Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any defect. Thermographic inspection is employed for finding such heat related problems before eventual failure of the system. However, an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In the present study, statistical features and artificial neural network (ANN) with confidence level analysis are utilized for inspection of electrical components and their thermal conditions are classified into two classes namely normal and overheated. All the features extracted from images do not produce good performance. Features having low performance reduce the diagnostic performance. The study reveals the performance of each feature individually for selecting the suitable feature set. In order to find the individual feature performance, each feature of thermal image was used as input for neural network and the classification of condition types were used as output target. The multilayered perceptron network using Levenberg-Marquardt training algorithm was used as classifier. The performances were determined in terms of percentage of accuracy, specificity, sensitivity, false positive and false negative. After selecting the suitable features, the study introduces the intelligent diagnosis system using suitable features as inputs of neural network. Finally, confidence percentage and confidence level were used to find out the strength of the network outputs for condition monitoring. The experimental result shows that multilayered perceptron network produced 79.4% of testing accuracy with 43.60%, 12.60%, 21.40, 9.20% and 13.40% highest, high, moderate, low and lowest confidence level respectively.
The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.
Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.
This study aimed to investigate the electrical activity of two muscles located at the dorsal surface during Islamic prayer (Salat). Specifically, the electromyography (EMG) activity of the erector spinae and trapezius muscles during four positions observed while performing Salat, namely standing, bowing, sitting and prostration, were investigated. Seven adult subjects with an average age of 28.1 (± 3.8) years were included in the study. EMG data were obtained from their trapezius and erector spinae muscles while the subjects maintained the specific positions of Salat. The EMG signal was analysed using time and frequency domain features. The results indicate that the trapezius muscle remains relaxed during the standing and sitting positions while the erector spinae muscle remains contracted during these two positions. Additionally, during the bowing and prostration positions of Salat, these two muscles exhibit the opposite activities: the trapezius muscle remains contracted while the erector spinae muscle remains relaxed. Overall, both muscles maintain a balance in terms of contraction and relaxation during bowing and prostration position. The irregularity of the neuro-muscular signal might cause pain and prevent Muslims from performing their obligatory prayer. This study will aid the accurate understanding of how the back muscles respond in specific postures during Salat.
Postconsumer polyethylene terephthalate (PET) has potential applications in many areas of manufacturing, but contamination by hazardous polyvinyl chloride (PVC) in common waste streams can reduce its recyclable value. Separating collected PET-PVC mixtures before recycling remains very challenging because of the similar physicochemical properties of PET and PVC. Herein, we describe a novel flotation process with corona modification pretreatment to facilitate the separation of PET-PVC mixtures. Through water contact angle, surface free energy, X-ray photoelectron and FT-IR characterization, we found that polar hydroxyl groups can be more easily introduced on the PVC surface than on the PET surface induced by corona modification. This selective wetting can suppress the floatability of PVC, leading to the separation of PET as floating product. A reliable mechanism including two different hydrogen-abstraction pathways was established. Response surface methodology consisting of Plackett-Burman and Box-Behnken designs was adopted for optimization of the combined process, and control parameters were solved based on high-quality prediction models, with fitting from significant variables and interactions. For physical or chemical circulation strategies with PET purity prioritization, the validated purity of the product reached 96.05% at a 626 W corona power, 5.42 m/min passing speed, 24.78 mg/L frother concentration and 286 L/h air flow rate. For the energy recuperation strategy with PET recovery prioritization, the factual recovery reached 98.08% under a 601 W corona power, 6.04 m/min passing speed, 27.55 mg/L frother concentration and 184 L/h air flow rate. The current work provides technological insights into the cleaner disposal of waste plastics.
Unusual walk patterns may increase individuals' risks of falling. Anthropometric features of the human body, such as the body mass index (BMI), influences the walk patterns of individuals. In addition to the BMI, uneven walking surfaces may cause variations in the usual walk patterns of an individual that will potentially increase the individual's risk of falling. The objective of this study was to statistically evaluate the variations in the walk patterns of individuals belonging to two BMI groups across a wide range of walking surfaces and to investigate whether a deep learning method could classify the BMI-specific walk patterns with similar variations. Data collected by wearable inertial measurement unit (IMU) sensors attached to individuals with two different BMI were collected while walking on real-world surfaces. In addition to traditional statistical analysis tools, an advanced deep learning-based neural network was used to evaluate and classify the BMI-specific walk patterns. The walk patterns of overweight/obese individuals showed a greater correlation with the corresponding walking surfaces than the normal-weight population. The results were supported by the deep learning method, which was able to classify the walk patterns of overweight/obese (94.8 ± 4.5%) individuals more accurately than those of normal-weight (59.4 ± 23.7%) individuals. The results suggest that application of the deep learning method is more suitable for recognizing the walk patterns of overweight/obese population than those of normal-weight individuals. The findings from the study will potentially inform healthcare applications, including artificial intelligence-based fall assessment systems for minimizing the risk of fall-related incidents among overweight and obese individuals.
Gait data collection from overweight individuals walking on irregular surfaces is a challenging task that can be addressed using inertial measurement unit (IMU) sensors. However, it is unclear how many IMUs are needed, particularly when body attachment locations are not standardized. In this study, we analysed data collected from six body locations, including the torso, upper and lower limbs, to determine which locations exhibit significant variation across different real-world irregular surfaces. We then used deep learning method to verify whether the IMU data recorded from the identified body locations could classify walk patterns across the surfaces. Our results revealed two combinations of body locations, including the thigh and shank (i.e., the left and right shank, and the right thigh and right shank), from which IMU data should be collected to accurately classify walking patterns over real-world irregular surfaces (with classification accuracies of 97.24 and 95.87%, respectively). Our findings suggest that the identified numbers and locations of IMUs could potentially reduce the amount of data recorded and processed to develop a fall prevention system for overweight individuals.