21 research outputs found
Performance of impedance measurement algorithm applied in line with a compensation circuit
Parametric sensitivity analysis of SEL-421 distance relay algorithms used in compensated line
Improving Current and Voltage Transformers Accuracy Using Artificial Neural Network
Part 11: Engineering Applications of AI and Artificial Neural NetworksInternational audienceCapacitive Voltage Transformers (CVTs) and Current Transformers (CTs) are commonly used in high voltage (HV) and extra high voltage (EHV) systems to provide signals for protecting and measuring devices. Transient response of CTs and CVTs could lead to relay mal-operation. To avoid these phenomena, this paper proposes an artificial neural network (ANN) method to correct CTs and CVTs secondary waveform distortions caused by the transients. PSCAD/EMTDC software is employed to produce the required voltage and current signals which are used for the training process and finally the results show that the proposed method is accurate and reliable in estimation of the CT primary current and the CVT primary voltage
Transhumeral Loading During Advanced Upper Extremity Activities of Daily Living
Percutaneous osseointegrated (OI) implants for direct skeletal attachment of upper extremity prosthetics represent an alternative to traditional socket suspension that may yield improved patient function and satisfaction. This is especially true in high-level, transhumeral amputees where prosthetic fitting is challenging and abandonment rates remain high. However, maintaining mechanical integrity of the bone-implant interface is crucial for safe clinical introduction of this technology. The collection of population data on the transhumeral loading environment will aid in the design of compliance and overload protection devices that mitigate the risk of periprosthetic fracture. We collected marker-based upper extremity kinematic data from non-amputee volunteers during advanced activities of daily living (AADLs) that applied dynamic loading to the humerus. These kinematic data are available for download and will aid in the development of overload protection devices and appropriate post-operative rehabilitation protocols that balance return to an active lifestyle with patient safety
Comparison of free and locked elbow conditions.
<p>Peak moments and forces at 25% humeral length for AADLs performed with both free and locked elbow: underhand toss, jogging, jumping jacks. (A) Significant differences were only observed for peak bending moments during jumping jacks (p = 0.037). (B) No significant differences in torsional moment. (C) Significant differences were only observed for peak axial forces during jogging (p = 0.017). Note that jogging included only N = 15 subjects for which a full gait cycle was available in both free and constrained elbow conditions.</p
Gender differences by AADL at the 25% amputation level.
<p>Gender differences by AADL at the 25% amputation level.</p
List of advanced AADLs ranked in ascending order of demand.
<p>List of advanced AADLs ranked in ascending order of demand.</p
Mean bending moment, torsional moment and axial force curves.
<p>Mean (solid line) ± SD (shaded) moments and forces at 25% humeral length for advanced AADLs as a percent of the activity cycle (x-axis). Jogging curves are from subject data that captured a full gait cycle (N = 23). Jumping jack curves represent three consecutive jumping jacks constituting one cycle.</p
Capture volume and marker placement for 3D kinematic motion capture.
<p>Representative image of the experimental setup including 10 camera positions (pyramids), in-ground force plates (labeled 1 & 2 but not utilized), and kinematic model within the motion capture laboratory (left), marker placement defining rigid body segments (center), and Visual 3D upper extremity model including marker visualization, virtual amputation levels (25, 50, 75% residual humerus), segment geometries and coordinate axes (right).</p
Bending moments during jumping jacks.
<p>The proximal humerus (25% amputation level) experienced the highest bending moment across all subjects. Lines represent averages across all subjects for each amputation level. Shaded areas represent upper and lower standard deviations for the proximal and distal humeral segments, respectively.</p