Anh Mai

Neural Network–Based Intelligent Compaction Analyzer for Estimating Compaction Quality of Hot Asphalt Mixes

Continuous real-time estimating of compaction quality during the construction of a hot mix asphalt (HMA) pavement is addressed in this paper. The densification of asphalt pavements during construction usually is accomplished by using vibratory compactors. During compaction, the compactor and the asphalt mat form a coupled system whose dynamics are influenced by the changing stiffness of the mat. The measured vibrations of the compactor along with process parameters such as lift thickness, mix type, mix temperature, and compaction pressure can be used to predict the asphalt mat density. Contrary to existing techniques in the literature in which a model is developed to fit experimental data and to predict mat density, a neural network-based approach is adopted that is model-free and uses pattern-recognition techniques to estimate density. The neural network is designed to read the entire frequency spectrum of roller vibrations and to classify these vibrations into different levels. The intelligent asphalt c...

Calibration Procedures for the Intelligent Asphalt Compaction Analyzer

The intelligent asphalt compaction analyzer (IACA) is a device based on neural network technology that can measure the density of an asphalt pavement continuously in real time during its construction. It was shown during limited field trials that the IACA could, in real time, measure the density of an asphalt pavement during its construction with accuracy comparable to existing point-wise measurement technologies. In this paper, the procedure to calibrate the IACA and the validation of the performance, i.e., accuracy of density measurements, are addressed. The results demonstrate that the IACA can be used to determine the density of the asphalt mat during compaction with an accuracy needed for quality control operations in the field.

Effect of prosthetic foot on residuum-socket interface pressure and gait characteristics in an otherwise healthy man with transtibial osteomyoplastic amputation

ABSTRACT This article elucidates the effect of prosthetic foot on the residuum-socket interface (RSI) pressure and gait characteristics in a man with transtibial osteomyoplastic amputation (TOA). The study evaluates the effect of three prosthetic feet, including 1) Renegade Foot® from Freedom Innovations (Irvine, CA), 2) Venture Foot™ from College Park (Fraser, MI), and 3) Proprio Foot® from Össur (Reykjavik, Iceland) in six gait activities: walking forward at “normal” pace, walking forward at fast pace, ascending and descending a staircase, and ascending and descending a ramp. Force resistive sensors were placed at six locations, including distal anterior end-bearing, middle posterior, and four proximal points inside the prosthetic socket, to capture real-time RSI pressures. Whereas nominal values of pressure were observed in the proximal region, greater pressure was observed at the distal anterior end-bearing region of the socket, which confirmed one of the intended outcomes of the TOA procedure. Of 36 statistical tests (t-test, p < 0.05), 35 tests (97.2%) confirmed the hypothesis that when the same prosthetic foot was used in the same gait activity, peak and mean pressures are greater at the distal anterior end-bearing location than at other locations. Furthermore, 182 of 216 (84.2%) statistical tests (t-test, significance level of 0.05) supported the hypothesis that at the same measured location during the same gait activity, different prosthetic feet result in different peak (or mean) RSI pressures. Coefficients of variation of the mean sustained pressures showed that when the gait activity was changed, each prosthetic foot affected the sustained pressure differently, even at the same measured location. Each prosthetic foot also had a direct effect on temporal gait parameters such as stance phase and gait cycle durations. These results elucidate the importance for clinicians to understand the characteristics of different prosthetic foot designs to match with the specific needs of the client with amputation.

Unmanned Aerial Vehicles operational requirements and fault-tolerant robust control in level flight

Unmanned Aerial Vehicles (UAVs) are playing an important role both in military as well as civilian applications. However, their role in civilian applications is hampered by the lack of adequate guidelines and operational requirements. In this paper a set of flight, operational and performance requirements for the use of fixed wing UAVs in civilian applications are collated from several resources in the public domain. The application of these requirements to the flight control of an unmanned fixed-wing aircraft is also addressed in this paper. A robust controller is designed to maintain the stability and the desired performance of the system in the presence of modeling uncertainty and measurement noise. A neural network based Fault Detection and Identification (FDI) scheme is then developed to estimate the effectiveness of control inputs. Finally, a reconfigurable controller is designed to compensate for the degradation of the actuation on the occurrence of a fault. Monte Carlo simulation is used to validate the capability and performance of the designed controller.

Gait identification for an intelligent prosthetic foot

Design of an actively controlled prosthetic foot is an emerging research area in robotics. When there are changes in walking conditions such as terrain or speed, classical control methods might confront difficulties. An intelligent prosthetic foot will adapt more efficiently to those changes if it is equipped with an online learning control algorithm. To design such controller, the first step is to acquire real-time gait information from the amputee to study walking behaviors of the individual. In this paper, we developed a neural network-based gait pattern classifier and a rule-based gait phase detector which will provide gait information in real-time.

Field validation of the Intelligent Asphalt Compaction Analyzer

The Intelligent Asphalt Compaction Analyzer (IACA) is a device based on Neural Network Technology that can measure the density of an asphalt pavement continuously in real-time during its construction. It was shown during limited field trials that the IACA could, in real-time, measure the density of an asphalt pavement during its construction with accuracy comparable to existing point-wise measurement technologies. In this paper, the validation of the performance of the IACA, i.e. accuracy of density measurements, is addressed. The results demonstrate that the IACA can be used to determine the density of the asphalt mat during compaction with an accuracy needed for quality control operations in the field.

Neural Network-based Intelligent Compaction Analyzer for Estimating Compaction Quality of Hot Asphalt Mixes

Abstract The development and validation of a tool that can estimate the level of compaction of a Hot Mix Asphalt (HMA) pavement during its construction is addressed in this paper. Densification of asphalt pavements during their construction is usually accomplished through the use of vibratory compactors. During compaction, the compactor and the asphalt mat form a coupled system whose dynamics are influenced by the changing stiffness of the mat. In this paper, it is shown that the measured vibrations of the compactor along with the process parameters such as lift thickness, mix type, mix temperature, and compaction pressure can be used to predict the density of the asphalt mat. Contrary to existing techniques in the literature where a model is developed to fit the experimental data and to predict the density of the mat, a novel neural network based approach is adopted that is model-free and uses pattern-recognition techniques to estimate the density. During compaction of a HMA mat, the neural network then classifies the observed vibrations as those corresponding to a known level of compaction. The results also show that the analyzer can estimate the density continuously, and in real-time with accuracy levels adequate for quality control in the field. Using this tool, for the first time, the overall quality of construction of a HMA pavement can be verified thereby creating the potential to improve the quality of the roads.

Residual muscle contraction and residuum socket interface force in men with transtibial osteomyoplastic amputation

ABSTRACT In the transtibial osteomyoplastic amputation (TOA) technique, the distal ends of the tibia and fibula are surgically joined to form a “bone bridge” to stabilize the bony anatomy of the distal residuum. The distal-most muscles also are secured to reestablish a length-tension relationship. Unlike conventional amputation techniques in which the muscles are not secured and do not retain length-tension relationship, the TOA procedure is anticipated to allow muscles to actively contract and retain normal physiological function. In this case series, outcomes of the TOA procedure were investigated by measuring electromyography signals from the tibialis anterior and gastrocnemius muscles in the residuum and forces at the residuum socket interface (RSI) in unilateral transtibial amputees with TOA during three types of gait activities (self-paced walking, brisk 2-minute walking, and walking over a distance of 25 ft while carrying various loads). Results confirmed the presence of loadings at the distal residuum and the activity in the residuum muscles during these gaits. Furthermore, statistical analysis showed that when the distal RSI force variation was higher, the residual tibialis anterior muscle was more active compared with its activity at lower distal RSI force variation.

Cross-sectional study of residuum measures during gait and work-related activities in men with transtibial amputation resulting from a traumatic event

ABSTRACT Most adults with transtibial amputation due to trauma (TTAT) are work-eligible yet are disproportionately unemployed. Inappropriate residuum muscle activity and load at the distal residuum-prosthetic socket interface (RSI) during prosthetic use are suggested contributors to employment-ending injury. The purposes of this study were to examine residuum muscle activity and RSI loads during self-paced gait, brisk gait, and carrying and to determine lift/carry capacities in 10 men with TTAT and 31 controls. A cross-sectional study design was used. Descriptive and bone health biomarker data were collected. During self-paced and brisk 2-minute walk tests, distances, step-length difference, and muscle activity (rectus femoris, tibialis anterior, gastrocnemius) were recorded. In participants with TTAT, RSI loads were simultaneously determined. Floor-to-knuckle lift and 25-ft carry capacity tests were conducted. Participants were similar in personal characteristics, biomarker values, self-paced/brisk walking step length differences, and distances walked. One participant with conventional TTAT and nine with osteomyoplastic TTAT demonstrated lower carrying (25.0 kg, p < 0.01) and lifting (28.0 kg, p < 0.05) capacities than controls did. In participants with TTAT, (1) all muscles tested were active during initial and terminal stance, (2) gastrocnemius activation was inverse to respective activation in intact/control limbs during self-paced and brisk walking, and (3) RSI loads were greater throughout self-paced and brisk gait. Authors caution that generalizations cannot be made because of sample size. Men with TTAT walked similarly in step length and distance but demonstrated lower lift and carry capacities than controls did. Future study may be warranted concerning rehabilitation strategies as well as muscle activation and RSI loading during gait based on surgical approach in men with TTAT.

Adaptive Dynamic Programming-Based Control of an Ankle Joint Prosthesis

The potential of an adaptive dynamic programming (ADP)-based control strategy for learning the human gait dynamics in real-time and generating control torque for a prosthetic ankle joint is investigated in this paper. This is motivated by the desire for control strategies which can adapt in real-time to any gait variations in a noisy environment while optimizing some gait related performance indices. The overall amputated leg–prosthetic foot system is represented by a link-segment model with the kinematic patterns for the model are derived from human gait data. Then a learning-based control strategy including an ADP-based controller and augmented learning rules is implemented to generate torque which drives the prosthetic ankle joint along the designed kinematic patterns. Numerical results show that with the proposed learning rules, the ADP-based controller is able to maintain stable gait with robust tracking and reduced performance indices in spite of measurement/actuator noises and variations in walking speed. Promising results achieved in this paper serve as the starting point for the development of intelligent ankle prostheses, which is a challenge due to the lack of adequate mathematical models, the variations in the gait in response to the walking terrain, sensor noises and actuator noises, and unknown intent of users.