This study presents a novel radiation measurement system capable of simultaneously measuring solar and upward longwave radiation, with the goal of achieving measurement accuracy within ±5% under the tested experimental conditions. A multiphysics heat transfer analysis based on computational fluid dynamics (CFDs) was first conducted to quantify the influence of key environmental factors on the thermal response of the sensing elements. Subsequently, an environmental correction model was developed using a multilayer perceptron (MLP) neural network to compensate for the nonlinear effects of meteorological variables. Finally, a field comparison platform was constructed to assess the system’s performance. During the experiments, solar radiation data from a Kipp and Zonen CMP10 pyranometer and longwave radiation values derived from the Stefan–Boltzmann law were used as reference standards. The results showed that the relative errors for solar and longwave radiation measurements ranged from –3.66% to 3.69% and –3.86% to 3.81%, respectively. The root mean square errors (RMSEs) between the estimated and measured values were 15.4 W/m2 for solar radiation and 16.7 W/m2 for longwave radiation, with corresponding mean absolute errors (MAEs) of 9.8 and 11.4 W/m2. The correlation coefficients were 0.98 and 0.96, respectively, indicating a strong agreement with the reference data. These results demonstrate the high accuracy and robustness of the proposed system, highlighting its potential for applications in energy balance analysis, climate monitoring, and agroecological research.

Accurate air temperature observation requires minimizing solar radiation-induced deviations, which are strongly influenced by radiation shield performance. However, conventional shields often produce significant errors under strong solar radiation or weak ventilation. In this study, an air temperature observation system integrating a radiation shield and a backpropagation (BP) neural network-based correction method is proposed. Computational fluid dynamics (CFD) simulations were conducted to quantify radiation-induced temperature deviations under representative meteorological conditions, and the simulated dataset was used to train and test the neural network model. Initial field comparison experiments were performed using a 076B forced-ventilation system as a reference, where measured differences were treated as experimental deviations and model outputs as predicted deviations. The results show that, before correction, the proposed system exhibited a maximum deviation of 1.05 °C and a mean deviation of 0.26 °C, while the root mean square error and mean absolute error between experimental and predicted deviations were 0.30 °C and 0.23 °C, respectively. The correction significantly reduced temperature deviations, demonstrating the effectiveness of the proposed system in improving measurement accuracy.

Excellent architectural design, energy-efficient control systems, and smart home technologies need to take into account the influence of solar radiation. Therefore, there is a necessity for high-precision measurement of solar radiation. However, existing solar radiation instruments are susceptible to environmental factors such as wind speed, air temperature, and air density, resulting in significant measurement errors. Therefore, this paper proposes the design of a solar radiation measurement instrument based on the thermoelectric effect. By integrating neural network algorithms, this instrument can mitigate the influence of environmental factors on solar radiation measurement. First, employing computational fluid dynamics (CFD) for multi-physics simulations of the instrument yielded solar radiation values under various environmental parameters. Subsequently, employing neural network algorithms to train and learn from the CFD simulation results, a quantitative relationship between solar radiation values and environmental parameters was established. This formed a radiation measurement error correction algorithm to mitigate the influence of environmental parameters on solar radiation observation results. Finally, constructing a radiation observation platform validated the measurement accuracy of the instrument. The experimental results indicate that the maximum radiation error of the new instrument is −3.97%, with an average radiation error of −0.16%, and the full-scale radiation error is less than 3.88%.

With the global aging population on the rise, fall-related injuries among the elderly pose significant health risks, making timely and accurate fall detection crucial. Traditional fall detection methods, including wearable sensors, ambient sensors, and camera-based systems, often face limitations such as user compliance, limited coverage, and privacy concerns. This paper proposes a novel human fall detection method utilizing a smart floor system that employs sensors combining capacitance and inductance. These sensors capture electrical properties data, which is then processed using data processing and analysis techniques to accurately detect and differentiate between various human postures such as standing, sitting, and lying down. This system addresses the shortcomings of existing technologies by providing comprehensive coverage without the need for user-worn devices and operates effectively irrespective of environmental conditions. By integrating this system into everyday environments, we can significantly enhance the safety and independence of the elderly, offering a reliable and cost-effective solution for fall detection.

Dear Editor, Tracking control in networked environment is a very challenging problem due to the contradiction of rapid response to the time-varying signal and the inevitable delay introduced by networks. This letter has proposed several fuzzy-inverse-model-based network tracking control frameworks which are helpful in handling the system with nonlinear dynamics and uncertainties. The control frameworks have adopted different strategies such as feedback correction, internal model structure and adaptive technology. Simulations have proved the validity of the strategies. Moreover, the combination of two or more technologies can greatly improve the control performance.

To address the critical role of atmospheric temperature in climate change and disaster monitoring, enhancing measurement accuracy to 0.1 °C is essential. Current instruments are susceptible to radiation interference, resulting in errors of approximately 1 °C. This study introduces a novel temperature sensor that improves accuracy by combining natural ventilation with forced ventilation. Silver-coated aluminum plates (95 % reflectivity) and white-coated deflectors (87 % reflectivity) minimize solar radiation errors. A neural network algorithm, along with CFD simulations, further corrects radiation errors under varying weather conditions. Field tests based on the 076B ventilation device demonstrate that this new sensor reduces the average radiation error to 0.02 °C, achieving a RMSE of 0.034 °C and a MAE of 0.028 °C. The correlation coefficient (r) with the reference temperature reached 0.999, demonstrating the sensor’s high precision and providing an effective solution for reducing temperature measurement errors to below 0.1 °C.