Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics of nonlinearly damped microcantilevers under electrostatic excitation

Nonlinear dynamic behaviour of a cantilever microbeam actuated by a combination of DC and AC loading are investigated in presence of squeeze-film damping. A reduced order model formulated accounting for the nonlinearities of the system arising out of electrostatic forces and squeeze-film damping is numerically simulated to observe the large amplitude dynamic characteristics near primary and superharmonic resonances. The emphasis is on the significance of nonlinear damping in capturing the true dynamic characteristics of microsystems formulated as distributed parameter model. The damping nonlinearity is found to considerably affect the dynamics with a profound stabilising effect on the microsystem. Under the effect of large DC bias voltage, frequency–response curves obtained for different amplitudes of AC excitation exhibit local and global bifurcations. Response sensitivity to initial conditions is investigated near bifurcation points. Findings in the superharmonic resonance domain are emphasised.
Computational Method of Optimal Kick-pass Plays Considering Run Plays after Catches in Seven-a-side Rugby

Recently, in the broadcasting of rugby, it has become increasingly important to provide spectators and viewers explanatory information for the easy understanding of rules, plays, tactics, and game development. In this context, a computational method of optimal attack plays up to a try was developed on the basis of players’ and ball location data of seven-a-side rugby. However, this method cannot treat attack plays involving kick-pass plays. Therefore, in this study, we propose a computational method of an optimal kick-pass play based on the players’ location data in seven-a-side rugby. In the proposed method, a kick-pass simulation model with random elements is utilized to simulate the irregular motions of rugby balls at their bounces. The proposed method also calculates the (time-varying) catchable space for each player based on the player motion model to determine the catcher in the simulated kick-pass play. In addition, the proposed method uses a run play simulation model to evaluate the run play of the most probable ally catcher. Employing such simulation models, the proposed method enables us to compute an optimal kick-pass play among multiple kick-pass plays. For an initial validation, the proposed method is applied to several synthetic players’ location data.
Geometry-Based Grasping Pipeline for Bi-Modal Pick and Place

We propose an autonomous grasping pipeline that relies on geometric information extracted from segmented point cloud data. This is in contrast to many recent approaches leveraging deep learning and thus relying on a rather large amount of training samples. We argue that the proposed geometric approach facilitates task-level planning as the shape, size, and symmetry of objects can be directly taken into account during the planning process that utilizes the new MoveIt! Task Constructor (MTC) framework to define and plan action sequences composed of several inter-related sub-tasks. The efficiency of the proposed grasping pipeline is illustrated in pick-and-place scenarios, including a long-distance pick-and-place requiring a hand-over between two hands.
Single-Chained Fragment Variable (scFv) Recombinant as a Potential Receptor for SARS-CoV-2 Biosensor Based on Surface Plasmon Resonance (SPR)

Detection of SASR-CoV-2 plays a significant role in reducing the transmission of COVID-19. Antigen swab test is widely used for screening due to its low processing time and cost, while RT-PCR is used in patient monitoring since it is quite expensive. Although the antigen swab test is more affordable than the RT-PCR, it only generates a discrete result: positive or negative. Thus, it cannot be used for patient monitoring. A method using antigen-antibody binding and surface plasmon resonance (SPR) principle was developed in this research to create an affordable, instant, and quantified SARS-CoV-2 detection method. In this study, modified scFv is tested as a potential bioreceptor since it is easier to be expressed than the whole antibody. The results show that the scFv with the best potential was harvested from the periplasm of E. coli and purified. It has a maximum response at 8.02 RU, LOD at 8.34 ng/mL, linearity at 1.38 in the range of 25-200 ng/mL, and a determination coefficient at 92 percent.
Direct extraction of nonlinear FET Q-V functions from time domain large signal measurements

The authors present a simple and accurate procedure for the direct extraction of the quasi-static FET model nonlinear charge functions from large signal measurements. The method is based on the proper use of a vector nonlinear network analyzer (VNNA) with load-pull facilities. To our knowledge, these results show for the first time a direct procedure to extract the nonlinear charges of a FET using a very reduced number of large signal measurements.
Toward Gait Symmetry Enhancement via a Cable-Driven Exoskeleton Powered by Series Elastic Actuators

Gait rehabilitation is essential for chronic stroke patients to regain independent walking ability and quality of life, of which symmetry is regarded as a gold standard of gait quality. In this letter, a cable-driven lower limb exoskeleton powered by series elastic actuators (SEAs) has been developed, and a generalized gait-phase-based assistive strategy has been presented to enhance the gait symmetry of chronic stroke patients in the future. This strategy features a parameterized assistive force generation method that allows individuals customization to achieve more synchronized assistance with their walking gait patterns. A compliant human-robot interaction is guaranteed by SEA, and the exoskeleton exhibits low passive impedance and good transparency. The proposed assistive strategy was evaluated with five able-bodied subjects walking with the assistance from exoskeleton while adding artificial impairments to mimic a pathological gait with deficits (i.e., reduced knee flexion and foot-drop). Experimental results proved its efficacy in enhancing temporal walking gait symmetry to the levels comparable to participants’ normal gait.
PCTMA-Net: Point Cloud Transformer with Morphing Atlas-based Point Generation Network for Dense Point Cloud Completion

Inferring a complete 3D geometry given an in-complete point cloud is essential in many vision and robotics applications. Previous work mainly relies on a global feature extracted by a Multi-layer Perceptron (MLP) for predicting the shape geometry. This suffers from a loss of structural details, as its point generator fails to capture the detailed topology and structure of point clouds using only the global features. The irregular nature of point clouds makes this task more challenging. This paper presents a novel method for shape completion to address this problem. The Transformer structure is currently a standard approach for natural language processing tasks and its inherent nature of permutation invariance makes it well suited for learning point clouds. Furthermore, the Transformer’s attention mechanism can effectively capture the local context within a point cloud and efficiently exploit its incomplete local structure details. A morphing-atlas-based point generation network further fully utilizes the extracted point Transformer feature to predict the missing region using charts defined on the shape. Shape completion is achieved via the concatenation of all predicting charts on the surface. Extensive experiments on the Completion3D and KITTI data sets demonstrate that the proposed PCTMA-Net outperforms the state-of-the-art shape completion approaches and has a 10% relative improvement over the next best-performing method.
The development of design ideas in the early apparel design process: a pilot study

The purpose of this study is to examine the apparel design process by asking how a designer applies perceived information from visual sources and develops concepts through the exploration of design elements and principles. A stimuli-based design experiment was conducted with a professional designer as a pilot study. It was observed that preliminary visual units, termed small concepts, gradually evolve during the early design stage. Since this process is critical to continue the entire idea development, this paper focuses primarily on the detailed observation of the early design process, in which the designer perceives visual elements from a source-of-inspiration image, immediately transfers them to clothing design-related elements, explores variations in shape and placement by applying design principles, and finally develops small concepts. These small concepts become the basis for the later development of more complex design ideas. Understanding this design knowledge and relevant strategies will trigger creative idea generation and shed light on teaching the design process to apparel design students.
A High-accuracy Framework for Vehicle Dynamic Modeling in Autonomous Driving

Vehicle dynamic models are the key to bridge the gap between simulation and real road test in autonomous driving. An accurate vehicle model allows control algorithms in simulation being transferred to real road test with same quality. In this paper, we present a dynamic model residual correction framework (DRF) for vehicle dynamic modeling. DRF provides a general accuracy improvement framework on existing vehicle dynamic models. On top of any existing open-loop dynamic model, this framework builds a Residual Correction Model (RCM) by integrating deep Neural Networks (NN) with Stochastic Variational Gaussian Process (SVGP) model. RCM takes a sequence of vehicle control commands and dynamic states for a certain time duration as modeling inputs, extracts underlying context from this sequence with deep encoder networks, and predicts open-loop dynamic model prediction errors. Five vehicle dynamic models are derived from DRF via encoder variations. Our contribution is consolidated with evaluation of the absolute trajectory error and the similarity between DRF outputs and the ground truth. Compared to classic rule-based and learning-based vehicle dynamic models, DRF accomplishes as high as 74.12% to 85.02% of the absolute trajectory error drop among all DRF variations.
Affect-driven Robot Behavior Learning System using EEG Signals for Less Negative Feelings and More Positive Outcomes

Learning from human feedback using event-related electroencephalography (EEG) signals has attracted extensive attention recently owing to their intuitive communication ability by decoding user intentions. However, this approach requires users to perform specified tasks and their success or failure. In addition, the amount of attention needed for decision-making increases with the task difficulty, decreasing human feedback quality over time because of fatigue. Consequently, this can reduce the interaction quality and can even cause interaction breakdowns. To overcome these limitations and enable the interaction of robots with higher complexity tasks, we propose a closed-loop control system that learns affective responses to robot behaviors and provides natural feedback to optimize robot parameters for smoothing the next action. Experimental results demonstrate our affect-driven closed-loop control system yielded better affective outcomes and task performance than an open-loop system with correlated neuroscientific characteristics of EEG signals, thus enhancing the quality of human-robot interaction.
Meta-Learning for Emotion Prediction from EEG while Listening to Music

We are studying to realize an emotion induction system that generates music based on emotions predicted in real-time from electroencephalogram (EEG). Since there are individual differences in EEG while listening to music, a model trained from a single participant’s data is expected to provide highly accurate emotion prediction. However, a time-consuming EEG recording is required to avoid data shortage. We need to reduce the recording time to minimize the burden on the participants. Therefore, we train a model which considers the individuality of EEG from multiple participants’ data and fine-tune it from a small amount of a single target’s data. In this paper, we propose a method using meta-learning for pre-training. We compared three methods: two methods using multiple participants’ data (with/without meta-learning) and a method using a single participant’s data. Our proposed method obtained the lowest RMSE (valence: 0.244 and arousal: 0.287). We demonstrate the effectiveness to use meta-learning to train an emotion prediction model, which is a necessary step for constructing the emotion induction system.
Temporal Attentive Adversarial Domain Adaption for Cross Cultural Affect Recognition

Continuous affect recognition is becoming an increasingly attractive research topic, recent works mainly focus on modeling the temporal dependency and multi-modal fusion to boost the performance. Despite recent improvement, the cross-cultural affect recognition in videos is still not well-explored. In this paper, we propose the temporal attentive adversarial domain adaption for cross cultural affect recognition. The LSTM is firstly used to encode the contextual representation for each frame. Then, a DNN based regressor is used to estimate the affective dimension arousal or valence, and optimized to promote the encoded representation is emotion discriminative. In addition, another DNN based sequence level culture classifier, which takes the fused representation of each frame as the input, is used to recognize the culture of the input sequence, and optimized to encourage the encoded representation is culture invariant. Since different frames over a video may contribute not equally in recognizing the culture, we propose to add another frame level culture classifier, which could adaptively and attentively assign more weighting scores for the important frames for recognizing the culture. The proposed method is evaluated on the benchmark dataset AVEC2019 CES. Our experimental results show that the proposed method improves the performance compared to state-of-the-art methods, with the concordance correlation coefficient (CCC) reaching 0.576 for arousal and 0.472 for valence, on the cross cultural test set.
PWM-Induced Losses in Electrical Machines: An Impedance-Based Estimation Method

The continuous development of electrical machines and power electronic converters is increasingly demanding for higher efficiency targets, especially in those application fields where a limited amount of stored energy is available (e.g., electric mobility). Notably, in modern inverter-fed variable speed drives (VSDs) and integrated on-board battery chargers (iOBCs), the electrical machine losses induced by pulse-width modulation (PWM) can represent a relevant component of the overall system loss and are gathering increasing attention from both industry and academia. In this context, a novel approach to estimate the PWM-induced losses in electrical machines is proposed here, exploiting a straightforward impedance measurement in the frequency domain. The key features of the proposed methodology are its simple implementation and its wide applicability, being unaffected by the machine kind (i.e., induction and synchronous machines) and/or design characteristics. In essence, the machine phase impedance knowledge allows to derive a simple and accurate high-frequency machine loss model, commonly known as machine loss factor. This model can then be exploited to estimate the machine harmonic losses induced by a generic voltage excitation waveform.
Trust your supervisor: quadrotor obstacle avoidance using controlled invariant sets

Supervision of a nominal controller, to enforce safety, is concerned with appropriately modifying the generated control inputs, if needed, in order to keep a control system within a set of safe states. An integral component in supervision is a controlled invariant set contained in the set of safe states. In this paper, we build on recent results on the computation of polytopic controlled invariant sets to present a supervision framework that computes the corrected inputs analytically and, hence, suitable for real-time control. The framework is validated on the task of quadrotor obstacle avoidance by forcing the vehicle to navigate within controlled invariant sets of the obstacle-free space. The results are experimentally demonstrated on a Crazyflie 2.0 quadrotor.
Privacy Protection Algorithms for Smart Meters Based on Cluster Analysis

Smart meters can measure high-precision electricity consumption data of users. The wide use of smart meters may cause users to worry about their privacy disclosure. This paper proposes new algorithms based on setting different strategies to control the battery. The idea of this paper is clustering historical household power load data and designing strategies to protect users' privacy. In order to prove the advance of our algorithms, we compare our algorithms with the previous best-effort and stepping framework algorithms. Through comparison, we can conclude that our algorithms have smaller mutual information, the privacy protection effect of our algorithms is better. At the end of the paper, we also research the performance differences of different algorithms under different battery capacity.
A Review of Recent Trends and Industry Prospects for Artificial Intelligence Technologies

Recent years have witnessed various breakthroughs and advancements in research and application of Artificial Intelligence (AI) technologies. Notably, AI is leading the 4th global industrial revolution (Industry 4.0). In fact, deep learning rose to become one of the most highly researched aspects in various industrial sectors worldwide, making it especially important to timely review the recent progress and industry prospects of AI. In this paper, we briefly review the recent advances in AI technologies, discuss the various advantages and challenges they bring, and finally, offer a few points of thought regarding the future developments in the industry.
Design of matching transformer for high-power transmitter of active sonar considering transformer ripple temperature

Active SONAR (SOund Navigation And Ranging) transducers for underwater communications and detections have both transmission and reception modes. In real operation, its transmitter in the reception mode does not transfer the power to transducer while it does in the transmission mode. As a result, the matching transformer for the transmitter has significant amount of power loss or almost no loss in case of transmission mode or reception mode, respectively. The matching transformer should be designed based on the power loss due to the operating duty and resultant maximum temperature of the transmission mode and reception mode. This paper proposes a new matching transformer design method that considers the transformer ripple temperature by calculating the maximum temperature of the transformer in the transmission mode.
Selective re‐meshing: A new approach to include mode stirring effects in the steady state FDTD simulation of microwave heating cavities

A new selective remeshing approach to efficiently utilize the finite‐difference time‐domain (FDTD) method for simulating 3D microwave heating cavities with rotating mode stirrers is presented here. The proposed scheme is implemented in a ∼300L multimode applicator and the result compares favorably with a validation experiment. The proposed implementation of the selective remeshing scheme is efficient, in that the remeshing is done “on the fly” and the previous FDTD solution is used to continue the simulation as a single run of the FDTD solver. © 2006 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 1179–1182, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21572
"Safe Skin" - A Low-Cost Capacitive Proximity-Force-Fusion Sensor for Safety in Robots

This paper presents the design and evaluation of the low-cost capacitive proximity-force-fusion sensor "safe skin", which can measure simultaneously the proximity of humans as well as the contact force. It was designed such that the force and proximity sensing functions can work concurrently without interfering with each other. Moreover, active shielding, on-chip digitization and ground isolation are implemented for the sensor to minimize the influence from stray capacitance and electromagnetic interference (EMI) from the environment, which ensures that the sensor has a high system robustness for industrial applications. The prototype version has the capability of detecting a grounded human hand sized object from a distance of 400 mm. Moreover, forces in the range of 5 to 40 N can be measured, with 43.7% hysteresis and 6.7% nonlinearity. Due to its sensing characteristics, when used on a robot, the sensor could be used to ensure the safety of nearby humans in the future. The sensor could also potentially be used as an interface for human-robot interaction (HRI).
Variable Stiffness Folding Joints for Haptic Feedback

Origami robots composed of rigid parts with flexible joints have inherent compliance that enables deployment and reconfiguration for various shape adaptations. The major drawback of such mechanical compliance is its intrinsic softness and lack of controllability of this stiffness. In this work, we propose a design of variable stiffness origami joints to be integrated into large scale origami systems with an inner controllable joint stiffness. This novel way of controlling compliance in origami structures demonstrates that the embedded variable stiffness joint in the prototype can provide rich haptic feedback to the user through its compact, collapsible interactive platform.
Application and evaluation of Machine Learning for news article popularity prediction

The internet is increasingly becoming the primary source of news worldwide. Social networking sites have further enabled instantaneous spread of such articles by often allowing single-click user sharing. Majority of the organizations publishing such articles drive revenue through advertisements which is ultimately dependent on the popularity of the article. This popularity is mainly defined in terms of views and shares. One of the emerging applications of Machine Learning is to help organizations predict which articles are most likely to become popular and thus allow them to improve targeted advertising campaigns in order to optimize revenue. This paper proposes and evaluates Machine Learning based approaches alongside Rolling, Growing and a Hybrid training window techniques in order to predict the popularity of news articles.
Design rated oriented strandboard in CSA standards

The 1994 edition of CSA (Canadian Standards Association) Standard 086.1 "Engineering design in wood" includes, for the first time, specified strength and stiffness for design rated oriented strandboard (OSB). Design rated OSB panels are manufactured in accordance with the new CSA Standard 0452. In this paper, the results of the short-term (1–10 min) test data which led to the establishment of the three rating grades of Type 1 (standard) design rated OSB are presented. In addition, factors that may affect the classification such as panel thickness and orientation are discussed. Key words: OSB, waferboard, structural panels, wood.