Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart rotor control of wind turbines under actuator limitations

As the diameters of wind turbine rotors increases, the loads across the rotors are becoming more uneven due to the inhomogeneous wind fields. Therefore more advanced sensors as well as passive and active load reduction techniques are introduced to improve the controller performance. We investigate two methods to consider the saturation and rate limits of the pitch and trailing edge flaps during individual pitch and trailing edge flap control implementation. We compare two saturation and rate limit implementations in the rotating and non-rotating frame of reference. The results show that considering the saturation in the non-rotating frame of reference leads to a more straightforward controller design and implementation.
Forwardstepping: A New Approach for Control of Dynamical Systems

In this paper, we mainly focus on developing a new paradigm motivated by investigating the consensus problem of networked Lagrangian systems with unknown time-varying delay and switching topologies. We propose a class of dynamic feedback by adding differentiators to accommodate the discontinuity and uncertainty resulting from time-varying delay and switching topologies, yielding arbitrary times differentiable reference velocities. Using these reference velocities, we develop delay/topology-independent adaptive controllers with arbitrary times differentiable control torques for networked Lagrangian systems. This specific practice motivates the formulation of a new paradigm, referred to as forwardstepping, for handling discontinuity, time-varying uncertainty, and unavailable state measurement in control systems.
Development of a Robotic Manipulator to be used in Multirotor Aerial Vehicle

This research introduces a 3 DOF manipulator which is capable of mounting on top of a multirotor aerial vehicle (MAV). It was developed by considering desirable factors like weight reduction, reachable workspace, minimizing the inertia variation and center of mass variation. Reachable workspace for the developed manipulator was identified by computer simulations. A mathematical relationship between the manipulator movements and the inertia variation was obtained. Manipulator movements deviate the initial center of mass of the aerial manipulator which will differ the dynamics of the overall multirotor manipulation system. Therefore, the relationship for the manipulator center of mass variation was obtained with respect to a given general cubic polynomial trajectory. Hence, designing a controller for the overall aerial manipulation can be done while considering the center of mass and inertia variation of the manipulator. This approach will be more effective in disturbance compensation of the aerial vehicle.
The Effect of Spatial Consistence on Character Recognition of Brain-Computer Interface

Brain-computer interface technology has broad application prospect in the field of elderly care, disabled assistance, rehabilitation, hazardous environment operation, military and so on. However, the brain-computer interface technology is unable to meet the needs of practical application because of low accuracy and information transfer rate. We investigate the neural mechanisms of higher cognition of simultaneous audiovisual stimuli and ascertain neural mechanisms of modulation of spatial consistency on the processing of audiovisual information. Visual stimulus was English alphabetical and auditory stimulus was alphabetical pronunciation. Stimulus were presented in consistent and inconsistent positions. Through analysis of experimental results, it was found that the spatial consistency of stimulation affected the event-related potential component. The N1 and P2 components induced by inconsistent stimulation are significantly smaller than the consistent stimulus, but the inconsistent stimulus induces a larger P300 component. This result can improve the experimental paradigm of BCI character recognition system.
Exploring the Use of Augmented Reality Interfaces for Driver Assistance in Short-Notice Takeovers

With conditionally automated vehicles (Level 3), drivers are still required to be ready to intervene upon a takeover request (TOR) and face the difficulty of achieving their optimal performance level directly after a passive phase. In this work, we examine the effects of using an augmented reality (AR) interface with world-registered visualizations to assist drivers in the last moments before a takeover and in the first seconds of controlling the vehicle. We focus on urban situations with an unplanned, short-notice TOR and created three distinct example scenarios in a mixed-reality driving simulation. We present a prototype of an AR assistance system realized on a simulated windshield display (WSD). In a user study, we compare the AR system to a conventional head-down display (HDD) and present results on driving performance, driver workload and usability. The AR assistant enabled higher lateral performance and reduced workload in situations where steering is required directly after takeover but prolonged reaction time when only fast longitudinal input was required. The AR interface performed better than the HDD in most user-centered aspects including comfort of use and helpfulness.
End-to-End Deep Learning Applied in Autonomous Navigation using Multi-Cameras System with RGB and Depth Images

The present work demonstrates how an autonomous navigation system of ‘End-to-End’ deep learning principles is directly improved in its response process, depending on the information obtained by different input images configurations. For this, a methodology was developed to allow working with RGB and depth images, which were obtained through a Microsoft Kinect V2 sensor device. Three cameras were used for this experiment. The images of the different cameras were concatenated or grouped, generating new and different input configurations from the vision system. To develop the presented methodology, two support and validation systems were implemented. Through the process of computer simulation, it was able to test the first approaches and define the most important ones. In order to validate the proposed methodology and solutions in real world situations, a 1/4 scale automotive vehicle was prototyped. Finally, the experiments shows the importance of the use of multi-cameras systems for a better performance of autonomous navigation systems based on End-to-End learning approach, heaving an average error of 2.41 degrees in the best configuration tested, with three RGB cameras.
Development of Graphene Oxide Based Capacitive Gas Sensor for Selective Detection of NO2

NO2 gas is a harmful vapor that, when present in large quantities, can impose severe threats on health and environment. Gas sensors for NO2 have been in development over the past few decades albeit selectivity towards the gas still remains somewhat unperfected. A method was suggested to improve selectivity towards NO2 by registering a unique and recurring feature of the signal behavior as the selection criterion i.e. gradient of sensor response. A parallel plate capacitive gas sensor was fabricated taking graphene oxide as the dielectric medium and was tested for NO2. Graphene oxide was synthesized adhering to the Improved Hummer’s Method (Tours Method) which produced sufficiently oxidized graphene with a yield of 193% with respect to the starting material. Synthesized graphene oxide was characterized by Fourier Transform Infrared Spectroscopy (FTIR), Thermo Gravimetric Analysis (TGA) and X-ray Diffractometry (XRD) techniques ascertaining that the products were well oxidized with acceptable d-spacing values. Sensor assembly was tested for capacitance variation in the frequency domain, time domain and under the influence of constant temperature ramp. The results showed an average response time of about 1 minute to reach the steady state signal and an equal time to go back to the initial reference signal level once the target gas was evacuated.
A decision-making architecture for automated driving without detailed prior maps

Autonomous driving requires general methods to generalize unpredictable situations and reason in complex scenarios where safety is critical and the vehicle must react in a reliable manner. In this sense, digital maps are a crucial component for relating the location of the vehicle and identifying the different road features. In this work, we present a decision-making architecture which does not require detailed prior maps. Instead, OSM is used to plan a global route and an automatically generate driving corridors, which are adapted using a proposed vision-based algorithm. Moreover, a grid-based approach is also applied to consider the localization uncertainty. Those self-generated driving corridors are used by the local planner to plan the trajectories the vehicle will follow. Our approach integrates global, local and HMI components to provide the required functionalities for autonomous driving in a general manner.
P300 Detection with Adaptive Filtering and EEG Spectrogram Graph

A new method of P300 potential detection is given in the paper. Firstly, by creating the signal differential profile, a single electrode choose method of P300 is given. Secondly, aiming at the shortcoming of traditional smoothing method, adaptive filtering method is applied to the signal preprocessing of P300 potential for the first time in this paper. This method takes the average signal of the original data as the reference signal and can effectively eliminate all kinds of noise components mixed in the P300 potential. Furthermore, different from traditional time-domain feature extraction method of P300 potential, the paper proposes a feature extraction method based on short-time Fourier transform, which fully extracts the frequency-domain features of P300 potential while retaining the time-domain features. Finally, a convolutional neural network classifier is designed and obtains an average classification accuracy of 89.81% in the different average times of reference signals which was about 5% higher than traditional methods.
High-Definition Map Combined Local Motion Planning and Obstacle Avoidance for Autonomous Driving

Local motion planning plays an important role in an autonomous driving system. And applying mature local motion planning methods to real traffic scenarios with regular constraints is one of the keys to the applications of autonomous vehicles. In this paper, we present a local motion planning method combined with High-Definition (HD) maps. Through the HD map defined by OpenStreetMap, the local motion planner can obtain the prior knowledge of traffic scenarios and achieve path planning and optimization accordingly. In order to improve the safety and comfort of the obstacle avoiding process, we also propose an inertia-like path selection algorithm based on this planning method. We evaluated the proposed method on our designed autonomous driving experimental platform ‘Pioneer’ and participated in the 2018 Intelligent Vehicles Future Challenge. In the competition, the ‘Pioneer’ successfully completed all the races and won the championship without any manual intervention.
Interference Management in Cellular Full-Duplex MIMO Systems with Statistical CSI

The weighted sum-rate problem for a cellular MIMO network with a full-duplex base station and half-duplex user devices is considered. The system is subject to interference from uplink to downlink users, rendering the optimization problem non-convex. We use interference shaping, which defines sets of admissible interference covariance matrices to separate uplink and downlink into independent sub-problems. While the uplink covariances are optimized to comply with these constraints, a worst case interference optimization has to be performed in the downlink. To limit the channel estimation overhead, it is assumed that only statistical channel state information is available for the interference channels and we propose a scheme with which the uplink can still comply with the shaping sets. For the downlink, we show that the worst case interference problem can be recast into a single convex optimization problem.
Rotation Invariance-Based HOG for Multi-posture Human Detection

The technology of human detection is evolving rapidly and it has been applied in various fields. Histograms of oriented gradients (HOG) are proven to be able to significantly outperform existing feature sets for human detection. This paper presents a foreground-based scalloped (FSR)-HOG features with rotation invariance for human detection. Firstly, extracting the FSRHOG human features on the foreground image, then training the positive and negative samples by SVM linear classifier. Experimental results show that the FSRHOG feature vector can retain the accuracy of HOG as well as improve the detection rate, and it is suitable for human detection with high motion complexity.
Earth Networks Lightning Network and Dangerous Thunderstorm Alerts

The Earth Networks Total Lightning Network (ENTLN) incorporates advanced lightning location technology delivering competitive lightning detection efficiency and location accuracy. ENTLN can detect both in-cloud (IC) and cloud-to-ground (CG) lightning. It consists of over 1600 wideband sensors deployed in 40+ countries to detect lightning and generate faster-localized storm alerts. Since its initial deployment, several improvements were made over the years to enhance its performance and usability. Notable ones are the addition of many new sensors each year to improve detection efficiency and extend lightning detection coverage to any regions of the world. Firmware improvements have also been made to further increase sensitivity, and the implementation of a multi-parameter classification algorithm allows IC and CG pulses to be distinguished with very good accuracy. A performance study was conducted to evaluate the characteristics of the ENTLN. The results of the study show that the detection efficiency and the classification accuracy of the new ENTLN processor have greatly improved. ENTLN data is used to track the properties of storms cells, such as the lightning flash rate, cell direction, and speed as a basis for issuing alerts for thunderstorms that have the potential for severe weather. Most severe convective storms can generate high IC flash rate and high IC/CG flash-rate ratios. When rates exceed critical thresholds, Dangerous Thunderstorm Alerts (DTAs) are issued. The system helps in predicting the storm dangers by generating a DTA with significant lead time before the ground-level severe weather. Through the life of the DTA warned storm, the storm cells are monitored continuously, which enhances the situational awareness. In this study, we analyze several severe weather events to estimate the effectiveness of DTAs as well as implications for improving public safety.
A Mathematical Model of the Pneumatic Force Sensor for Robot-assisted Surgery

Restoring the sense of touch in robotic surgery is an emerging need several researchers tried to address. In this paper, we focused on the slave side proposing a pneumatic sensor to estimate contact forces occurring during the interaction between surgical instruments and anatomical areas. It consists of a tiny pneumatic balloon, which, after being inflated, appears near the tip of the instrument during the measurement phase only. This paper presents a mathematical method relating the intensity of the contact force to the variation of pressure inside the balloon. The latter was modeled as a spherical elastic membrane, whose behavior during contact was characterized taking into account both the deformation of the membrane and the compression of the contained gas. Geometrical considerations combined with an energetic approach allowed us to compute the force of interest. The effectiveness of our sensing device has been confirmed by experimental results, based on comparison with a high-performance commercial force sensor.
LearnAFL: Greybox Fuzzing With Knowledge Enhancement

Mutation-based greybox fuzzing is a highly effective and widely used technique to find bugs in software. Provided initial seeds, fuzzers continuously generate test cases to test the software by mutating a seed input. However, the majority of them are “invalid” because the mutation may destroy the format of the seeds. In this paper, we present a knowledge-learn evolutionary fuzzer based on AFL, which is called LearnAFL. LearnAFL does not require any prior knowledge of the application or input format. Based on our format generation theory, LearnAFL can learn partial format knowledge of some paths by analyzing the test cases that exercise the paths. Then LearnAFL uses these format information to mutate the seeds, which is efficient to explore deeper paths and reduce the test cases exercising high-frequency paths than AFL. We compared LearnAFL with AFL and some other state-of-the-art fuzzers on ten real-world programs. The result showed that LearnAFL could reach branch coverage 120% and 110% of that of AFL and FairFuzz, respectively. LearnAFL also found 8 unknown vulnerabilities in GNU Binutils, Libpng and Gif2png, all of which have been reported to the vendors. Besides, we compared the format information learned from the initial seed of an ELF file with a format standard of ELF files. The result showed that LearnAFL learns about 64% part of the file format without any prior knowledge.
Cyber Resilience in IoT Network: Methodology and Example of Assessment through Epidemic Spreading Approach

Cyber Resilience is an important property of complex systems and is important consideration in developing specific IoT applications. This work aims at introducing a novel approach to assess IoT resilience adopting the risk perception in network-based epidemic spreading approach. In particular IoT has been considered a network of devices where the probability of infection and interactions (communication), needs to be balanced in order to reduce the malware outbreack while maintaining the network functionalities at an acceptable level. The mathematical model and the simulation results reveal the benefit of a shift from a risk-based to a resilience based approach to threat management in IoT.
Study on Motion Recognition for a Hand Rehabilitation Robot Based on sEMG Signals

Robot-assisted self-rehabilitation training can overcome the shortcomings of clinical rehabilitation, which is significant for stroke patients. This paper proposed a hand robot rehabilitation training system based on surface electromyography (sEMG) signals. This study combines pattern recognition technology and biomedical technology to collect the sEMG signals of the daily movements of the stroke patients, and then pre-processes signals, feature extraction and pattern recognition classification, and recognizes the movement intention of the patients. Through the hand rehabilitation robot to help the affected side of the hand for rehabilitation training. In the online self-rehabilitation training, a certain motion mode performed by the contralateral side of the patient is obtained according to the pattern recognition offline trained classifier to obtain the classification result, and the motor-controlled hand robot is driven to perform the corresponding motion to realize the self-rehabilitation training. Finally, simulation experiments and volunteer rehabilitation training experiments were carried out. The experimental results show that the recognition rate of pattern recognition reaches 93.70%±2.22%. Compared with BP neural network in other literatures, our proposed wavelet neural network is better than 8% in the accuracy of recognition classification. This system we designed can effectively help patients with self-rehabilitation training.
Outcome-Oriented Predictive Process Monitoring with Attention-Based Bidirectional LSTM Neural Networks

Outcome-oriented predictive process monitoring plays an important role for business process management, which is to predict the most likely outcome class (positive or negative) for an on-going case given the event log consisting of completed business process instances with labeled outcome classes. Unfortunately, the existing traditional machine learning methods for this problem require a lot of manual intervention and long time in real-time prediction applications. In order to construct a predictive classification model automatically, we propose an effective approach based on deep learning techniques, called Att-Bi-LSTM, which is combined with the bidirectional long short-term memory network and the attention-mechanism. This approach can capture and optimize the features that have a decisive effect on the outcome for a completed case so as to construct a predictive model with good performance. The extensive experiments conducted on twelve real datasets show that our approach outperforms the state-of-the-art ones in terms of prediction accuracy, earliness, and time performance.
Implementing Deep Neural Network for Signal Transmission Distortion Mitigation of PAM-4 Generated by Silicon Mach-Zehnder Modulator

We propose and demonstrate a distortion mitigation of 50-Gbitls PAM-4 signal generated by a silicon-based Mach-Zehnder-modulator (SiMZM) after 20-km single-mode-fiber transmission via deep-neural-network (DNN). The implementation of a 2-layer DNN is discussed.
End-Effector Force Estimation for Robotic Manipulators from Motor Current Measurements

End-effector force of the robotic manipulator plays an important role in force control. Generally, conventional end-effector force measurement approaches need additional sensors, e.g., the force/torque (F/T) sensor and the joint torque sensor, which raise the cost and introduce complexity into manipulators’ mechanical and electrical design. Therefore, we propose an approach to estimate the end-effector contact force of a manipulator utilizing the motor current measurement without any additional sensors. By building the dynamics model of the manipulator with external wrench, the proposed approach utilizes the friction torque model of the joints to improve the precise of the wrench estimation. Additionally, In order to identify the parameters of motors and the friction torque model, a parameter identification method based on non-linear least squares regression is introduced. The experiments are implemented on the robot IRIC-I which is developed in our lab. Experimental results indicate that the end-effector force can be reliably estimated without any extra sensors, and show the potential of utilizing the estimated end-effector force in force control of the robotic manipulator.