Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effectiveness of Gait Event Detection Based on Absolute Shank Angular Velocity in Turning

Heel-strike (HS) and toe-off (TO) events of the human walking are the basis for gait analysis. The gait event detection algorithm is applied in the fields of spatio-temporal parameter analysis, rehabilitation, and wearable auxiliary devices. Based on the shank’s absolute angular velocity in the sagittal plane, offline/online detection of HS-TO for normal walking, incline walking and other gaits in sagittal plane can be achieved. This type of detection algorithms assume that the motion occurs only in the sagittal plane, and the motion characteristics of the left and right shanks in one gait cycle are consistent. However, the effectiveness of this type of gait event detection algorithms for the turning gait is unclear. The turning gait in daily life is frequent and inevitable. Ignoring the gait event detection for the turning gait will limit the application of the existing gait event detection algorithms. To this end, this paper compared the degree of symmetry of the absolute angular velocity of the left and right shanks under normal walking and turning gait. The validity of a typical HS-TO detection algorithm based on shank joint angular velocity is analyzed, and the necessity of studying the turning gait event detection algorithm is demonstrated.
Linear Quadratic Pareto Game Problem: A Case of Deterministic Controller

In this paper, we study a kind of linear quadratic Pareto game problem in stochastic system. The biggest difference from the existing literature, is that the control is described by deterministic time function. This kind of game is inspired by some national policy arising from government and can be used to present the players’ action. By applying the weighted sum method, we could transform the Pareto game into an optimal control problem and by virtue of Pontryagin maximum principle, we derive all Pareto efficient strategies. Moreover, we conclude that the optimal state is characterized by a linear stochastic differential equation of mean-field type, which is one of novel features in our paper. Finally, we work out one numerical example to enhance the implementability of the theoretical results.
Auto Cable Pretension Method for Soft Exosuit Based on Gait Trajectory Prediction Network

Cable-driven systems are widely used in various kinds of robots. And this actuation method is also applied in SIAT Soft Exosuit (SSEX). In practice, due to the softness of cable and necessity of not restricting wearer’s mobility, the cable is designed to be a little loose and will be looser in some particular time. Consequently, force will not be transmitted through the cable until it is stretched, which will increase system latency. To this end, we propose an auto pretension method to reduce system latency in cable-driven soft exosuit system. Our approach achieves latency decreasing by pre-tightening the cable before the time point we need it to transmit force. And thus, we have to predict the gait trajectory to know when to start pretension procedure in advance. To solve this problem, we propose a Gait Trajectory Prediction Network (GTPN) which can predict gait trajectory by analyzing wearer’s body parameters. Experimental results demonstrate the obvious decreasing of system lag and better wearing experience.
Image Stitching Based on Improved Gradual Fusion Algorithm

In order to improve the effect of image stitching and eliminate the gap between images caused by the uneven local brightness of the image in the process of stitching, an improved gradual fusion algorithm is proposed. Compared with linear gradual fusion method, the improved gradual fusion method has a non-linear change in weight, so it achieve no stitching gap and smooth transition at the stitching point . Surf algorithm is used for feature point detection and Hessian matrix is used to obtain feature points. The feature points are matched by Euclidean distance, and the improved gradual fusion algorithm is used for image fusion to achieve the smooth transition. The experimental results show that the improved algorithm could improve the effect of image fusion significantly.
Sliding Residual Network for High-Speed Target Detection in Additive White Gaussian Noise Environments

Facing high-speed targets, the traditional radar target detection framework may suffer from performance degradation due to the signal-to-noise ratio loss caused by Doppler mismatches, especially for phase coded waveforms. In this paper, an end-to-end sliding residual network detector (SRND), which is derived from the likelihood ratio test, is proposed to detect high-speed targets in additive white Gaussian noise environments with a single radar echo pulse. The SRND uses a residual network with an efficient depth to increasingly capture the representations of target echoes, and we partially show this process through visualization. The SRND is robust to target velocities because the employed residual network utilizes layers of convolutional filters to match with target echoes of both low-speed and high-speed targets. Besides, with a waveform adapter, the SRND is compatible with different waveforms, that is to say, the SRND needs to be trained only once and then can cope with different phase modulations of waveforms. More importantly, the SRND, which is trained with computer-generated data only, can deal with not only simulated data but also measured data. Numerical experiments are given to demonstrate the superior detection performance of the SRND over the traditional detector.
The optimal type of online channel considering showrooming

Considering the common market phenomenon "showrooming", this paper studies the optimal type of online channel under the supply chain with one manufacturer and two retail channels. There are two types of online channel, which are the reseller channel and the marketplace channel. Our conclusions are as follows. In the presence of showrooming, first, the marketplace channel increases the profit of the supply chain; second, if the platform fee is not high enough, the manufacturer prefers to adopt the marketplace channel; finally, if the platform fee is intermediate, the marketplace channel is a win-win strategy for the manufacturer and the online retailer. In the absence of showrooming, if the platform fee is low enough, the marketplace channel increases the profit of the supply chain; if the platform fee is high enough, the reseller channel increases the profit of the supply chain.
Analysis and Suppression Strategy for the Double-Line Frequency Pulsation in Single-Phase Quasi-Z-Source Converter

Devices and the passive components in the single-phase impedance source network converter are sensitive to the double-line-frequency (DLF) pulsation. Previous research works have studied the power decoupling and the DLF equivalent model for the power pulsation suppression method. This paper explored the DLF transfer routes in the impedance source network. A comprehensive stress derivation of the passive components and switching devices are obtained for the suppression method enhancement and optimization. For the DLF passive suppression method, the explanation for the passive component volume issue is provided from the stress aspect. The component value solution space and corresponding design guidance are presented. For the DLF active suppression method (ASM), a buck-type dc-side ASM suppression method is employed and its optimization is demonstrated with the assistant circuit stress analysis. The total capacitor and inductor values get reduced by 77% and 25%, respectively. The corresponding pulsewidth modulation method implementation and the common-mode leakage current demonstration are also presented. The effectiveness of the theoretical analysis and optimization methods is verified by simulations and experiments.
A Unified Link Prediction Framework for Predicting Arbitrary Relations in Heterogeneous Academic Networks

Most of the existing link prediction methods for heterogeneous academic networks can only predict one or two specific relation types rather than arbitrary relation types. Although several recently proposed methods have involved multi-relational prediction problems, they do not comprehensively consider the rich semantic or temporal information of heterogeneous academic networks. Considering that researchers may have diverse requirements for different types of academic resources, in this study, we propose a new unified link prediction framework (UniLPF) for arbitrary types of academic relations. First, a weighted and directed heterogeneous academic network containing rich academic objects and relations is constructed. Then, an automatic meta-path searching method is proposed to extract the meta-paths for arbitrary prediction tasks. Two meta-path based object similarity measures combining temporal information and content relevance are also proposed to measure the features of the meta-paths. Finally, a pervasive link prediction model is built, which can be embodied based on an arbitrarily specified prediction task and the corresponding meta-path features. Extensive experiments for predicting various relation types with practical significance are conducted on a large-scale Microsoft Academic dataset. The experimental results demonstrate that our proposed UniLPF framework can predict arbitrary specified academic relations, and outperforms the comparison methods in terms of F-measure, accuracy, AUC and ROC. In addition, the time scalability experiments prove that UniLPF also achieves good performance for predicting the academic relations over time.
Derivative feedback control for differential-algebraic systems with time-varying delay

This paper considers the problem of derivative state feedback (DSF) control for differential-algebraic systems with time-varying delay. Based on system augmentation technique, we realize the differential-algebraic systems normalization problem by virtue of derivative feedback control. Sufficiency conditions are derived in terms of linear matrix inequalities (LMIs) by selecting delay-dependent L-K functional. Numerical example demonstrates the availability of the proposed method.
Phytochemicals: Aging and Health

Technophysiology, Evolution, and Aging: Towards a New Image of Aging, P.S. Timiras Are There Age-Related Changes in Flavonoid Bioavailability? , C-Y. Oliver Chen and J.B. Blumberg Trends in the Analysis of Phytochemicals: Flavonoids and Carotenoids, E. Koh and A.E. Mitchell Anti-inflammatory Botanicals: A Case Study of Genetic Screens as Part of a Pharmacogenomic Approach, M. Dey, I. Belolipov, S. Zakirov, A. Akimaliev, J. Akimaliev, I. Sodonbekov, and I. Raskin Characteristics and Function of Lutein and Zeaxanthin within the Human Retina, B.R. Hammond, Jr., and L.M. Renzi Phytochemicals and the Aging Brain, S. Bastianetto and R. Quirion Green Tea: Skin Care and Skin Diseases, S.D. Hsu Pomegranate Phenolic Antioxidant Activities Protect against Cardiovascular Diseases, M. Aviram, M. Rosenblat, and B. Fuhrman Customizing Dietary Recommendations by Genotype in the Era of Genomics, S. Vakili, G. Jooyoung Shin, and M.A. Caudill Development of a Botanical Combination Product for Allergy Symptoms, M. Lemay, D. Fast, Y. Lin, C. Buyce, and L. Rozga IL-1 Genotype-Selective Inhibition of Inflammatory Mediators by Botanicals: A Nutrigenetics Proof of Principle, R.K. Randolph Index
Hybrid ANN and Artificial Cooperative Search Algorithm to Forecast Short-Term Electricity Price in De-Regulated Electricity Market

Smart grid has evolved into a viable platform for participants of electricity market to effectively regulate their bidding strategies based on demand-side management (DSM) models ascribed to its immense technological advancements in recent years. Reliability of system operation as well as capital cost investments can improve greatly with responsiveness of market participants. In this regard, efficient design, implementation, evaluation of numerous demand response measures and development of robust short-term price forecasting in the day-ahead transactions are of the utmost importance. Accuracy and efficiency of the day-ahead price forecasting process are complex challenges in deregulated electricity market. The unstable nature of electricity price compared to load series causes lower accuracy. Therefore, this research proposes a hybrid method for electricity price forecasting via artificial neural network (ANN) and artificial cooperative search algorithm (ACS). In parallel, a feature selection technique based on the combination of mutual information (MI) and neural network (NN) is developed in this study to select the input variables subsets, which have substantial impact on forecasting of electricity price. Actual data sets are collected from Ontario electricity market of the year 2017 for the verification of simulation results. Finally, the simulation results validated the premise of the proposed hybrid method through enhanced accuracy compared to the results acquired by implementing hybrid support vector machine (SVM) and hybrid ANN optimization methods.
Trajectory Tracking of Mobile Service Robot

In recent years, there is a strong demand for automated and even autonomous robots in many fields such as manufacturing or social services and varieties of robots have been developed. In order to realize the humanized service from the robots, path planning and path tracking problems should be addressed according to typical and unique working environment. In this paper, the simplified four wheeled electric vehicle was chosen as our robot model, then the lateral controller was proposed based on the Lyapunov stability theory and linear control theory. Associated simulation models were constructed in the MATLAB environment, and simulation results show that the designing method of commad signals is reasonable and linear feedback controller can be used for robot trajectory tracking.
Can Structural MRI Radiomics Predict DIPG Histone H3 Mutation and Patient Overall Survival at Diagnosis Time?

Radiomics was proposed to identify tumor phenotypes noninvasively from quantitative imaging features. The present study aimed at investigating if radiomic features measured at diagnosis time from structural MRI can predict histone H3 mutations and overall survival of patients with diffuse intrinsic pontine glioma. To this end, 316 radiomic features from multimodal diagnostic MRI of 38 patients were extracted, and three clinical parameters were added. Two approaches for computing radiomic features were proposed: a global estimation from a spherical region of interest defined inside the tumor and a local estimation where features are computed inside the previously defined region from fixed size spherical patches and the mean of these features is considered. A feature selection pipeline was then developed. Three machine learning models for H3 mutation classification and three regression models for overall survival prediction were used. Leave-one-out F1-weighted scores for SVM model combining imaging and clinical features reached 0.83, showing a good prediction of H3 mutation using structural MRI. Results on overall survival prediction are not conclusive and suggest the need of a larger number of patients.
Online Multi-modal Imitation Learning via Lifelong Intention Encoding

For the imitation learning of robotic manipulation, generalized ability that enable robots to continuously acquire new tasks and reuse pre-existing experience from previous learned tasks is necessary. In this paper, we aim to learn multi-modal imitation demonstrations with different intentions by optimizing a sparse coding lifelong intention dictionary, which enable robots to autonomously imitate various complex behaviours. We investigate multi-intention which encode from an action imitation learning tasks sequence that comes from various experts with diverse intention. Our approach developed a lifelong imitation learning framework for inverse reinforcement learning (IRL) to deal with multi-intention imitation learning problem. suitable for inverse reinforcement learning intention encoding from open-ended demonstrations. Our work is aimed at producing the reward function of general tasks and maintaining a reusable experience dictionary automatically which can be used for online multi-intention sparse encoding.
Research Progress on Joint Operation Modeling Based on Complex Networks

With the development of joint operations theory, the modeling and analysis of its complex systems has become a research hot point in the military field. As a comprehensive research theory, complex networks not only have a natural similarity with the joint operation system, but also have strong advantages in describing the emergence of the system. Therefore, it has attracted the attention of many scholars in recent years. Based on the establishment of the model of complex networks in joint operations, this paper introduces the concept of joint operations and the theory of complex networks, and focuses on the modeling research results. Finally, we put forward some viewpoints on the existing modeling methods and discusses the possible development prospects in this field.
Robust Object Tracking Via Low-Rank and Reverse Fused-Lasso Regularization

Object tracking via low-rank and sparse learning easily exists the following problems: 1) requiring to solve a lot of l1 optimization problems, which is time-consuming. 2) usually occurring tracking drift when facing object abrupt motion. Therefore, a low-rank and reverse fused-lasso regularization based object tracking algorithm is proposed. Firstly, convex low rank approximation based on the nuclear norm is used to constraint the correlation of the candidate particles, so as to remove the uncorrelated particles. Secondly, fused lasso regularization is introduced to the object tracking, minimizing the absolute value sum of the sparse coefficient difference, which can restrict the object appearance with little difference between the consecutive frame, but allow the greater variation between individual frames, so as to adapt object abrupt motion. Thirdly, an inverse sparse representation formulation is built for object appearance, using candidate particles to represent the target template inversely, simplifying the computation for online tracking. Experimental results show that the proposed algorithm performs favorably against state-of-the-art methods, especially for object abrupt motion.
Large-scale Classification of 12-lead ECG with Deep Learning

The 12-lead Electrocardiography(ECG) is the gold standard in diagnosing cardiovascular diseases, but most previous studies focused on 1-lead or 2-lead ECG. This work uses a large data set, comprising 7,704 12-lead ECG samples, as the data source, and the goal is to develop a classification model for six common types of urgent arrhythmias. We consider the characteristics of multivariate time-series data to design a novel deep learning model, combining convolutional neural network (CNN) and long short-term memory (LSTM) to learn feature representations as well as the temporal relationship between the latent features. The experimental results indicate that the proposed model achieves promising results and outperforms the other alternatives. We also provide brief analysis about the proposed model.
Trajectory Planning for De-tumbling Space Debris Using Tethers Based on Radau Pseudospectral Method

The tethered space-tug system which contains a chaser, a target spacecraft and connected by a tether is regarded as a promising way for active debris removal to mitigate the debris level. Based on Radau Pseudospectral Method, this paper investigates the trajectory planning of chaser for de-tumbling the debris target, which is one of most challengeable part in a removal mission. The constraints of collision and tether angle are considered in the path optimization, and a PD controller is designed to track the planned trajectory. Simulation results show that the tumbling spacecraft could be stabilized through the maneuver of chaser and control of tether tension, and closed-loop controller could improve the tracking accuracy.
Design of Bidirectional DC-DC Converter Control Based on Arrange the Transition Process

As the demand for electricity in automobiles, communications, computer systems, hybrid vehicle systems, and aerospace systems continues to increase, bidirectional DC-DC converters are widely used in these fields. The traditional two-way DC-DC converter adopts PID control. However, in the initial stage of the transition process, due to large error, the control force is too large, and overshoot is likely to occur. A new bidirectional DC-DC converter control method based on the arrangement transition process is designed for the robustness and stability of the energy storage system. After obtaining the second-order linear model of the converter by the identification method, the PID controller and the PID controller that arranges the transition process are used to simulate and experiment the voltage and current control of the bidirectional DC-DC converter. The experimental results show that the transition process can eliminate the output overshoot during the initial operation of the converter, and to some extent reduce the contradiction between the system overshoot and the adjustment time.
The Stability Analysis of DC-DC Conversion Systems Based on Monodromy Matrix

The stability analysis for the dc-dc conversion system which can be used in dc distributed power systems (DPS) is discussed in this paper. In this paper, the methods of monodromy matrix and small-signal analysis are proposed for the stability analysis of the dc-dc conversion system. Firstly, the methods of stability analysis for the whole system are generalized which can be used in any dc-dc conversion system as loon as the periodic orbit exists. Then, the efficiency of the propose methods can be verified though the dc-dc conversion system, which can minimize the ripple of the voltages, and altered radio of intermediate bus and output voltages for applying in pulse-width-modulated (PWM) voltage source converters for many industrial applications. Finally, stability of the systems can be verified by the experimental prototypes.
On the Product of Complex Gaussians With Applications to Radar

The joint probability density function (pdf) of a complex vector, which is the product of a complex Gaussian scalar and a complex Gaussian vector, added to another complex Gaussian vector, is derived. The pdf, cumulative distribution function, moments, and moment generating function of the squared norm and norm of this vector are then derived. The derived result is then used to characterize the performance of energy detector (square law detector) for radar system.
A New Switching Nonlinear Extended State Observer

To improve the observation accuracy and shorten the transient time simultaneously, in this paper, we propose a new nonlinear extended state observer (ESO) by using a switching strategy. The convergence of the proposed ESO is proved with explicit error estimation. The effectiveness of the proposed switched nonlinear ESO is demonstrated through numerical simulations and comparison with existing ESOs. The numerical results show that the proposed new ESO is more accurate and the transient time is shorter than the previous ESOs with the same tuning parameter.