Showing papers by "Vaughn College of Aeronautics and Technology published in 2020"

TrafficChain: A Blockchain-Based Secure and Privacy-Preserving Traffic Map

Abstract: Intelligent Connected Vehicles (ICVs) can provide smart, safe, and efficient transportation services and have attracted intensive attention recently. Obtaining timely and accurate traffic information is one of the most important problems in transportation systems, which would allow people to select fast routes and avoid congestions, thus saving their travel time on the road. Currently, the most popular ways to obtain traffic information is to inquire navigation agents, e.g., Apple map, and Google map. However, these navigation agents are essentially centralized systems, which are vulnerable to service congestions, a single point of failure, and attacks. Furthermore, users' privacy gets compromised as the agents can know their home and work addresses and hence their identities, track them in real-time, etc. In this paper, we propose TrafficChain, a secure and privacy-preserving decentralized traffic information collection system on the blockchain, by taking advantage of fog/edge computing infrastructure. In particular, we employ a two-layer blockchain architecture in TrafficChain to improve system efficiency, design a privacy-preserving scheme to protect users' identities and travel traces, and devise LSTM based deep learning mechanisms that can defend against Byzantine attacks and Sybil attacks in our system. Furthermore, an incentive mechanism is designed to motivate users to participate in the system. Simulation results show that TrafficChain works very efficiently and is resilient to both Byzantine attacks and Sybil attacks.

Study of a magnetorheological brake under compression-shear mode

Abstract: A magnetorheological (MR) brake under compression-shear mode is designed, simulated and experimentally investigated in this paper. A MR brake under compression-shear mode was first designed considering compression enhanced shear yield stress of MR fluid. Then, the operating principle of the MR brake was illustrated and mathematical torque expressions, operating under compression-shear mode assuming Herschel–Bulkley model, was further established. Moreover, simulation analysis of the designed magnetic circuit was performed as well. An experimental prototype was fabricated and tested to evaluate the transmission performance of MR brake. The results showed that the large torque could be produced at high applied currents, high compressive stress, large compressive strain and small initial gap distances. The rotational speed and compressive speed had little effect on the torque. The characteristic rising time of the torque was greatly affected by the rotational speed, the compressive strain, and compressive speed. However, the current had little effect on the rising time. The time constant would became shorter when both the rotational speed and the compressive speed were faster. Through analyzing the compression of particle chains in MR fluids directly, it was found that the diameter and the length of the particles chains brought a strong influence on the essential property of MR fluids under compression. Thus, the compressive stress or compressive strain and the initial gap distance also played an important role in enhancing the torque. The results also showed that the proposed MR brake could generate a maximum torque of 241 Nm, about 17.9 times the magnitude of braking torque without compression, and achieve a high torque density of 125.6 kN m–2 and a time constant of 58 ms. This study provides a better understanding of MR brake under compression-shear mode and the implications for many high-power applications.

A Novel Hybrid PSO-K-Means Clustering Algorithm Using Gaussian Estimation of Distribution Method and Lévy Flight

Abstract: Clustering is an important data analysis technique, which has been applied to many practical scenarios. However, many partitioning based clustering algorithms are sensitive to the initial state of cluster centroids, may get trapped in a local optimum, and have poor robustness. In recent years, particle swarm optimization (PSO) has been regarded as an effective solution to the problem. However, it has the possibility of converging to a local optimum, especially when solving complex problems. In this paper, we propose a hybrid PSO-K-means algorithm, which uses the Gaussian estimation of distribution method (GEDM) to assist PSO in updating the population information and adopts Levy flight to escape from the local optimum. The proposed algorithm is named a GEDM and Levy flight based PSO-K-means (GLPSOK) clustering algorithm. Firstly, during initialization, a few particles are initialized using the cluster centroids generated by K-means, while other particles are randomly initialized in the search space. Secondly, GEDM and PSO are selected with different probability to update the population information at different optimization stages. Thirdly, Levy flight is adopted to help the search escape from the local optimum. Finally, the greedy strategy is carried out to select the promising particles from the parents and the newly generated candidates. Experimental results on both synthetic data sets and real-world data sets show that the proposed algorithm can produce better clustering results and is more robust than existing classic or state-of-the-art clustering algorithms.

Hybrid Sliding Mode/H-Infinity Control Approach for Uncertain Flexible Manipulators

Abstract: In this article, a hybrid control approach combining sliding mode and H-infinity is proposed for an uncertain single-link flexible manipulator. The sliding mode controller stabilizes the nonlinear manipulator system, while the H-infinity controller enhances the noise rejection capability of the system by reducing the total system nonlinearity. The proposed hybrid controller is designed with the goal of rejecting external noises, hence providing a higher system performance, compared to a pure sliding mode controller. To avoid unintentional consequences of switching between the sliding mode and H-infinity controller, a fuzzy neural network weighting method is designed providing a smooth synthesis of both controller outputs. The neuro-fuzzy method applies a weighted combination of the two controller outputs to the manipulator system. In addition, a novel fuzzy estimation method is used to characterize the unstructured nonlinear disturbances in manipulator systems. The proposed hybrid control approach along with the fuzzy estimator is capable of providing a versatile means to stabilize flexible manipulator systems while maintaining a precise reference trajectory tracking in presence of unstructured uncertainty and nonlinear dynamics, as demonstrated by simulation results.

A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data.

Abstract: Human Activity Recognition (HAR) using embedded sensors in smartphones and smartwatch has gained popularity in extensive applications in health care monitoring of elderly people, security purpose, robotics, monitoring employees in the industry, and others. However, human behavior analysis using the accelerometer and gyroscope data are typically grounded on supervised classification techniques, where models are showing sub-optimal performance for qualitative and quantitative features. Considering this factor, this paper proposes an efficient and reduce dimension feature extraction model for human activity recognition. In this feature extraction technique, the Enveloped Power Spectrum (EPS) is used for extracting impulse components of the signal using frequency domain analysis which is more robust and noise insensitive. The Linear Discriminant Analysis (LDA) is used as dimensionality reduction procedure to extract the minimum number of discriminant features from envelop spectrum for human activity recognition (HAR). The extracted features are used for human activity recognition using Multi-class Support Vector Machine (MCSVM). The proposed model was evaluated by using two benchmark datasets, i.e., the UCI-HAR and DU-MD datasets. This model is compared with other state-of-the-art methods and the model is outperformed.

Thermotropic Liquid-Crystalline and Light-Emitting Properties of Bis(4-aalkoxyphenyl) Viologen Bis(triflimide) Salts

Abstract: A series of bis(4-alkoxyphenyl) viologen bis(triflimide) salts with alkoxy chains of different lengths were synthesized by the metathesis reaction of respective bis(4-alkoxyphenyl) viologen dichloride salts, which were in turn prepared from the reaction of Zincke salt with the corresponding 4-n-alkoxyanilines, with lithium triflimide in methanol. Their chemical structures were characterized by 1H and 13C nuclear magnetic resonance spectra and elemental analysis. Their thermotropic liquid-crystalline (LC) properties were examined by differential scanning calorimetry, polarizing optical microscopy, and variable temperature X-ray diffraction. Salts with short length alkoxy chains had crystal-to-liquid transitions. Salts of intermediate length alkoxy chains showed both crystal-to-smectic A (SmA) transitions, Tms, and SmA-to-isotropic transitions, Tis. Those with longer length of alkoxy chains had relatively low Tms at which they formed the SmA phases that persisted up to the decomposition at high temperatures. As expected, all of them had excellent thermal stabilities in the temperature range of 330-370 °C. Their light-emitting properties in methanol were also included.

Rate rules for hydrogen abstraction reaction kinetics of polycyclic aromatic hydrocarbons and vinyl radical

Abstract: Hydrogen abstraction reaction from polycyclic aromatic hydrocarbons (PAHs) is an important reaction class for PAHs consumption and soot formation. In this work, the reaction kinetics for a series of PAHs containing up to seven aromatic rings by vinyl radical are systematically investigated using the M06-2X/cc-pVTZ method. Based on the electronic structure calculations, the rate constants of title reactions are calculated by using transition state theory coupled with Eckart tunneling correction at the temperature range of 500–2500 K. The effects of PAH sizes, structures, and reaction sites on the rate constants are examined. The results show that the PAH sizes and reaction sites have little effect on the rate constants, while the structures of PAHs influence the rate constant significantly. Hence, the hydrogen abstraction reactions are simplified into C5 and C6 reaction classes depending on the abstraction site on the five- or six-membered ring. The simple two classes are conducive to construct the combustion model of PAHs. The reactivity with C6 class possesses the higher activity than the C5 class. Moreover, the difference in rate constants between the two classes is large at low temperatures while the two reaction classes are competitive above 1000 K. The rate rules are summarized by taking the average values of rate constants of a representative set of reactions in each class, which are applicable for the chemical model construction of PAHs.

High-Sensitivity Cuboid Interferometric Fiber-Optic Hydrophone Based on Planar Rectangular Film Sensing.

Abstract: Interferometric fiber-optic hydrophones are an important means in the field of underwater acoustic detection. The design of the hydrophone sensor head is the key technology related to its detection sensitivity. In this paper, a high-sensitivity cuboid interferometric fiber-optic hydrophone based on planar rectangular film sensing is proposed, and the sensitivity of the sensor is compared with that of the widely used air-backed mandrel hydrophone under the same conditions. The acoustic characteristic models of the two types of sensors were established by theoretical calculation and simulation analysis to obtain the theoretical pressure sensitivity. Some experiments were performed to examine the theory and design. According to the experiment results, the mean phase sensitivity of the mandrel type was −112.85 dB re 1 rad/μPa in the operating frequency range of 10–300 Hz, and that of the cuboid type was −84.50 dB re 1 rad/μPa. The latter was 28.35 dB higher than the former was. These results are useful for improving hydrophone sensitivity.

A 3D Computer Vision-Guided Robotic Companion for Non-Contact Human Assistance and Rehabilitation

Abstract: With the rapid aging of the U.S. population, the mobility impairment is becoming a more and more challenging issue that affects a large number of individuals. The research presented in this paper aims at helping the mobility-challenged individuals with a novel robotic companion, which is a walker-type mobile robot capable of accompanying the human user and keeping user at the center for protection and possible power assistance. The robotic companion is equipped with a 3D computer vision system, which provides a unique capability of sensing the human-robot relative position/orientation without physical contact or the need for wearable sensors. As such, the robotic companion enables the user to walk freely with minimum disturbance to his/her normal gait, relieving the user from the physical and cognitive loads associated with the use of traditional assistive devices. For the development of the robotic companion, the authors designed and fabricated a low-cost, differentially steered mobile robotic platform, and also developed a unique image processing system to extract the position/orientation information from the 3D camera-captured images. Furthermore, an advanced motion control system was developed for the robotic companion, which provides novel solutions to the unique challenges such as sway reduction and noise reduction in digital differentiation. To quantify the performance, component and system-level experimentation was conducted, and the results demonstrated that robotic companion and its key components function as desired and the system is expected to reduce the user load and improve the user mobility in real-world scenarios.

Numerical modelling and microstructural evolution of hybrid Ti-6Al-4V/Ti-Al-Si-Cu composite coating

Abstract: Additive manufacturing is a commercially competitive manufacturing technique with the possibility of altering the entire perception of design and fabrication It offers suitable capabilities for the building and repairing applications in the aerospace industry, which usually requires high level of accuracy and customization of parts which usually use materials known to pose difficulties in fabrication such as titanium alloys The major factors that determine the formation of the dendritic structure are the thermal gradients within the substrate during cooling and the cooling rates The rapid cooling and input of heat locally during the laser deposition process resulted in metallurgical modifications such as the formation of a complete martensitic structure, a mixture of columnar grains and layer of bands During the deposition process, the metal solidified, and the developed model enabled predictability of microstructural development and the sizes of the grain growth The 3D numerical investigation provided clarification and had substantial effects in the prediction of the overall resulting molten pool size and geometry size

Human behavior or taphonomy? On the breakage of Eastern North American Paleoindian endscrapers

Abstract: Endscrapers, the most abundant tool class at Eastern North American Paleoindian sites, are flaked stone specimens predominately used for scraping hides. They are found broken in high frequencies at these sites, a pattern that has been attributed to use. However, previous experimental and ethnographic research on endscrapers suggests that they are difficult to break. We present a series of replication experiments assessing the amount of force required for endscraper breakage, as well as the amount of force generated during human use. We also analyze which morphometric variable best predicts the breakage force. Our results demonstrate that the force humans can generate is far below the breakage force, which is best predicted by endscraper thickness. Finally, we examine an actual Paleoindian endscraper assemblage, concluding that human use was not the cause for breakage. Taphonomic factors such as plowing, or trampling, are a much better potential explanation for the high breakage frequencies present at Paleoindian sites.

Trust Aware Emergency Response for A Resilient Human-Swarm Cooperative System

Abstract: A human-swarm cooperative system, which mixes multiple robots and a human supervisor to form a heterogeneous team, is widely used for emergent scenarios such as criminal tracking in social security and victim assistance in a natural disaster These emergent scenarios require a cooperative team to quickly terminate the current task and transit the system to a new task, bringing difficulty in motion planning Moreover, due to the immediate task transitions, uncertainty from both physical systems and prior tasks is accumulated to decrease swarm performance, causing robot failures and influencing the cooperation effectiveness between the human and the robot swarm Therefore, given the quick-transition requirements and the introduced uncertainty, it is challenging for a human-swarm system to respond to emergent tasks, compared with executing normal tasks where a gradual transition between tasks is allowed Human trust reveals the behavior expectations of others and is used to adjust unsatisfactory behaviors for better cooperation Inspired by human trust, in this paper, a trust-aware reflective control (Trust-R) is developed to dynamically calibrate human-swarm cooperation Trust-R, based on a weighted mean subsequence reduced algorithm (WMSR) and human trust modeling, helps a swarm to self-reflect its performance from the perspective of human trust; then proactively correct its faulty behaviors in an early stage before a human intervenes One typical task scenario {emergency response} was designed in the real-gravity simulation environment, and a human user study with 145 volunteers was conducted Trust-R's effectiveness in correcting faulty behaviors in emergency response was validated by the improved swarm performance and increased trust scores

Multi-label Classification of Abnormalities in 12-Lead ECG Using Deep Learning

Abstract: Identifying arrhythmias from electrocardiogram(ECG) signals remains an intractable challenge. This study aims to develop an effective and non-invasive approach to realize the recognition of arrhythmias based on 12-lead ECG for the PhysioNet/Computing in Cardiology Challenge2020. To this end, we propose a deep learning-based diagnosis approach, called EASTNet which captures the characteristics of cardiac abnormalities and correlation between heartbeats sampled randomly from 12-lead ECG records by a 34-layer 1D-deep squeeze-and-excitation network. Experimenting in the multi-label arrhythmia classification task, our team, EASTBLUE, was unable to rank and score in the hidden validation and test sets, but achieved diagnostic performance with 0.7030 ± 0.0090 metric score using 5-fold cross-validation on the training set. We also investigate the effect of beat sampling on diagnostic performance, and find that the beat sampling plays a role in data augmentation that effectively alleviates network overfitting. These results demonstrate that our approach has good potential application prospects in clinical practice, especially in the auxiliary diagnosis of abnormalities.

Exact Region of Attraction Determination Based on Manifold Method

Abstract: The exact region of attraction plays an important role in autonomous nonlinear system, while the results based on the conventional method, such as Lyapunov function approach, are always conservative. However, results via the manifold method, which is the main approach studied, are exact. This method optimizes the distribution of points on the circle through modifying the end point of the former trajectory and inserting/deleting point on the circle on the basis of trajectory arc length method to improve the accuracy and efficiency. First, the basic theory of manifold method is introduced. Secondly, a methodology for determining stable manifold are proposed, which is the core of the manifold method in stability boundary determining. Finally, on this basis, three examples about academic model, power system and aviation system are taken to illustrate the advantages of the method. The results show that the method can improve the accuracy and significantly reduce the calculation time, and can be widely used in engineering systems.

User Authentication Through Pen Tablet Data Using Imputation and Flatten Function

Abstract: Identifying a user or a person through handwriting-data is a popular technique. Many researches have been done in this area most of which are image or pattern-based analysis. The accuracy level depends on the quality of the image or pattern. In this paper, we proposed user authentication system using an individual's pen tablet handwriting data. The proposed system is concerned with the numerical value of a person handwriting data getting from the digital pen and tablet device. Hence, user authentication through pen tablet data ensures more accuracy by working with user's real time handwritten data. In the proposed system, 24 persons writing samples(1262 .csv files and 23 class)are used for extracting features to identify a user based on their handwriting attributes. Six completely separated features are extracted after data analysis and pre-processing. The extracted features are mainly concerned with the vital attributes of a user's handwriting. The extracted features are used for classification. With this concern, we utilized different classification algorithms such as Support Vector Machine (SVM), Logistic Regression (LR), Linear Discriminant Analysis (LDA) and Random Forest (RF) classifier. From the implementation, different algorithms show different accuracy level. The testing accuracy rate of SVM, LR, LDA and RF is 87%, 85%, 76% and 77% respectively. The experimental analysis shows that we got more robust and satisfactory results which ensure the practicality of our system.

On improved RCM model to theart evaluation for radiation sesource

Abstract: Focusing on the deficiency of intuition, real-time and complexity of threat evaluation of radiation resource, an algorithm based on improved radar chart method (RCM) is proposed in this paper. In the algorithm proposed, coarse sorting is integrated with fine sorting to obtain a more accurate and reliable result of threat evaluation. Coarse sorting is applied to sequence all the radiation resource roughly according to radar operation mode, and reduce the task priority of low-threat radiation resource. Then, on the basis of improved RCM, fine sorting is applied to sequence the radiation resource with same radar operation mode. Finally, obtain the results of threat evaluation which combined coarse sorting with fine sorting. Simulation analysis shows the correctness and effectiveness of this algorithm. Comparing with classical method of threat evaluation of radiation resource based on RCM, the algorithm proposed in this paper is more visual in image and can work quickly with lower complexity.

Research on Interfacial Bonding Properties and Engineering Applications of Polymer Modified Mortar

Abstract: Using the slotting method to process the interface of ordinary mortar, based on ordinary mortar specimens, new and old mortar bonding specimens were prepared, and the bond strength test was carried out. The effect of interface roughness on the bonding strength of VAE rubber powder modified mortar and styrene-acrylic emulsion modified mortar was tested, and then the interfacial bonding properties of polymer modified mortar were studied. The test results show that the bonding strength of polymer-modified mortar increases with the increase of interface roughness; in engineering applications, the interface can be properly chiseled to increase the interface roughness, which is beneficial to the bonding of polymer-modified mortar firm. At the same time, the application prospects of VAE rubber powder modified mortar in the field of concrete anti-corrosion, and the application prospect of styrene-acrylic emulsion modified mortar in the repair and freeze-thaw damage of concrete structure and anti-seepage treatment are prospected.

Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming

Abstract: A mixed aerial and ground robot team, which includes both unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs), is widely used for disaster rescue, social security, precision agriculture, and military missions. However, team capability and corresponding configuration vary since robots have different motion speeds, perceiving ranges, reaching areas, and resilient capabilities to the dynamic environment. Due to heterogeneous robots inside a team and the resilient capabilities of robots, it is challenging to perform a task with an optimal balance between reasonable task allocations and maximum utilization of robot capability. To address this challenge for effective mixed ground and aerial teaming, this paper developed a novel teaming method, proficiency aware multi-agent deep reinforcement learning (Mix-RL), to guide ground and aerial cooperation by considering the best alignments between robot capabilities, task requirements, and environment conditions. Mix-RL largely exploits robot capabilities while being aware of the adaption of robot capabilities to task requirements and environment conditions. Mix-RL's effectiveness in guiding mixed teaming was validated with the task "social security for criminal vehicle tracking".

A Fault-Tolerant Control Method for Unmanned Aerial Vehicle (UAV) Formation with Topological Faults Considered

Abstract: A fault-tolerant control method for the UAV formation that has topological fault, control surface fault, actuator fault and uncertainty is proposed. Firstly, the formation motion model and the UAV motion model are established. Then based on the topological fault detection method, the topological fault reconstruction and optimization algorithm is proposed to realize the formation topological fault reconstruction and optimization with minimum communication cost and formation reconstruction cost. In designing the backstepping fault-tolerant control method, the actuator's fault identification module and the auxiliary system module are used to estimate and compensate for the faults of the actuator and the control surface respectively so as to realize the stable flight of the UAV formation under the conditions of actuator fault, control surface fault and uncertainty. The simulation results verify the superiority of the fault-tolerant control method for the UAV formation with topological faults.

Efficient Frequency Domain Feature Extraction Model using EPS and LDA for Human Activity Recognition

Abstract: Activity identification based on machine learning for human computing aims to understand or capture the state of human behavior, its environment, and integrate user by exploiting distinct types of sensors to give adjustment to the exogenous computing system. The ascent of universal computing systems requires our environment a solid requirement for novel methodologies of Human Computer Interaction (HCI). The recognition of human activities, commonly known as HAR can play a vital task in this regard. HAR has an appealing use in the health-care system and monitoring of Daily Living Activities (DLA) of elderly people by offering the input for the development of more interactive and cognitive environments. This paper is presenting a model for the recognition of Human Activities. In this proposed model, the Enveloped Power Spectrum (EPS) is used for extracting impulse components of the signal, and the Linear Discriminant Analysis (LDA) is used as a dimensionality reduction procedure to extract the discriminant features for human daily activity recognition. After completing EPS feature extraction techniques, LDA is performed on those extracted spectra for extracting features using the dimension reduction technique. Finally, the discriminant vocabulary vector is trained by the Multiclass Support Vector Machine (MCSVM) to classify human activities. For validating the proposed scheme, UCI-HAR datasets have been implemented which demonstrates higher recognition accuracy which has been acknowledged.