict the weld line formation as well as the mold-filling behavior of selected thermoset injection molding compounds, such as the flow length, cavity pressure profile, temperature distribution, and viscosity variation. The results obtained in this paper provide important academic knowledge about the flow disturbance behavior as well as its influence on the mechanism of weld line formation in the process of thermoset injection molding. Furthermore, the simulated results were compared with the experimental results, which helps provide an overview of the ability of computer simulation in the field of the reactive injection molding process.

Accurate remaining useful life (RUL) prediction of tantalum capacitors is essential for enhancing the reliability and maintainability of power electronic systems. However, online RUL prediction remains a challenging task due to the difficulty of accessing internal degradation states and the non-stationarity of operating conditions. This paper presents a novel CNN-LSTM-Attention-based deep learning framework for accurate online RUL prediction of tantalum capacitors, leveraging infrared surface temperature measurements and ambient thermal compensation. The proposed framework initiates with the collection of degradation temperature data under controlled accelerated aging experiments, where true degradation indicators are extracted by eliminating ambient temperature interference through dual-sensor compensation. The resulting preprocessed data are used to train a hybrid deep neural network model that integrates convolutional layers for local feature extraction, long short-term memory (LSTM) units for sequential dependency modeling, and a soft attention mechanism to selectively focus on the critical degradation patterns. A channel attention module is further embedded to adaptively optimize the importance of different feature channels. Experimental validation using three groups of aging data demonstrates the effectiveness and superiority of the proposed method over conventional LSTM and CNN-LSTM baselines. The CNN-LSTM-Attention model achieves a substantial improvement in prediction accuracy, with mean absolute percentage error (MAPE) reductions of up to 60.97%, root mean squared error (RMSE) reductions of up to 65.63%, and coefficient of determination (R2) increases of up to 68.67%. The results confirm the ability to deliver precise and robust online RUL predictions for tantalum capacitors under complex operational conditions.

In order to realize the human body temperature fast and non-contact measurement, an infrared thermometer is designed. The infrared human body temperature sensor is mainly used to convert the human body’s infrared into voltage signal, an operational amplifier to amplify the signal, filter circuit to filter the signal, the analog signal into digital signal by the A/D conversion circuit, data processing by the MCU, LCD display and voice reporting body temperature and time, so the human body non-contact measurement is realized. The experimental results show that: the device can realize the temperature and time of acquisition, the measurement error is not more than 0.5 °Ç voice broadcast and liquid crystal display the temperature and time, overrun alarm and other functions

This chapter contains sections titled: Infrared Radiation Historical Development Advantages of Infrared Measuring Technology Comparison of Thermal and Photonic Infrared Sensors Temperature and Spatial Resolution of Infrared Sensors Single‐Element Sensors Versus Array Sensors References

This chapter contains sections titled: Operating Principles Thermal Models Network Models for Thermal Sensors Thermoelectric Radiation Sensors Pyroelectric Sensors Microbolometers Other Thermal Infrared Sensors Comparison of Thermal Sensors References

Electronic sensor foils only 2 μm thick are extremely light, 27-fold lighter than office paper, durable and flexible and conform to curvilinear surfaces for many innovative applications. Feather-light unbreakable plastic electronics Flexible electronics is emerging as a mainstream technology for smart, mobile, wearable devices and also for biomedical applications. Kaltenbrunner et al . break new ground by fabricating light-as-a-feather virtually imperceptible and unbreakable electronic foils that can conform to any desired shape. The foils consist of organic transistors with an ultra-dense oxide gate dielectric, itself only a few nanometres thick, deposited on ultra-lightweight plastic films, for an overall thickness of just two micrometres. They can withstand repeated severe bending and stretching, can crumple like paper, and work at elevated temperatures and in wet environments. The authors demonstrate that the flexible electronic foil can act as a tactile sensor on a model of the upper human jaw, illustrating the potential for this technology in health care and monitoring. Electronic devices have advanced from their heavy, bulky origins to become smart, mobile appliances. Nevertheless, they remain rigid, which precludes their intimate integration into everyday life. Flexible, textile and stretchable electronics are emerging research areas and may yield mainstream technologies 1 , 2 , 3 . Rollable and unbreakable backplanes with amorphous silicon field-effect transistors on steel substrates only 3 μm thick have been demonstrated 4 . On polymer substrates, bending radii of 0.1 mm have been achieved in flexible electronic devices 5 , 6 , 7 . Concurrently, the need for compliant electronics that can not only be flexed but also conform to three-dimensional shapes has emerged 3 . Approaches include the transfer of ultrathin polyimide layers encapsulating silicon CMOS circuits onto pre-stretched elastomers 8 , the use of conductive elastomers integrated with organic field-effect transistors (OFETs) on polyimide islands 9 , and fabrication of OFETs and gold interconnects on elastic substrates 10 to realize pressure, temperature and optical sensors 11 , 12 , 13 , 14 . Here we present a platform that makes electronics both virtually unbreakable 4 and imperceptible. Fabricated directly on ultrathin (1 μm) polymer foils, our electronic circuits are light (3 g m −2 ) and ultraflexible and conform to their ambient, dynamic environment. Organic transistors with an ultra-dense oxide gate dielectric a few nanometres thick formed at room temperature enable sophisticated large-area electronic foils with unprecedented mechanical and environmental stability: they withstand repeated bending to radii of 5 μm and less, can be crumpled like paper, accommodate stretching up to 230% on prestrained elastomers, and can be operated at high temperatures and in aqueous environments. Because manufacturing costs of organic electronics are potentially low, imperceptible electronic foils may be as common in the future as plastic wrap is today. Applications include matrix-addressed tactile sensor foils for health care and monitoring, thin-film heaters, temperature and infrared sensors, displays 15 , and organic solar cells 16 .