Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial.
DC distribution of PV systems has spread back especially in the residential sector as a variety of electronic appliances became locally available in the market. The compatibility of household appliances with the best voltage-level in a DC environment is the field that still in the research phase and has not yet made a practically extensive appearance. This paper mainly discusses this issue by providing a review of the concerning research efforts, identifying the gaps in the existing knowledge. The work explains the electrical diagrams of the recently produced appliances, classifying them to get an understanding of how each one consumes energy. It includes exploiting the recent dependence of the commercial appliances on power electronics to improve the efficiency of the existing DC distribution systems by extrapolating new architectures. The proposed topology has a DC distribution environment with two levels of voltage for all appliances. Appliances performances have been evaluated by calculating the energy transfer efficiency. The outcomes of this work can help in designing more efficient DC power distribution networks with minimal energy converters and establishing standardizations for DC microgrids.
The main tool for measuring system efficiency in homes and offices is the energy monitoring of the household appliances' consumption. With the help of GUI through a PC or smart phone, there are various applications that can be developed for energy saving. This work describes the design and prototype implementation of a wireless PV-powered home energy management system under a DC-distribution environment, which allows remote monitoring of appliances' energy consumptions and power rate quality. The system can be managed by a central computer, which obtains the energy data based on XBee RF modules that access the sensor measurements of system components. The proposed integrated prototype framework is characterized by low power consumption due to the lack of components and consists of three layers: XBee-based circuit for processing and communication architecture, solar charge controller, and solar-battery-load matching layers. Six precise analogue channels for data monitoring are considered to cover the energy measurements. Voltage, current and temperature analogue signals were accessed directly from the remote XBee node to be sent in real time with a sampling frequency of 11-123 Hz to capture the possible surge power. The performance shows that the developed prototype proves the DC voltage matching concept and is able to provide accurate and precise results.
The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system's modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.
The use of computer-based automated approaches and improvements in lung sound recording techniques have made lung sound-based diagnostics even better and devoid of subjectivity errors. Using a computer to evaluate lung sound features more thoroughly with the use of analyzing changes in lung sound behavior, recording measurements, suppressing the presence of noise contaminations, and graphical representations are all made possible by computer-based lung sound analysis. This paper starts with a discussion of the need for this research area, providing an overview of the field and the motivations behind it. Following that, it details the survey methodology used in this work. It presents a discussion on the elements of sound-based lung disease classification using machine learning algorithms. This includes commonly prior considered datasets, feature extraction techniques, pre-processing methods, artifact removal methods, lung-heart sound separation, deep learning algorithms, and wavelet transform of lung audio signals. The study introduces studies that review lung screening including a summary table of these references and discusses the literature gaps in the existing studies. It is concluded that the use of sound-based machine learning in the classification of respiratory diseases has promising results. While we believe this material will prove valuable to physicians and researchers exploring sound-signal-based machine learning, large-scale investigations remain essential to solidify the findings and foster wider adoption within the medical community.