Application on Demodulation of FBG Sensing Signals using Phase Detection Algorithm of Intake and Exhaust

2021 ◽  
Author(s):  
Chenjie Kong ◽  
Tianming Chen ◽  
Jun Zhang ◽  
Guizhong Jiang ◽  
Yuan Shen ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Phase Detection

A phasor-distance based faulty phase detection and fault classification technique for parallel transmission lines

2020 ◽  
Vol 21 (4) ◽  
Author(s):  
Nishant Kothari ◽  
Bhavesh R. Bhalja ◽  
Vivek Pandya ◽  
Pushkar Tripathi ◽  
Soumitri Jena
Keyword(s):  
Transmission Lines ◽  
Power Flow ◽  
Detection Algorithm ◽  
Current Transformer ◽  
Operating Conditions ◽  
Fault Classification ◽  
Phase Detection ◽  
Parallel Transmission ◽  
Classification Technique ◽  
Computational Requirement

AbstractThis paper presents a phasor-distance based faulty phase detection and fault classification technique for parallel transmission lines. Detection and classification of faulty phase(s) have been carried out by deriving indices from the change in phasor values of current with a distance of one cycle. The derived indices have zero values during normal operating conditions whereas the index corresponding to the faulty phase exceeds the pre-defined threshold in case of occurrence of a fault. A separate ground detection algorithm has been utilized for the identification of involvement of ground in a faulty situation. The performance of the proposed technique has been evaluated for intra-circuit, inter-circuit and simultaneous faults with wide variations in system and fault conditions. The suggested technique has been evaluated for over 23,000 diversified simulated fault cases as well as 14 recorded real fault events. The performance of the proposed technique remains consistent under Current Transformer (CT) saturation as well as different amount and direction of power flow. Moreover, suitability to different power system network has also been studied. Also, faults having fault current less than pre-fault conditions have been detected accurately. The results obtained suggest that it is able to detect faulty phases as well as classify faults within quarter-cycle from the inception of fault with impeccable accuracy. Besides, as modern digital relays have been already equipped with phasor computation facility, phasor-based technique can be easily incorporated with relative ease. At last, a comparative evaluation suggests its superiority in terms of fault classification accuracy, fault detection time, diversify fault scenarios and computational requirement among other existing techniques.

scholarly journals Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1081
Author(s):  
Tamon Miyake ◽  
Shintaro Yamamoto ◽  
Satoshi Hosono ◽  
Satoshi Funabashi ◽  
Zhengxue Cheng ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Calibration Method ◽  
Detection Algorithm ◽  
Video Data ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Gait Phase ◽  
Logistic Regression Algorithm ◽  
Short Time

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

scholarly journals Fall inducing movable platform (FIMP) for overground trips and slips

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Jie Kai Er ◽  
Cyril John William Donnelly ◽  
Seng Kwee Wee ◽  
Wei Tech Ang
Keyword(s):  
Real World ◽  
Muscle Activation ◽  
Statistical Parametric Mapping ◽  
Detection Algorithm ◽  
Repeated Measure ◽  
Future Research ◽  
Phase Detection ◽  
In Series ◽  
Gait Phase ◽  
Movable Platform

Abstract Background The study of falls and fall prevention/intervention devices requires the recording of true falls incidence. However, true falls are rare, random, and difficult to collect in real world settings. A system capable of producing falls in an ecologically valid manner will be very helpful in collecting the data necessary to advance our understanding of the neuro and musculoskeletal mechanisms underpinning real-world falls events. Methods A fall inducing movable platform (FIMP) was designed to arrest or accelerate a subject’s ankle to induce a trip or slip. The ankle was arrested posteriorly with an electromagnetic brake and accelerated anteriorly with a motor. A power spring was connected in series between the ankle and the brake/motor to allow freedom of movement (system transparency) when a fall is not being induced. A gait phase detection algorithm was also created to enable precise activation of the fall inducing mechanisms. Statistical Parametric Mapping (SPM1D) and one-way repeated measure ANOVA were used to evaluate the ability of the FIMP to induce a trip or slip. Results During FIMP induced trips, the brake activates at the terminal swing or mid swing gait phase to induce the lowering or skipping strategies, respectively. For the lowering strategy, the characteristic leg lowering and subsequent contralateral leg swing was seen in all subjects. Likewise, for the skipping strategy, all subjects skipped forward on the perturbed leg. Slip was induced by FIMP by using a motor to impart unwanted forward acceleration to the ankle with the help of friction-reducing ground sliding sheets. Joint stiffening was observed during the slips, and subjects universally adopted the surfing strategy after the initial slip. Conclusion The results indicate that FIMP can induce ecologically valid falls under controlled laboratory conditions. The use of SPM1D in conjunction with FIMP allows for the time varying statistical quantification of trip and slip reactive kinematics events. With future research, fall recovery anomalies in subjects can now also be systematically evaluated through the assessment of other neuromuscular variables such as joint forces, muscle activation and muscle forces.

Automated pre-ictal phase detection algorithm from EEG signals

2013 ◽  
Author(s):  
V. Zeljkovic ◽  
C. Druzgalski ◽  
L. Yequn ◽  
C. Jian ◽  
Y. Xin ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Phase Detection ◽  
Eeg Signals

scholarly journals Detecting intermittent steering activity: Development of a phase-detection algorithm

2012 ◽  
Author(s):  
Hugo M. Da Silva Peixoto de Aboim Chaves ◽  
Jasper A. Pauwelussen ◽  
Mark Mulder ◽  
Marinus M van Paassen ◽  
Riender Happee ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Phase Detection

scholarly journals Fall Inducing Movable Platform (FIMP) for Overground Trips and Slips

2020 ◽  
Author(s):  
Jie Kai Er ◽  
Cyril John William Donnelly ◽  
Seng Kwee Wee ◽  
Wei Tech Ang
Keyword(s):  
Statistical Parametric Mapping ◽  
Detection Algorithm ◽  
Repeated Measure ◽  
Phase Detection ◽  
Experimental Conditions ◽  
Gait Phase ◽  
Movable Platform ◽  
Fall Recovery ◽  
Fall Mechanisms ◽  
Fall Prevention Intervention

Abstract The study of falls and any related fall prevention/intervention device requires the recording of true falls incidence. However, true falls are rare, random and difficult to collect. Therefore, a system that can perturb falls in an ecologically valid and repeatedly manner will greatly benefit the understanding of the neuromuscular mechanisms underpinning real-world falls events. A fall inducing movable platform (FIMP) was designed to arrest and accelerate the subject's ankle to induce trip via a brake and slip via a motor respectively. A gait phase detection algorithm was also created to allow the timely activation of the fall mechanisms to induce different recovery actions. Statistical Parametric Mapping (SPM1D) and two sample t-test were used to evaluate the transparency of the platform before it was used to induce falls. Thereafter, SPM1D and one-way repeated measure ANOVA were used assess the effectiveness of FIMP in inducing realistic falls. Walking with the FIMP's fall mechanisms attached on the ankle (SW) was found to be similar to normal walking (NW), except for a slight increase in ankle flexion during the swing phase. However, the magnitude of change would be considered negligible when compared to the changes in joint angles during the trips and slips of interest. During the FIMP induced trips, the brake activates at the terminal-swing and mid-swing gait phase to induce the lowering and skipping strategies respectively. The characteristic leg lowering and the subsequent contralateral leg swing was seen in all subjects for the lowering strategy. Likewise, for skipping strategy, all subjects skipped forward on the perturbed leg. On the other hand, slip was induced by FIMP using the motor to impart unwanted forward acceleration to the ankle with the help of friction-reducing ground sliding sheets. Joints stiffening was observed during slips, and subjects adopt the \textit{surfing} strategy after the initial slip. Results indicate that FIMP can induce reliable and ecologically valid falls repeatedly under simulated experimental conditions. The usage of SPM1D with FIMP allows the creation of the first ever quantifiable trip and slip reactive kinematics comparison. Effects of fall recovery anomalies can now be easily identified.

scholarly journals Detection of Outliers in a Time Series of Available Parking Spaces

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Yanjie Ji ◽  
Dounan Tang ◽  
Weihong Guo ◽  
Phil T. Blythe ◽  
Gang Ren
Keyword(s):  
Outlier Detection ◽  
Detection Method ◽  
Experimental Tests ◽  
Detection Algorithm ◽  
Phase Detection ◽  
Real World Data ◽  
Two Phase ◽  
Real Time Information ◽  
The City ◽  
Parking Facilities

With the provision of any source of real-time information, the timeliness and accuracy of the data provided are paramount to the effectiveness and success of the system and its acceptance by the users. In order to improve the accuracy and reliability of parking guidance systems (PGSs), the technique of outlier mining has been introduced for detecting and analysing outliers in available parking space (APS) datasets. To distinguish outlier features from the APS’s overall periodic tendency, and to simultaneously identify the two types of outliers which naturally exist in APS datasets with intrinsically distinct statistical features, a two-phase detection method is proposed whereby an improved density-based detection algorithm named “local entropy based weighted outlier detection” (EWOD) is also incorporated. Real-world data from parking facilities in the City of Newcastle upon Tyne was used to test the hypothesis. Thereafter, experimental tests were carried out for a comparative study in which the outlier detection performances of the two-phase detection method, statistic-based method, and traditional density-based method were compared and contrasted. The results showed that the proposed method can identify two different kinds of outliers simultaneously and can give a high identifying accuracy of 100% and 92.7% for the first and second types of outliers, respectively.

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