Failure Mode Detection and Validation of a Shaft-Bearing System with Common Sensors.

Failure mode detection is essential for bearing life prediction to protect the shafts on the machinery. This work demonstrates the rolling bearing vibration measurement, signals converting and analysis, feature extraction, and machine learning with neural networks to achieve failure mode detection for a shaft bearing. Two self-designed bearing test platforms with two types of sensors conduct the bearing vibration collection in normal and abnormal states. The time-domain signals convert to the frequency domain for analysis to observe the dominant frequency between these two types of sensors. In feature extraction, principal components analysis (PCA) combines with wavelet packet decomposition (WPD) to form the two feature extraction methods: PCA-WPD and WPD-PCA for optimization. The features extracted by these two methods serve as input to the long short-term memory (LSTM) networks for classification and training to distinguish bearing states in normal, misaligned, unbalanced, and impact loads. The evaluation arguments include sensor types, vibration directions, failure modes, feature extraction methods, and neural networks. In conclusion, the developed methods with the typical lower-cost sensor can achieve 97% accuracy in bearing failure mode detection.

Effective Free-Driving Region Detection for Mobile Robots by Uncertainty Estimation Using RGB-D Data.

Accurate segmentation of drivable areas and road obstacles is critical for autonomous mobile robots to navigate safely in indoor and outdoor environments. With the fast advancement of deep learning, mobile robots may now perform autonomous navigation based on what they learned in the learning phase. On the other hand, existing techniques often have low performance when confronted with complex situations since unfamiliar objects are not included in the training dataset. Additionally, the use of a large amount of labeled data is generally essential for training deep neural networks to achieve good performance, which is time-consuming and labor-intensive. Thus, this paper presents a solution to these issues by proposing a self-supervised learning method for the drivable areas and road anomaly segmentation. First, we propose the Automatic Generating Segmentation Label (AGSL) framework, which is an efficient system automatically generating segmentation labels for drivable areas and road anomalies by finding dissimilarities between the input and resynthesized image and localizing obstacles in the disparity map. Then, we train RGB-D datasets with a semantic segmentation network using self-generated ground truth labels derived from our method (AGSL labels) to get the pre-trained model. The results showed that our AGSL achieved high performance in labeling evaluation, and the pre-trained model also obtains certain confidence in real-time segmentation application on mobile robots.

Wireless Stimulus-on-Device Design for Novel P300 Hybrid Brain-Computer Interface Applications.

Improving the independent living ability of people who have suffered spinal cord injuries (SCIs) is essential for their quality of life. Brain-computer interfaces (BCIs) provide promising solutions for people with high-level SCIs. This paper proposes a novel and practical P300-based hybrid stimulus-on-device (SoD) BCI architecture for wireless networking applications. Instead of a stimulus-on-panel architecture (SoP), the proposed SoD architecture provides an intuitive control scheme. However, because P300 recognitions rely on the synchronization between stimuli and response potentials, the variation of latency between target stimuli and elicited P300 is a concern when applying a P300-based BCI to wireless applications. In addition, the subject-dependent variation of elicited P300 affects the performance of the BCI. Thus, an adaptive model that determines an appropriate interval for P300 feature extraction was proposed in this paper. Hence, this paper employed the artificial bee colony- (ABC-) based interval type-2 fuzzy logic system (IT2FLS) to deal with the variation of latency between target stimuli and elicited P300 so that the proposed P300-based SoD approach would be feasible. Furthermore, the target and nontarget stimuli were identified in terms of a support vector machine (SVM) classifier. Experimental results showed that, from five subjects, the performance of classification and information transfer rate were improved after calibrations (86.00% and 24.2 bits/ min before calibrations; 90.25% and 27.9 bits/ min after calibrations).

Systematic node management mechanism using ZigBee-based real-time vital sign information monitoring system to manage large numbers of patients.

BACKGROUND: Local hospitals must deal with large numbers of patients during mass casualty incidents, and the wireless sensor networks (WSNs) can help in these situations by monitoring vital signs. Conventional ZigBee nodes can obtain the ID of a device by assigning a unique 16-bit short address or by burning firmware into an IC. These methods tend to complicate node management and lack portability. OBJECTIVE: The study developed a node management mechanism to deal with a large number of patients in real-time, through the wireless monitoring of physiological signals. The mechanism proposed for the ZigBee WSN is based on a three-layer (Coordinator, Control Router, and End Device) tree topology. METHODS: The proposed system includes a node deployment process to formulate a ZigBee WSN as a tree topology, an algorithm to automatically number ZigBee nodes for monitoring and control system (MCS), and an algorithm to automatically obtain the short addresses of nodes for data collection. Specifically, an algorithm automatically collects data from ZigBee nodes for display on a computer graphical user interface (GUI). We also developed a reliable data transmission method capable of resolving the problem of packet loss. RESULTS: The proposed method has been applied in a local hospital. Our research findings provide a valuable reference for the development of ZigBee-based MCS. CONCLUSIONS: The proposed node management mechanism is faster, more reliable, and more intuitive to use, than traditional methods.

Development of a Blood Pressure Measurement Instrument with Active Cuff Pressure Control Schemes.

This paper presents an oscillometric blood pressure (BP) measurement approach based on the active control schemes of cuff pressure. Compared with conventional electronic BP instruments, the novelty of the proposed BP measurement approach is to utilize a variable volume chamber which actively and stably alters the cuff pressure during inflating or deflating cycles. The variable volume chamber is operated with a closed-loop pressure control scheme, and it is activated by controlling the piston position of a single-acting cylinder driven by a screw motor. Therefore, the variable volume chamber could significantly eliminate the air turbulence disturbance during the air injection stage when compared to an air pump mechanism. Furthermore, the proposed active BP measurement approach is capable of measuring BP characteristics, including systolic blood pressure (SBP) and diastolic blood pressure (DBP), during the inflating cycle. Two modes of air injection measurement (AIM) and accurate dual-way measurement (ADM) were proposed. According to the healthy subject experiment results, AIM reduced 34.21% and ADM reduced 15.78% of the measurement time when compared to a commercial BP monitor. Furthermore, the ADM performed much consistently (i.e., less standard deviation) in the measurements when compared to a commercial BP monitor.

Corrigendum to "Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation".

[This corrects the article DOI: 10.1155/2016/3039454.].

Changes in the spectral index of skin-surface laser Doppler signals of nude mice following the injection of CT26 tumor cells.

This study investigated microcirculatory-blood-flow responses in nude mice following the injection of CT26 tumor cells by analyzing the frequency content of skin blood-flow signals recorded on the skin surface. CT26 cells were injected subcutaneously (10^4/100 µl) into the right back flank of each 7-week-old mouse. Three-minute laser Doppler flowmetry (LDF) signals were measured in 60 nude mice. The data sequences were obtained at 1, 2, and 3 weeks after injecting CT26 cells. Mouse tissue samples were cut into sections and examined microscopically to determine the condition of cancer metastasis. Spectral analysis performed after 1 week revealed a significant decrease in the relative energy contribution of the endothelium-related frequency band, and significant increases in those of the myogenic and respiration-related frequency bands of the LDF signals in the metastasis group (n=12). To the best of our knowledge, this is the first study demonstrating the feasibility of evaluating metastasis in animal subjects based on changes in noninvasively measured LDF parameters. Changes in the LDF spectral indexes can be attributed to differences in the microcirculatory regulatory activities. The present measurements performed on the skin surface provide a noninvasive and real-time method for evaluating the microcirculatory responses induced by implanting CT26 tumor cells.

Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation.

A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI). The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM) and accuracy-recognition mode (AM), were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR). When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints.

Postural alignment in children with bilateral spastic cerebral palsy using a bimanual interface for powered wheelchair control.

OBJECTIVE: To examine postural alignment in children with bilateral spastic cerebral palsy while driving a powered wheelchair using both a unilateral joystick and an innovative bimanual interface. DESIGN: Cross-sectional study. SUBJECTS: A total of 20 children with bilateral spastic cerebral palsy (mean age 9.0 years (standard deviation 2.1); 11 with diplegia, 9 with quadriplegia) and 14 typically developing children (mean age 7.7 years (standard deviation 2.9)). METHODS: All children drove the powered wheelchair in both the unilateral and bimanual conditions. The Seated Postural Control Measure quantified the postural alignment of subjects while driving the powered wheelchair. Statistical analysis was carried out using repeated measures analysis of variance and Spearman's rank correlation coefficient. RESULTS: As expected, typically developing children had better postural alignment in both driving conditions than children with cerebral palsy. Children with cerebral palsy demonstrated more symmetrical postural alignment while using the bimanual interface than when using the unilateral joystick. In addition, the severity of cerebral palsy correlated moderately with postural symmetry in both conditions. CONCLUSION: The results suggest that this innovative bimanual interface might be beneficial for promoting symmetrical postural alignment in some children with bilateral spastic cerebral palsy.

Bimanual gliding control for indoor power wheelchair driving.

Power wheelchairs are essential for many individuals with mobility impairment. The objective of this study was to investigate the effectiveness of bimanual gliding (BG) and conventional joystick (CJ) control in an indoor environment, with application to (1) wheelchair driving performance (i.e., practice time, completion time, and driving deviation) and (2) muscle activity of the upper limbs. This study included 22 participants (11 experienced manual wheelchair users and 11 novice manual wheelchair users). Experienced wheelchair users who used the BG strategy needed less time to practice and complete the task. Muscle activity of the upper limbs was focused on the triceps brachii, with relatively less use of the wrist muscles while applying the BG strategy. In novice wheelchair users, wrist muscles were less involved when using the BG control compared with the CJ control. The findings imply that it is feasible to modify manual wheelchairs using BG and motors, which can serve as an alternative option for wheelchair users.

The position of the bipolar cup reflects the direction of the hip contact force acting on it.

We radiographically measured the bipolar cup position to analyze the direction of joint force acting on the bipolar cup. The abduction angle of the bipolar cup was measured in each radiograph taken immediately and at six 12 weeks and yearly after the operation. Radiographs in patients with weight bearing were also investigated. The results indicated that the abduction angle of the bipolar cup was 24.1 degrees +/- 11.2 degrees immediate postoperatively and was 16.2 degrees +/- 5.1 degrees at 6 weeks, 16.1 degrees +/- 5.1 degrees at 3 months, and 16.2 degrees +/- 5.1 degrees at 1 year. The cup abduction angles with weight bearing were not different from those without weight bearing and were 15.9 degrees +/- 4.9 degrees , 16.2 degrees +/- 4.4 degrees , and 16.1 degrees +/- 4.7 degrees on the supine, double-legged stance, and single-legged stance radiographs, respectively. Because the position of the bipolar cup reflects the direction of loads pivoting on it, the direction of the joint force in the frontal plane acting on the bipolar prosthesis is about 16 degrees to vertical.