Miracle-dz/UniFAR-FTUs huggingface dataset:
LQR-Assisted Whole-Body Control of a Wheeled Bipedal Robot With Kinematic Loops

We present a hierarchical whole-body controller leveraging the full rigid body dynamics of the wheeled bipedal robot Ascento. We derive closed-form expressions for the dynamics of its kinematic loops in a way that readily generalizes to more complex systems. The rolling constraint is incorporated using a compact analytic solution based on rotation matrices. The non-minimum phase balancing dynamics are accounted for by including a linear-quadratic regulator as a motion task. Robustness when driving curves is increased by regulating the lean angle as a function of the zero-moment point. The proposed controller is computationally lightweight and significantly extends the rough-terrain capabilities and robustness of the system, as we demonstrate in several experiments.
Benefits of Dynamic Line Rating for the Russian Power Corridor between the European and Siberian Zones

Dynamic capacity rating technologies of power lines has been growing in the electric power industry since 1977. Yet, its use is absent in Russia. In this study, a methodology for calculating the dynamic line rating is presented. The economic benefits related to the use of a dynamic line rating instead of the conventional static line rating is assessed for simple-but-illustrative two-zonal network representing Russia power corridor that connects Siberian and European zones. Our simulations show that the use of dynamic line rating, in comparison with a static one, results in economic savings for all the months of the year, reaching as high as 8%.
The Analysis of Key Technologies for Advanced Intelligent Quadruped Robots

As Boston Dynamics announces more and more technical details of its quadruped robots to the world, and ETH successfully applies artificial intelligence to ANYmal, the quadruped robot has attracted more and more attention of the researchers on robot and artificial intelligence all over the world. Taken the famous quadruped robots in the world as the research objects, this paper analyzes the key technologies of advanced intelligent quadruped robots. The integration of function and structure is introduced for the dexterous mechanism, the typical gaits and jumping motion is introduced for the advanced mobile ability, perceptual and navigation is introduced for the autonomous walking. By analyzing the key technologies of advanced intelligent quadruped robots, the paper could provide abundant theoretical reference for the technical development of the legged robot.
Assessing Mental Stress Based on Smartphone Sensing Data: An Empirical Study

Mental stress is a critical factor affecting one's physical and mental well-being. At the early stage, the effect of stress is often underestimated, while it usually leads to serious issue Lateran. Therefore, it is crucial to detect stress before it evolves into severe problems. Traditional stress detection methods are based on either questionnaires or professional devices, which are time-consuming, costly and intrusive. With the popularity of smartphones embedded with a rich set of sensors, which can capture people's context, such as movement, sound, location and so on, it is an alternative way to access people's behavior by smartphones. Through an empirical study, this paper proposes an automatic and non-intrusive stress detection framework based on smartphone sensing data. First, we construct various discriminative features from multi-modality phone sensing data, in which both absolute and relative features are considered to make the model more personalized. Then, to tackle the challenge of label insufficiency, we further develop a co-training based method for stress level classification. Finally, we evaluate our model based on an open dataset, and the experimental results verify its advantages over other baselines.
An Introduction to Marching Memory (MM)

Today's computer systems rather have a “big issue,” which is what is called the memory bottleneck, especially as their scale has been growing with nanotechnology year by year toward EXA-scale supercomputing for scientific calculations and deep learning in neural-networking. To solve the issue, we must be back to the origin of computer system design pursuing what memory in computer systems should be. We are developing Marching Memory (MM), and can show how the features of it suit the solution with reasonable conditions such as higher speed with no latency under lower power consumption than SRAM.
Future Location Prediction of a Mobile User Using Historic Visiting Patterns

The ability of modern smartphones to provide us with real time location-based data is one of its most important features. Being able to predict a person’s future location based on the real time location data would be the next step in utilizing this functionality. Using this functionality, combined with machine learning one’s probable destination can be predicted with a reasonable accuracy. People don’t always use map-based navigation for the places they visit every day, like their work place or school and there may be significant traffic on the regular route taken, however, if our device knows where we’re headed, it can warn us beforehand and help us reroute. This functionality can also be used by cops to determine the future location of a criminal fleeing a crime scene.These features and functionalities can be implemented through various machine learning algorithms which are compared to determine the most accurate one. The proposed system can predict a user’s future location using the current location and time, learning from the user’s previously visited locations.
Islanding Detection Method for Inverter Cluster Based on Interharmonic Impedance Measurement

As the number of grid-connected inverters in distributed generation systems increases, the detection signal of the active method will interfere with each other. According to the reason, it is necessary to study the islanding detection technology applicable to the inverter clusters with different control strategies. In this paper, the islanding detection method of inverter clusters based on interharmonic impedance measurement is studied. The detection principle of the method is that the impedance of the system increases when islanding operation state is enabled. This method connects the interharmonic disturbance device at the point of common coupling, and collects the interharmonic voltage and current at the output terminals of the disturbance device by FFT. Then, the interharmonic impedance change between systems and judge is calculated whether the system is in the islanding operation state. Finally, the effectiveness of the method is confirmed by MATLAB/Simulink platform.
DC Power Supply Control System Based On Hybrid Energy Storage

In the paper, in order to solve the problem that the response speed of DC power supply of lead-acid batteries for high-frequency devices is slow. Based on a thorough study of the structure and operation mechanism of DC operating power supply, this paper proposes a new hybrid storage power supply system composed of lithium batteries and super-capacitors instead of lead-acid batteries. The advantages of hybrid storage technology applied in DC operating power supply system are introduced in details. At the same time, the energy optimization management of new DC operating power supply is also discussed. The dynamic power sharing between lithium batteries and super-capacitors is realized. At the same time, the dynamic power limitation method of lithium batteries based on Super-capacitor SOC is adopted to dynamically limit the charging and discharging power of batteries and improve the life of batteries. At the last, the superior performance of the proposed new DC operating power supply is verified by simulation.
A Design of Ink-Printable Triband Slot Microstrip Patch Antenna for 5G Applications

In this paper, we present a slot microstrip patch antenna with ink-printing fabrication for 5G applications. The antenna has a three-layer structure with a slotted patch, operating at three frequency bands, namely, 5.73 GHz, 6.16 GHz and 8.34 GHz, with an average antenna gain of 5 dBi.
Influence of Batch Normalization on Convolutional Neural Networks in HRRP Target Recognition

In the task of radar marine target detection and recognition based on deep learning, convolutional neural network (CNN) is used to realize the recognition of high range resolution profile (HRRP) of target. And batch normalization (BN) is introduced into CNN to solve the problem that model is easy to over-fitting. Simulation results show that BN can accelerate the convergence speed of model training, increase the upper limit of initial learning rate, and improve the recognition accuracy of model.
A Maximum Fitting-based TrAdaBoost Method for Detecting Multiple Subjects' P300 Potentials

Individual difference of brain signal leads to the P300-based interface needs a large amount of training data to construct a pattern recognition model for each subject. Lots of training increases the training cost, and causes the subject’ s fatigue. TrAdaBoost is a method of transfer a learned classifier's information to another classifier. In order to solve the problem of overfitting caused by combining too many classifiers, a novel maximum fitting-based TrAdaBoost (M-TAB) is proposed to identify the P300 potential across multiple subjects. The M-TAB first trains a classifier with a small number of a subject's data. Then it uses this classifier to adjust the weights of many other classifiers that are trained by other subjects' data. The method retains a high accuracy of 91.05% even if the training data is reduced to 33.33%. The M-TAB improves the accuracy and the information transfer rate by 10.65% and 2.31 bits • min-1, compared with the traditional training method.
Behavioural Smoking Identification via Hand-Movement Dynamics

Smoking is a commonly observed habit worldwide, and is a major cause of disease leading to death. Many techniques have been established in medical and psychological science to help people quit smoking. However, the existing systems are complex, and usually expensive. Recently, wearable sensors and mobile application have become an alternative method of improving health. We propose a human behavioural classification based on the use of a one-dimensional local binary pattern (LBP), combined with a Probabilistic Neural Net (PNN) to differentiate smoking from other movements as measured from a wearable device. Human activity signals were recorded from two sets of 6 and 11 participants, using a smart phones equipped with an accelerometer and gyroscope mounted on a wrist module. The data combined structured and naturalistic scenarios. The proposed architecture was compared to previously studied machine learning algorithms and found to out-perform them, exhibiting ceiling level performance.
Study on Frequency Emergency Coordination and Optimal Control with Multitype Measures

System moment of inertia and frequency regulation ability is decreasing with the increasing scale of UHVDC and new energy integration in China. Therefore, the frequency stability of the system is becoming increasingly prominent. The cost of traditional security control measures such as generator tripping and load-shedding is high, the low-cost DC emergency power support, the new energy frequency modulation, load control and other continuous regulation control measures get more attention in daily operation. Aiming at different types of control measures, this paper puts forward a frequency security margin measurement index, which takes multi-type control measures into account. On the premise of ensuring frequency security and stability of power grid, the cost of frequency control after power grid failure can be reduced and the requirement of lean frequency stability control of modern power grid can be met by coordinating the continuous and discrete control measures. Based on the actual planning of UHVDC transmission network, the feasibility of the proposed method is verified.
Energy-Efficient Base Station Deployment in Heterogeneous Communication Network

With the advent of the 5G era, mobile users have higher requirements for network performance, and the expansion of network coverage has become an inevitable trend. Deploying micro base stations (BSs) is regarded as one of feasible approaches to enhance network coverage. However, unreasonable deployment will cause mutual interference between base stations and further increase energy consumption. In this paper we formalize the deployment of micro BSs in the coverage area of macro BSs as a mixed integer nonlinear programming problem, and then propose, based on Kuhn-Munkres matching algorithm, an energy-efficient strategy to reduce network-wide energy consumption in polynomial time complexity. The simulation results show that the energy optimization ratio of the proposed strategy is higher than two benchmark strategies and the proposed deployment strategy can reduce energy consumption to 40%.
Analytical Prediction and Analysis of Electromagnetic-Thermal Fields in PM Eddy Current Couplings With Injected Harmonics Into Magnet Shape for Torque Improvement

To improve the output torque in permanent magnet (PM) eddy current couplings, a new structure based on harmonics injection into the magnet optimization theory is proposed. Then, a novel analytical electromagnetic-thermal model is established that can accurately reflect the 3-D distributions of the magnetic field and the eddy current. With the proposed analytical model, the eddy current loss, temperature distribution, torque characteristics and influence of the temperature rise caused by load increases on the torque are predicted and analyzed precisely. The maximum output torque can be improved by 11.22% with injected harmonics into the magnet shape. On this basis, an optimal design of the conductor plate structure is presented, based on the electromagnetic-thermal analytical model. An additional 13.05% torque improvement can be obtained, which leads to better torque characteristics and overload capability. Verification is conducted by a 3-D finite element analysis and measurements. The analytical model solves the multiphysics coupling analysis problem of PM eddy current couplings, and provides a fast and accurate calculation method to perform electromagnetic-thermal coupling analysis.
A Composite Fabric-Based Soft Rehabilitation Glove With Soft Joint for Dementia in Parkinson’s Disease

A kind of wearable exoskeleton soft rehabilitation glove is proposed for the dementia in Parkinson’s disease (PD) patients with loss of hand function, limited range of motion, and insufficient finger muscle strength to carry out rehabilitation exercise training in a passive or auxiliary way. A novel soft joint structure based on composite fabric material is introduced for the design of the soft glove with bionic method, and experiments are conducted to verify the effeteness of the proposed soft rehabilitation glove. The test results showed that when the fluid pressure was 0.42 MPa, the joint angle of MCP, PIP and DIP could be up to 81°, 98°, 72°, and produce output torque of 1.18Nm, 1.44Nm and 1.82Nm respectively, which meets the requirements of the hand rehabilitation. A dynamic rehabilitation-training test of the rehabilitation glove was also carried out, and the results showed that the movement frequency of soft fingers could reach 30 times/min, which is sufficient for repetitive flexion/extension exercise. In order to verify the grasping characteristics of the soft glove for irregular objects, experiments were carried out. The experimental results showed that the bionic soft glove was dexterous in grasping, which conforms to the universal grasping characteristics of human hands, has the function of assisting daily life (ADL), and meets the requirements of rehabilitation.
A Comprehensive Review of Health Indicators of Li-ion Battery for Online State of Health Estimation

Lithium-ion batteries have been widely used in various applications, especially in electric vehicles and battery energy storage systems (BESS). Reliable and accurate estimation of battery online state of health (SOH) becomes increasingly important. To realize this task, a variety of approaches have been proposed and several review papers have been addressed to summarize and classify these approaches. To the best of our knowledge, all current works concentrate on classifying estimation methods while no work has been done to review the health indicators (HIs) that are used for estimation. Classifying the HIs by how they are extracted, however, is of great importance because each HI is only effective in specific application situations. This paper, therefore, is to comprehensively review the existing HIs and make a classification based on how they are extracted and where they can be effectively used.
Simulation-Ready Characterization of Soft Robotic Materials

Simulation-driven design of soft robots requires accurate and stable simulations. In this article, we present an inverse finite element approach enabling simulation-ready characterization of soft robotic materials from uniaxial test data. Representing test specimens with finite elements, and modeling the specimen-device coupling, we enable the simultaneous fitting of hyperelastic parameters to single or multiple tests performed on specimens of varying shape and size. To safeguard against simulation instabilities, or non-physical behavior, we reparameterize and bound parameters using the consistency with linear elasticity. We use our characterization on commonly used silicones, and discuss the resolution- and order-dependence of fitted parameters, and the Mullins effect in the context of simulation-based design.
Survey of Depression Detection using Social Networking Sites via Data Mining

Depression detection from Social Networking sites has been studied broadly in previous years. These sites provide a platform for their users to share their life events, emotions, and everyday routine. Many researchers demonstrated that content generated by the users is an efficient way to know about their mental state. By mining user-generated content, depression can be predicted. By collecting all the necessary and relevant information from the social networking sites from the posts, we can predict the person’s mood or negativity. This survey paper focuses on prior research done regarding detecting depression levels based on content from social network sites.
Photometry Result of an Eclipsing Binary Star V798Her in the Globular Cluster NGC 6341

We present the photometric study of an eclipsing binary star V798Her in the globular cluster NGC 6341. The observations were obtained in Johnson's B, V & R filters using 0.8m Tarleton telescope and a CCD photometer. The observed light curves after determination of light elements show that V798Her is a W UMa contact binary system with a period of 0.2951110 days. This binary system will be further analyzed using Wilson Devinney model 2015 version.
A Low-Power Mixed-Signal ASIC for SiPM Readout at Low Temperature

A mixed-signal ASIC developed to readout silicon photomultipliers (SiPM) at low temperature is presented. The chip is designed in a 110 nm CMOS technology. Both single photon counting and Time-over-Threshold (ToT) operating modes are supported. The ToT modality is useful when many photons pile-up to yield a continuous signal. In single photon counting mode an event rate of up to 5 MHz per channel can be accommodated. The time resolution is 50 ps and the target power consumption is less tha 5 mW per channel. The architecture of a first 32-channel prototype is described. Dedicated test structures to qualify critical building blocks at cryogenic temperature have also been deployed and experimental results will be discussed.
The Modified Encoder-decoder Network Based on Depthwise Separable Convolution for Water Segmentation of Real Sar Imagery

In order to improve the accuracy and generalization ability for water segmentation in real SAR images, this paper proposes a modified feature extraction network and a modified encoder-decoder network based on depthwise separable convolution. Training these networks with a well-labeled water segmentation dataset and a series of adjusted hyper-parameters, this paper attains a well-trained water segmentation model for real SAR images. Segmentation results of spaceborne SAR images demonstrate the high accuracy and good robustness of the proposed method.
Kinetic Model Fitting with Forced Convexification

During dynamic tomography reconstruction, kinetic models, e.g. the two-tissue compartment model, should be fitted to the reconstructed activity values in each voxel and frame, which is equivalent to the solution of a huge number of general optimization problems. Newton’s and Levenberg’s methods have quadratic convergence speed close to the optimum, but they may get stuck in local extrema, and their convergence is not guaranteed at all. The failure of convergence of the voxel time activity functions may slow down the convergence of the global reconstruction. To solve this, we propose the dynamic transformation of the target function to shrink those regions of the domain where Newton’s method would not go into the direction of the optimum. We specify the conditions of such transformations and provide concrete solutions. We also make the transformation scale or time dependent to allow its gradual elimination when it is not needed anymore.
Fast Neutron Spectroscopy with a Nitrogen-Based Gaseous Detector

A simple, efficient and safe to use but also affordable fast neutron spectroscopic system is the Holy Grail in many scientific and industrial communities. 3He based detectors provide a solution that fulfils most of the requirements but its high demand combined with its scarcity has created a worldwide shortage and efforts are focused on finding an alternative solution. Several detectors are proposed for this task, such as BF3 proportional counters, plastic scintillators, and recoil detectors, all with disadvantages that make their adoption far from ideal. We propose a system based on a spherical proportional counter filled with nitrogen-based gas mixtures to exploit the 14N(n,α)B11 and 14N(n,p)C14 reactions for the detection of fast neutrons. 3He and N2 have comparable Q-values and absorption cross-sections for fast neutron capture, o ering similar detection capabilities. In the future, such a detector could be utilised in industrial applications such as reactor monitoring, medical physics, accelerator facilities, homeland security, fuel searches and serve as a tool in scientific research.
A Multiband (WWAN/Bluetooth/WiMAX) Square Monopole Antenna with Simple Structure for Wireless Communication System Applications And Optimization by using Artificial Intelligence

In this research article, a square monopole multiband antenna is designed for applications including Wireless Wide Area Network which includes Digital Cellular System (1.71GHz-1.88GHz) and Personal Communication System (1.85GHz-1.99GHz), Bluetooth (2.402GHz-2.480GHz) and World Wide Interoperability for Microwave Access (3.30GHz-3.80GHz). These above said operating wireless technologies are obtained by using 2 L-Shaped stubs embedded with patch and etched L-shaped slot on radiating patch. Lengths of the stubs are optimized by using simulators and algorithm used by artificial intelligence (Radial Basis Model) Antenna results are simulated on two different EM simulators to validate and offers gain of 3.86, 4.42 and 4.18dBi respectively in operating bands.