Measurement Sensors最新文献

Technology for “wireless power transfer” (WPT) has advanced greatly in recent years, especially when used for renewable power sources. Environmental issues and the growing need for renewable energy sources have prompted a great deal of study in this area. This report provided an overview of the evolution of WPT technologies for renewable energy systems, including their advantages, disadvantages, and future possibilities. An introduction to WTP’s foundational ideas is provided, including techniques such as resonant pairing, capacitive connection, and microwave-based approaches. It delves into how these systems have been modified and improved to facilitate the long-distance transmission of renewable energy sources like wind and solar power. The study emphasizes the benefits of WPT in renewable energy systems, such as improved convenience, lower maintenance costs, and the opportunity to lessen negative effects on the environment. Problems with WPT technology in renewable energy applications are also analyzed critically. Power loss in transmission, misalignment, and electromagnetic interference are all examples of technical difficulties, and these are on top of the financial and legal obstacles. WPT’s effects on humans and animals are also explored since they must be taken into account before broad use of the technology.

In order to improve the quality of urban logistics distribution services and meet customer needs to the greatest extent, improve the utilization rate of service resources and reduce the cost of logistics and distribution services, the author proposes an urban logistics distribution scheduling algorithm based on artificial bee colony. Uncertain dynamic customer demands often occur during the execution of logistics distribution services, resulting in the inability to achieve optimal cost by executing the distribution service according to the original plan, in response to such problems with dynamic customer needs, the author conducted a problem analysis, the corresponding mathematical model is established and transformed into a static problem for rescheduling. The result shows: Using the scheduling algorithm to solve the example 100 times, when the vehicle 1 and vehicle 3 can meet their own load, they can serve customers with new needs, which can make the delivery cost the lowest. In the case of a small number of customers, the algorithm can achieve high accuracy, and when the number of customers reaches a certain scale, the stability of the algorithm will decrease slightly.

Conclusion

The algorithm achieves the purpose of promptly responding to changes in customer demand and quickly adjusting distribution services.

In order to explore the energy-saving control method of electronic communication, the author proposes the application of Internet of Things and embedded technology in electronic communication. The application makes full use of the “Internet of Things” and embedded technology to create an energy-saving system, send data to the energy-saving system to control the power supply, and control operation of electrical appliances to achieve energy saving results.

According to the test results

The energy consumption of the system designed by the author after energy saving is 4.8335kWh, which is lower than that of the device before energy saving, which is 5.8424kWh and saves electricity. At different load current, the system output voltage is controlled at 220 V and the performance of the system is stable.

Conclusion

The system designed according to the model proposed by the author has a good performance in terms of energy consumption and has a connection with Internet technology and embedded technology in fire communication power.

The rapid growth of available wearable technologies, particularly in the healthcare sector, has ushered in a new era of digital health. This progress has introduced new possibilities for the monitoring of vital signs, including breathing rate (BR). BR is a crucial health parameter used to assess an individual's overall well-being; specifically, it can be considered a precise indicator of pathological conditions and stressors. The aim of this study is to conduct a metrological characterization of one of the most widely used BR sensors for the measurement of BR (i.e., Zephyr BioHarness 3.0), comparing it to a BR gold standard monitor (i.e., a spirometer), in determined test conditions, including both natural and defined BR patterns. Additionally, the Monte Carlo method (MCM) was applied to evaluate the propagation of the measurement uncertainty in the identification of the peaks of respiratory signal, used to calculate BR. Results reveal that the Zephyr BioHarness 3.0 sensor provides highly accurate BR measurements, with a precision of ±1 bpm. Bland-Altman analysis indicates a 95 % confidence interval of [-2; 3] bpm, demonstrating a close agreement with the reference instrument. The Pearson's correlation coefficient of 0.95 further validates the linear correlation between the reference/test measurements. The simulation based on MCM provides an expanded uncertainty of ±2 bpm (coverage factor k = 2) in BR estimation due to the uncertainty in the peaks identification (compatible with the accuracy provided by the manufacturer). These results highlight the Zephyr BioHarness 3.0 sensor reliability for BR monitoring while emphasizing the significance of accounting for different disturbing factors, e.g., uncertainty in peak detection during data analysis, also to provide a rigorous level of confidence in the measurement results, hence being able to properly interpret them in biomedical applications.

Deep Q-networks, as a representation of several classical techniques, have emerged as one of the primary branches in the field of value function-based reinforcement learning. The paper addresses two issues that come up in the realm of reinforcement learning for value function solving: estimating bias and maximizing projected action value function evaluation. By treating the estimation of the highest expected action value as a random selection estimation problem, the suggested approach addresses the estimation bias issue from the standpoint of random selection. A random choice estimate procedure forms the basis of the technique. Firstly, a proposed random choice estimator is presented and its theoretical fairness is established. Second, the estimator is applied to create a reinforcement learning method in a different application. Two techniques, namely stochastic two-depth Q-networks and double-Q learning, are suggested based on the random choice estimation technique. The main parameters of the suggested algorithms are then investigated, and parameter formulas for both predictable and unpredictable scenarios are created. Lastly, a random choice estimation perspective suggests a stochastic two-depth Q-network. The new approach may effectively remove bias in value function estimate, enhance learning performance, and stabilise the learning process, according to simulation findings on Grid World and Atari games.

Landscape planning and design is an indispensable part of modern urban construction. In landscape design, rendering technology can transform designers' imagination into actual images, allowing people to better understand the design intent. In order to solve the problem of insufficient precision in image rendering in landscape design, this paper proposes a landscape design image rendering technology based on improved neural network technology. This technology combines stereo matching algorithms, which evaluate the corrected stereo images to calculate the differences between the real scene and the underlying model. The stereo matching algorithm is based on the basic model and obtains accurate depth information from a wide range image pair, making the rendered landscape image more realistic and precise. The experiment demonstrates the effectiveness of the landscape design image rendering system through two important aspects: processing speed and parallax accuracy. The development of image rendering technology in landscape design cannot be separated from the mutual promotion of theory and practice. The technology proposed in this article has certain practical value and hopes to make more positive contributions to urban construction and cultural inheritance.

The global energy landscape is rapidly shifting toward cleaner, lower-carbon electricity generation, necessitating a transition to alternate energy sources. Hydrogen, particularly green hydrogen, looks to be a significant solution for facilitating this transformation, as it is produced by water electrolysis with renewable energy sources such as solar irradiations, wind speed, and biomass residuals. Traditional energy systems are costly and produce energy slowly due to unpredictability in resource supply. To address this challenge, this work provides a novel technique that integrates a multi-renewable energy system using multi objective optimization algorithm to meets the machine learning-based forecasted load model. Several forecasting models, including Autoregressive Integrated Moving Average (ARIMA), Random Forest and Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), are assessed for develop the statistical metrics values such as RMSE, MAE, and MAPE. The selected Non-Sorting Moth Flame Optimization (NSMFO) algorithm demonstrates technological prowess in efficiently achieving global optimization, particularly when handling multiple objective functions. This integrated method shows enormous promise in technological, economic, and environmental terms, emphasizing its ability to promote energy sustainability targets.

In order to fully utilize the existing resources of music information, people have found that music information retrieval technology has important research significance for personalized music teaching. Overall, music information retrieval technology is still in the experimental exploration stage and lacks technical and practical systems. In this context, this article proposes recommendation algorithms that can effectively adjust the content of information services based on user preferences. However, recommendation algorithms are still in an immature and complex stage, and there are still issues with their accuracy. In response to the issue of recommendation accuracy, this article utilizes information retrieval technology to optimize recommendation algorithms. Combine these two algorithms for recommendation. The main steps that affect recommendation results in recommendation algorithms include similarity calculation, nearest neighbor selection method, and score prediction method calculation. The experimental results indicate that the proposed algorithm can effectively solve the existing problems of users, and can also accurately retrieve the content of interest, greatly improving the diversity of the proposed content. We use utilizing algorithmic learning to create a personalized music teaching system that enables students to independently advance towards their self-development goals, ultimately achieving the goal of promoting the harmonious development of students' personalities.

Urban ecological security is not only a basic safety requirement for human security and development, but also an important part of national security and other security and infrastructure carriers. Protecting the environment is an important factor in the construction of China's natural civilization. In recent years, with the deterioration of the environment, the region and its people have paid more and more attention to the safety of urban ecological environment, which has become one of the hottest issues in current research. Land use/cover rate change (LUCC) affects the environment, which will also affect environmental safety, and changes in its appearance will also affect nature and the environment, providing a positive impetus for the control of natural processes. In this paper, researches on the above problems in City K and Zhengzhou are carried out, using MSPA and visual image forms to better connect local resources, and through the MCR model based on ecological control, a weight-based multi-structure network model is established, which is conducive to the establishment of a multi-structure network model based on ecological control. Build a city of environmental science network. The GIS control line was explored, and the concept of the control line was filled to a certain extent. At the same time, taking City K and Zhengzhou as examples, determining the breadth of their layout and management, as well as the control strategy of their environmental control lines, provides a specific reference angle for the layout and research of other cities. Urban ecological security is very important to each of us living in cities. It is a difficult problem at this stage to establish an ecological security model to open a new security pattern.