Research on Localization Parameter Estimation and Algorithm in the Digital Television Terrestrial BroadcastingRead the full article
Journal of Sensors publishes research focused on all aspects of sensors, from their theory and design, to the applications of complete sensing devices.
Chief Editor, Professor Harith Ahmad, is currently the director of the Photonics Research Center, University of Malaya, Malaysia. His current research is in the exploration of various 2D and 3D nanomaterials for optoelectronics applications.
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Influence Analysis of Rock Mass Mechanical Properties on Tunnel Rock Stability Based on Sensor Data Integration
This study takes the multisensor monitoring data of tunnel support structure and surrounding rock mechanical properties during TBM construction as the research object, integrates the monitoring data of multisensor through an intelligent optimization algorithm, and explores the self-consistent fusion characteristics between the multisensor structural mechanical properties and the field monitoring data. At the same time, the support structure parameters and construction progress in the tunnel construction process are dynamically adjusted and scientifically integrated through the information feedback of monitoring data. The research results are as follows: (1) The tunnel excavation rate is different under different rock strengths and surrounding rock levels. The overall performance is that the harder the surrounding rock, the lower the surrounding rock level, and the faster the tunnel excavation rate. The relationship between the FPI parameters of surrounding rock and the deformation of tunnel surrounding rock is closer, and the correlation coefficient reaches 98%. The mechanical properties of the surrounding rock have the greatest influence on its deformation. (2) The rotation speed, thrust, and torque of TBM machinery as well as the surrounding rock workability index and the tunnel surrounding rock deformation maintain a good fitting relationship, with a fitting rate of more than 85%. After the improved PSO-BP algorithm is used to calculate and fit the four parameters of torque, rotational speed, thrust, and surrounding rock drivable coefficient during TBM mechanical construction, the fitting accuracy is higher than 94%. (3) At the same time, according to the analysis of the fitting results, the fitting effect of TBM mechanical thrust is the best among the four-parameter algorithm analysis fitting degrees, which is 99.3%. (4) The measured points are evenly distributed near the surrounding rock deformation surface calculated by the improved PSO-BP algorithm, and the fitting variance is 0.972. With the tunnel excavation, the settlement deformation of surrounding rock first increases rapidly, and then, the settlement deformation enters a slow growth stage and finally reaches a relatively stable state. (5) After the jam occurs, the tunnel is expanded from the telescopic shield or tail shield to the outer side of the shield to reduce the contact area between the shield and the surrounding rock to reduce the extrusion of the surrounding rock pressure on the shield.
IoT-Based Voice-Controlled Smart Homes with Source Separation Based on Deep Learning
The widespread availability of cutting-edge computer technologies has shed light on the relevance of artificial intelligence (AI) applications in almost all sectors of the economy. As a result of the incorporation of voice control processing into many Internet of Things (IoT) devices, many of these IoT devices may be operated using spoken commands. The environment that is controlled by speech may include several devices, each of which may be used for a separate activity; yet, all of the devices may collect and process the same command at the same time. This may be the case if the devices can communicate with one another. Because other devices may choose to ignore orders that are intended for particular devices if those devices are not equipped to deal with those orders, only the device that is designed to carry out the activity and process the command will be able to carry out the activity. This is because only the device that is designed to carry out the activity and process the command will be able to carry out the activity. On the other hand, when all of the voice-controlled devices capture the command through the microphone, there is a greater chance that it will mix with other sounds coming from a variety of sources. This is because the microphone is being used to capture the command from all of the voice-controlled devices. These noises may include those that are emanating from the television, music systems, and other sounds that are created by activities taking on inside the family, among other things. During the identification of instructions via processing, any blending of other sounds that are not the primary command is regarded as noise and has to be deleted. This is because any such blending is deemed to be noise. The direction of arrival (also known as DOA) of the sound waves is given primary consideration by this approach. This is done at the same time as the performance of the system, and the proposal for it are being evaluated. Based on the angle of arrival estimate, a specific room impulse response (RIR) from a collection of defined RIR is identified as a room acoustic characteristic, and source separation is carried out using the technique of independent component analysis (ICA). Following the completion of the analysis of the signals produced by the split command speech, the characteristics of the speech are retrieved from the signals. The Mel-frequency cepstral coefficients (MFCC) approach is used so that the operation of feature extraction may be carried out. This is the goal of the technique. Following that, a support vector machine classifier is used to the data in order to further split these characteristics into a large range of distinct groups. Comparisons are made between the performance of the SVM classifier and the performance of a large number of different classifiers, including decision trees, which are often used in applications that incorporate machine learning (DT). After analyzing its performance, the multiclass SVM classifier is found to have an accuracy of 91%, according to the conclusions of the study. Utilizing a classifier that is based on a probabilistic neural network, which is sometimes referred to as a PNN, is one way in which the accuracy of future classifications may be enhanced. This particular classifier is made up of three layers: one layer of gated recurrent units (GRU), one layer of long short-term memory (LSTM), and one layer that integrates the two of those different kinds of memory. This classification seems to have obtained an accuracy of 94.5 percent, which is higher than the classification accuracy attained by the multiclass SVM classifier.
Application of GIS and Multisensor Technology in Green Urban Garden Landscape Design
In order to solve the problem of low definition of the original 3D virtual imaging system, the author proposes the application method of GIS and multisensor technology in green urban garden landscape design. By formulating a hardware design framework, an image collector is selected for image acquisition according to the framework, the image is filtered and denoised by a computer, the processed image is output through laser refraction, and a photoreceptor and a transparent transmission module are used for virtual imaging. Formulate a software design framework, perform noise reduction processing on the collected image through convolutional neural network calculation, and use pixel grayscale calculation to obtain the feature points of the original image, and use C language to set and output the virtual imaging, thus completing the software design. Combined with the above hardware and software design, the design of 3D virtual imaging system in garden landscape design is completed. Construct a comparative experiment to compare with the original system. The results showed the following: The designed system has a significant improvement in the clarity, the original system clarity is 82%~85%, and the image clarity of this system is 85%~90%. In conclusion, the author designed the method to be more effective.
Analysis of Chinese Household Consumption Expenditure Structure System Based on Factors and Clustering Algorithms
In recent years, with the gradual implementation of the central government’s policy of expanding domestic demand, the living standards of urban residents have been greatly improved. With the increasing level of urban living consumption, residents’ living consumption situation has attracted more attention and attention. In order to further increase the consumption income of households, it is necessary to conduct corresponding analysis on households in various regions. Based on SPSS statistical simulation software, this paper uses factor analysis and clustering methods often used in modern multivariate economic statistical analysis to establish multivariate statistical models and calculate and establish various analysis models suitable for my country’s household consumption expenditure structure (2020) and analyze the parameters, accordingly. From the perspectives of politics, economy, geography, etc., this paper reveals the changes in energy consumption in China’s family and social life and its possible implied related information, laws, directions, and the overall development level of society. Through empirical analysis and research, this paper can finally conclude that the current consumption expenditure structure of Chinese households is affected by a combination of factors such as national policies, economic development and technical conditions, and geographical environment characteristics, and the effect is different in different cities.
Research on Interactive Design Algorithm of Folk Museum Exhibit Map Based on Intelligent Wireless Sensor Network
With the continuous improvement of people’s living standards, people’s pursuit of material life and satisfaction of spirituality are also increasing, and the demand for culture and value orientation are also diversifying. As an important position of many cultural consumption places, museums have seen an unprecedented increase in their cultural radiation and public attention. In such a large social environment, the nature museum industry increasingly recognizes that to make full use of the role of museums and better reflects the characteristics of the industry, it is necessary to introduce visual image design into museums. However, visual imagery design in domestic museums is still in its infancy; only a few large national museums have adopted visual imagery design, while relevant research in small folk art museums is still rare. This paper takes Guangzhou Folk Museum as the research object, based on which a new model of interactive design of Guangzhou Folk Museum display map using intelligent wireless sensor technology is proposed, combining the industrial characteristics of folk art and the visual imagery characteristics of Guangzhou Folk Museum. By analyzing the performance of the core algorithm of this system, it is concluded that the performance of the system is very good. However, the application of this model can enable the Guangzhou Folk Museum to establish the brand image of Guangzhou Folk Museum and create more folk culture value in the face of new opportunities and challenges.
Sensor-Based Exercise Rehabilitation Robot Training Method
In order to provide convenience for rehabilitation doctors to formulate rehabilitation plans for patients, this paper proposes a training method for exercise rehabilitation robots based on sensors. In this research, the customized wearable sensor and universal mobile terminal are used as the hardware. Based on the sensor, the motion capture algorithm and motion reconstruction algorithm are developed. The table of experimental results shows that the cost can be saved by using the sensor, and the data can be captured accurately, which can meet the needs of rehabilitation medicine for motor function evaluation and training guidance. The range of motion of the joint and the manual measurement value are within 5°, which can meet the needs of rehabilitation medicine for motor function evaluation and training guidance. The system delay is less than 0.5 s, which has good real-time performance and can respond quickly to emergencies, ensuring the safety of patients’ out-of-hospital rehabilitation. The training method of motion rehabilitation robot based on sensor is helpful for rehabilitation doctors to carry out statistical data of functional evaluation and is of great significance for rehabilitation doctors to make training plans for patients and carry out rehabilitation training.