AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults.

In-home monitoring systems have been used to detect cognitive decline in older adults by allowing continuous monitoring of routine activities. In this study, we investigated whether unobtrusive in-house power monitoring technologies could be used to predict cognitive impairment. A total of 94 older adults aged ≥65 years were enrolled in this study. Generalized linear mixed models with subject-specific random intercepts were used to evaluate differences in the usage time of home appliances between people with and without cognitive impairment. Three independent power monitoring parameters representing activity behavior were found to be associated with cognitive impairment. Representative values of mean differences between those with cognitive impairment relative to those without were -13.5 min for induction heating in the spring, -1.80 min for microwave oven in the winter, and -0.82 h for air conditioner in the winter. We developed two prediction models for cognitive impairment, one with power monitoring data and the other without, and found that the former had better predictive ability (accuracy, 0.82; sensitivity, 0.48; specificity, 0.96) compared to the latter (accuracy, 0.76; sensitivity, 0.30; specificity, 0.95). In summary, in-house power monitoring technologies can be used to detect cognitive impairment.

A Compact, High Power Capable, and Tunable High Directivity Microstrip Coupler.

A coupler is an indispensable component to sample the forward and reflected power for the real-time radio frequency (RF) power monitoring system. The directivity of a coupler is a critical factor to achieve accurate RF power measurements. This paper proposes a microstrip coupler with a tunable high directivity circuit to accurately measure the reflected RF power. The directivity tuner composed of passive components adjusts phase and amplitude of the coupled RF signal, and cancel out the leakage signal from the RF input port at the coupled reflection port. The experimental results, which agree with simulation results, show that the microstrip coupler with the directivity tuner circuit has a compact size (~ 0.07 λg x 0.05 λg), high power capability (up to 1 kW), and high directivities (more than 40 dB) at operating frequency bands (f = 297.3 MHz, 400 MHz, and 447 MHz, respectively) for magnetic resonance imaging (MRI) applications.

Research on deep learning garbage classification system based on fusion of image classification and object detection classification.

With the development of national economy, the output of waste is also increasing. People's living standards are constantly improving, and the problem of garbage pollution is increasingly serious, which has a great impact on the environment. Garbage classification and processing has become the focus of today. This topic studies the garbage classification system based on deep learning convolutional neural network, which integrates the garbage classification and recognition methods of image classification and object detection. First, the data sets and data labels used are made, and then the garbage classification data are trained and tested through ResNet and MobileNetV2 algorithms, Three algorithms of YOLOv5 family are used to train and test garbage object data. Finally, five research results of garbage classification are merged. Through consensus voting algorithm, the recognition rate of image classification is improved to 2%. Practice has proved that the recognition rate of garbage image classification has been increased to about 98%, and it has been transplanted to the raspberry pie microcomputer to achieve ideal results.

Multi-exposure electric power monitoring image fusion method without ghosting based on exposure fusion framework and color dissimilarity feature.

To solve the ghosting artifacts problem in dynamic scene multi-scale exposure fusion, an improved multi-exposure fusion method has been proposed without ghosting based on the exposure fusion framework and the color dissimilarity feature of this study. This fusion method can be further applied to power system monitoring and unmanned aerial vehicle monitoring. In this study, first, an improved exposure fusion framework based on the camera response model was applied to preprocess the input image sequence. Second, the initial weight map was estimated by multiplying four weight items. In removing the ghosting weight term, an improved color dissimilarity feature was used to detect the object motion features in dynamic scenes. Finally, the improved pyramid model as adopted to retain detailed information about the poor exposure areas. Experimental results indicated that the proposed method improves the performance of images in terms of sharpness, detail processing, and ghosting artifacts removal and is superior to the five existing multi-exposure image fusion (MEF) methods in quality evaluation.