In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

Physical fitness is the overall ability of the body to adapt to the working environment and perform sporting and daily activities. The aim of this study was to analyze the correlation between muscle fitness and the electromyography (EMG) signals of lower limbs under varying exercise intensity. The standing long jump was used as a test task for assessing the power of the lower limb muscles. Participants were university freshmen who belonged to the top 20%, middle 20%, and bottom 20% groups in terms of physical fitness norms. The EMG signals of the participants’ lower limbs while they performed squats were collected under four exercise intensities of repetitions maximum (RM): no load, 8RM, 18RM, and 28RM; the features of the signals were extracted using time-domain and frequency-domain analysis. Statistical analysis was also performed. The top and bottom groups exhibited significant differences time-domain indicators mean absolute value (MAV) and average amplitude change (AAC) in the low-intensity exercise (28RM). The MAV, variance of EMG (VAR), root mean square (RMS), and AAC were significantly different between the top and bottom groups in the three graded intensities (8RM, 18RM, and 28RM). The mean frequency (MNF) and median frequency (MDF), which are frequency-domain indicators, were significantly different between the top and bottom groups in the low-intensity (28RM) and moderate-intensity (18RM) exercises.

Postural orthostatic tachycardia syndrome (POTS) typically occurs in youths, and early accurate POTS diagnosis is challenging. A recent hypothesis suggests that upright cognitive impairment in POTS occurs because reduced cerebral blood flow velocity (CBFV) and cerebrovascular response to carbon dioxide (CO₂) are nonlinear during transient changes in end-tidal CO₂ (PETCO₂). This novel study aimed to reveal the interaction between cerebral autoregulation and ventilatory control in POTS patients by using tilt table and hyperventilation to alter the CO₂ tension between 10 and 30 mmHg. The cerebral blood flow velocity (CBFV), partial pressure of end-tidal carbon dioxide (PETCO₂), and other cardiopulmonary signals were recorded for POTS patients and two healthy groups including those aged >45 years (Healthy-Elder) and aged <45 years (Healthy-Youth) throughout the experiment. Two nonlinear regression functions, Models I and II, were applied to evaluate their CBFV-PETCO₂ relationship and cerebral vasomotor reactivity (CVMR). Among the estimated parameters, the curve-fitting Model I for CBFV and CVMR responses to CO₂ for POTS patients demonstrated an observable dissimilarity in CBFV_max (p = 0.011), mid-PETCO₂ (p = 0.013), and PETCO₂ range (p = 0.023) compared with those of Healthy-Youth and in CBFV_max (p = 0.015) and CVMR_max compared with those of Healthy-Elder. With curve-fitting Model II for POTS patients, the fit parameters of curvilinear (p = 0.036) and PETCO₂ level (p = 0.033) displayed significant difference in comparison with Healthy-Youth parameters; range of change (p = 0.042), PETCO₂ level, and CBFV_max also displayed a significant difference in comparison with Healthy-Elder parameters. The results of this study contribute toward developing an early accurate diagnosis of impaired CBFV responses to CO₂ for POTS patients.

In traditional industrial fields, a robot arm is usually used for high-precision or highly repetitive movements, but now, with the development of three-dimensional (3D) stereo machine vision in smart manufacturing, the smart factory has moved toward the development of a robot arm combined with the image recognition technology. Currently, in the manufacturing industry, most of the images for computing are obtained using two-dimensional (2D) machine vision; here, the 2D advantage is that the camera lens can obtain the simulation of the plane color pixel, but the disadvantage is that it cannot obtain the real space depth distance information, resulting in a more accurate analysis of the workpiece position and features. Therefore, in this study, a pixel-wise voting network (PVNet)-based object pose estimation and feature extraction was developed to perform more diverse object testing for the considered network model. Unlike other workpiece picking systems for smart manufacturing, most of the systems today still framed the workpiece in two dimensions only, but the approach proposed in this paper framed the workpiece pose in three dimensions. Thus, the network could successfully predict the pose even when the workpiece was obscured or the image was not fully captured. The images were input into the neural network by means of supervised learning, and training was performed using transformation matrices between multi-angle images of the artifacts and feature points extracted from the 3D models. The results of this study revealed the pose estimation results of various objects at different viewing angles and proposed the feature gripping strategy for the robot arm to follow this process in the future.