Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Arm Movement Aided Electronic System for Parkinson's Patients

Parkinson's patients have different degrees of dyskinesia due to the controlling ability decrease of the brain to muscles, their typical symptoms are static tremors. A movement aided electronic system was designed in this paper, it can assist patients' arm movements to restrain the tremor. Feed-forward control and band limited multiple Fourier Linear Combiner Algorithm were adopted. The experiment results showed that the system could initially realize the function of arm movement to relieve the tremor, which had certain effect to improve the life quality of Parkinson's patients.
Research on a Continuous Deep Belief Network for Feature Learning of Time Series Prediction

A continuous Deep Belief Network (cDBN) with two hidden layers is proposed in this paper, focusing on the problem of weak feature learning ability when dealing with continuous data. In cDBN, the input data is trained in an unsupervised way by using continuous version of transfer functions, the contrastive divergence is designed in hidden layer training process to raise convergence speed, an improved dropout strategy is then implemented in unsupervised training to realize features learning by de-cooperating between the units, and then the network is fine-tuned using back propagation algorithm. Finally, the experiments on CATS benchmark and waste water parameters forecasting show that cDBN has the advantage of higher accuracy, simpler structure and faster convergence speed than other methods.
LADRC based TEC Temperature Control System

thermoelectric cooler (TEC) is widely applied in temperature control areas. Traditional PID based temperature controller cannot reach a stable operation status. In this paper, a LADRC based TEC temperature control system is designed. Comparing with the PID based control process, the LADRC based one has a significant better dynamic tracking capacity to the external and internal disturbances. Moreover, since LADRC algorithm ignores the complex thermal conduction process of the system, the LADRC based control has a potential applicability in practical applications.
Real-Time Multi-Modal People Detection and Tracking of Mobile Robots with A RGB-D Sensor*

A real-time multi-modal people detection and tracking algorithm based on RGB-D information is presented in this paper. The color image and the depth image is calibrated and aligned firstly with each other. At texture level a SVM classifier based on HOG features is used to detect and identify human body in color images, while at depth level an Adaboost classifier with Haar-like features is used to detect human head in depth images respectively. With the space relationship between human body and head, a combined multi-modal cascade detector is then constructed, which can greatly improve the detection accuracy. For tracking tasks, the KCF(Kernelized Correlation Filter) algorithm is adopted to realize the real-time people tracking, in which color and depth features are both considered and used in template matching to guarantee the robustness of the tracking algorithm. The proposed algorithm is robust to object occlusion and tracking lost caused by fast movement or cross walking. Extensive experiments are carried out on a wheeled mobile robot mounted with a Kinect2 sensor, and the result shows the feasibility and effectiveness of the method.
An initialization-free continuous-time distributed algorithm for convex optimization problem

In this paper, a distributed convex optimization problem for continuous-time multi-agent systems is studied. The objective function is equal to the sum of several strongly convex cost functions with Lipschitz gradients, which are solved by the corresponding agents. A new distributed algorithm is proposed and the multi-agent system is represented by a strongly connected undirected graph. The exponential convergence of the algorithm is analyzed and the minimum exponential convergence rate is obtained. Finally, mathematical simulations are implemented to illustrate the results.
Shared bicycle dynamic distribution model based on Boosting algorithm

The number of shared bicycles is large and the parking is not standardized. Accurate position prediction of the bicycle can effectively prevent the collision between the number of vehicles and the parking position. The traditional machine learning algorithm is slowly to process and can not meet the accuracy requirements. Therefore, the Boosting algorithm which promote the weak learner to a strong learner is adopted to enhance running speed and accuracy. At the same time, the way of constructing feature groups is proposed, and the importance of features is evaluated. The comparison experiment compares the prediction capabilities of the three Boosting algorithms, XGboost, LightGBM and Catboost. The example verification shows that the LightGBM algorithm has the highest test accuracy, and then this algorithm is used to predict the dynamic distribution of the Mobike in Beijing throughout the day. And show the heat distribution map of the prediction results of Mobike in different time periods.
Human Cooperative Control of Lower-limb Robotic Exoskeleton Based on Region Function

A human-cooperative control algorithm based on human region had been proposed in this paper, which enable the subjects change the movements of the lower-limb exoskeleton robots. At first, the kinematic and dynamics of the lower-limb exoskeleton robots is proposed, then we divide the task space into two portion, i.e., a human region, in which human can change the movement of the robots, a robot region, in which the robot drive take the lead. The designed control incorporating adaptive learning technique makes tracking errors convergent while the different region can be switched. By using Lyapunov analysis, we proved the closed-loop system is stable. The experimental results are presented to demonstrate the proposed controller have a good tracking performance.
A Performance Comparison of Data Mining Algorithms Based Intrusion Detection System for Smart Grid

Smart grid is an emerging and promising technology. It uses the power of information technologies to deliver intelligently the electrical power to customers, and it allows the integration of green technology to meet the environmental requirements. Unfortunately, information technologies have inherent vulnerabilities and weaknesses that expose the smart grid to a wide variety of security risks. The Intrusion detection system (IDS) plays an important role in securing smart grid networks and detecting malicious activity, yet it suffers from several limitations. Many research papers have been published to address these issues using several algorithms and techniques. Therefore, a detailed comparison between these algorithms is needed. This paper presents an overview of four data mining algorithms used by IDS in Smart Grid. A performance evaluation of these algorithms is conducted based on several metrics including the probability of detection, probability of false alarm, probability of miss detection, efficiency, and processing time. Results show that Random Forest outperforms the other three algorithms in detecting attacks with a higher probability of detection, lower probability of false alarm, lower probability of miss detection, and higher accuracy.
Research on Improved Stochastic Robust Attitude Control Technology

The traditional stochastic robust method, which uses Monte Carlo to estimate the probability, has the disadvantages of sampling point accumulation and long-time consumption. The Latin hypercube sampling (LHS) is used to sample the uncertainty domain, and the sampling time can be reduced. Then, the stochastic robust objective function is established, and the parameters of the control law are optimized by genetic algorithm, so that the control precision can meet the design target in the uncertain domain. The simulation results of a hypersonic vehicle show that the scheme can meet the system control requirements and has strong robustness.
Amodal detection of 3D objects with separate network output branches

Indoor 3D object detection has always been a great challenge in the field of computer vision because of the irregularities of indoor object’s shape and posture. In this paper, an improved deep neural network structure with four separate output branches is proposed. The neural network separately regress dimension, coordinate and orientation offsets, which can better predict the 3D-bounding-box’s position, size and angle informations. Meanwhile, a new loss function is introduced according to the proposed network structure. The experimental results evaluated on the public NYUV2 dataset demonstrate that the proposed algorithm outperforms the state-of-the-art method and has a better generalization ability for different indoor objects.
CSR: A Centrality Based on Robustness and Controllable Subspace of Complex Networks

Complex networks are everywhere, some of them are airline network, road network, power grid network and protein-to-protein network. These networks are robust in failure or under attack but still needs in-depth study. Researchers have proposed various techniques to find and enhance the robustness of networks. However, furthers study required in this area. In this paper, we study the robustness of controllable subspace under random removal of edges and propose the robustness index for controllable subspace from one node as well as from a set of nodes. This work proposes a new centrality CSR (Controllable Subspace and robustness) to rank the important nodes of network based on optimal values of controllable subspace and robustness of controllable subspace. We also discuss the effects of exact robustness index and approximate robustness index on two factor: computational time, and accuracy. We show the analysis of top ten percent important nodes of various real networks with different centrality measures and CSR to compare the results. With the extensive experimentation on the real networks, CSR outperformed other centrality measures.
UvA-DARE (Digital Academic Repository) Learning Workflow Scheduling on Multi-Resource Clusters

—Workﬂow scheduling is one of the key issues in the management of workﬂow execution. Typically, a workﬂow application can be modeled as a Directed-Acyclic Graph (DAG). In this paper, we present GoDAG, an approach that can learn to well schedule workﬂows on multi-resource clusters. GoDAG directly learns the scheduling policy from experience through deep reinforcement learning. In order to adapt deep reinforce- ment learning methods, we propose a novel state representation, a practical action space and a corresponding reward deﬁnition for workﬂow scheduling problem. We implement a GoDAG prototype and a simulator to simulate task running on multi- resource clusters. In the evaluation, we compare the GoDAG with three state-of-the-art heuristics. The results show that GoDAG outperforms the baseline heuristics, leading to less average makespan to different workﬂow structures.
Design of Single-Phase Digital Phase Locked Loop Based on MVF-QSG

QSG (quadrature signal generator) is the critical part of single-phase PLL. In order to effectively reduce the influence of DC components and harmonics on orthogonal signals, a QSG based on multivariable filter (MVF) is proposed, and it’s proved to have good frequency adaptability. In this paper, the author first analyzes the transfer function of MVF, theoretically analyzes the mechanism of its attenuation of DC and harmonics, analyzes the influence of parameter Kf on MVF-QSG, uses MATLAB to simulate the working performance of MVF-QSG on DC components and harmonics under steady state and dynamic conditions and the working performance under the conditions of abrupt voltage frequency variation, and finally establishes a 3kW grid-connected inverter platform, verifies MVF-PLL's phase-locking effect under the conditions of power grid amplitude step and distortion by experiments, and proves the feasibility of the method.
SMC-based Data-driven Terminal ILC for Nonlinear Discrete-time Systems with Unavailable Intermediate Measurements

In this work, a new terminal iterative learning control is proposed by incorporating the sliding mode control (SMC) to address for the system uncertainties. The learning control system design and analysis is provided directly for a class of nonlinear and nonaffine discrete-time systems via a data-driven method by passing modeling steps. An iterative sliding surface with respect of terminal output error is developed at first. The terminal learning control is then attained by designing and minimizing an objective function with the sliding surface. The robustness of the proposed method is guaranteed by rigorously theoretical analysis and is further verified by simulation results provided in this work.
Model-free based adaptive RBF neural network control for a rehabilitation exoskeleton

This paper introduces a new control strategy for a lower limb exoskeleton used for gait rehabilitation. Rehabilitation exoskeleton can assist patients suffering from stroke or spinal cord injuries to recover from walking disability. This paper firstly conduct human walking gait analysis using OpenSim and then presents a novel design of a lower limb exoskeleton which has 3 degree of freedom of each leg and two of which are actuated. Then, considering the time variation, non-linearity and hysteresis of the exoskeleton control, this paper proposes a model-free based adaptive RBF neural network control strategy(MFANNC) used for trajectory tracking. Finally, the control strategy is validated through co-simulation based on a virtual prototype established in SolidWorks and SimMechanics. The comparative experiment results show that MFANNC has better tracking performance than traditional PID controllers in the exoskeleton control. Finally, this paper employs a new method to establish human-machine coupling model in OpenSim.
Mining Frequent Patterns for ECG Multi-label Data by FP-Growth Algorithm Based on Spark

Chinese Cardiovascular Disease Database (CCDD), which contains 12-Lead electrocardiogram (ECG) data and detailed features with diagnosis, include more than 270 categories of ECG-type data. Each record can correspond to multiple diagnosis results. However, most of the current methods are for single-label data, few people study multi-label ECG data, especially the correlation in ECG type labels. As one of the top ten classic algorithms for data mining, association rule mining algorithm can be used to discover interesting associations or related information between item sets in a large amount of data. The association rule mining algorithm on a single machine is difficult to adapt to big data analysis and processing tasks because of its space-time complexity and I/O cost. This paper makes full use of the powerful data processing capabilities of Spark’s cluster system, studies the frequent patterns for ECG big data.
Air quality PM2.5 prediction based on multi-model fusion

In order to solve the problem of low prediction accuracy of single model, this paper proposes a PM2.5 prediction method based on multi-model fusion. We have constructed multiple Least Angle Regression (LARS) models based on atmospheric data onto different weather conditions, and the prediction results are sent to BP neural network for decision-level fusion. Using the monitoring data of Beijing University of Technology in Chaoyang District, the atmospheric data of 2018-01-01 ∼ 2018-10-31 was selected as the experimental research object. The simulation result shows that the single model is not ideal for predicting PM2.5 concentration on complex weather conditions. The multi-model fusion method can effectively solve the error preference problem of single model and improve the prediction accuracy.
Research on Object Location Detection based on ROS Machine Vision

Aiming at the problem that the positioning accuracy of machine vision is not high enough and low speed, this paper studies the object location detection based on ROS machine vision. Under the ROS platform, point cloud images are collected by Kinect, and multiple targets in complex scenes are identified and extracted by using MATLAB and OpenCV. In the early stage, the original point cloud image was processed by filtering and noise reduction and the improved target recognition method of single point cloud image was obtained through identifying part and edge detection of vector mutation in point cloud image. The experimental results show that the method can effectively extract the objects in the point cloud image, retain more details and feature points, calculate the geometric size, and facilitate the positioning and extraction in practical applications.
Synchronization of complex networks with memristive neural network nodes via impulsive control

This paper is concerned with the issue for the exponential synchronization of complex networks. Considering the excellent approximation ability of the memristive neural network, the dynamic of each node in complex networks is approximated by a memristive neural network. Compared with exiting papers concerning with impulsive synchronization control of complex networks, a new kind of impulsive controller, in which the possible impulsive controller gain fluctuation is taken into consideration, is designed. Based on the time-varying Lyapunov functional approach, a sufficient condition is derived to guarantee the synchronization of complex networks. Finally, a numerical example is provided to demonstrate the effectiveness of the theoretical result.
Expectation Minimax Algorithm for the Two-Player Military Chess Game

The two-payer military chess is a typical incomplete information computer game. This paper proposes an expectation minimax algorithm for the two-player military chess. The principle structure of expectation minimax algorithm is given based on the traditional model, a chessman’s priority probability evaluation algorithm and a situation evaluation function for two-player military chess game are used for the implementation of the two-payer military chess game. Experiments show that the proposed expectation minimax algorithm is verified effective which can improve the performance of two-payer military chess game.
Multiple-Bounce Scattering of Tomo-SAR in Single-Pass Mode for Building Reconstructions

Building reconstruction is a very important application in SAR tomography in urban areas. Most of the current reconstruction methods are the combinations of the edge-based method and region growing method. Due to the lack of resolutions in the elevation direction, these current methods fail to detect buildings with low height. In this paper, a multiple-bounce model is proposed to analyze the feature of buildings. A reconstruction algorithm is then proposed to incorporate these extra features. Comparisons with other methods are maded in this paper to show the effectiveness and the advantages of our method.
Data Fusion of Air Combat Based on Reinforcement Learning

To improve the accuracy of data fusion system in modern air combat, an improved method based on reinforcement learning technique is developed in this paper. Firstly, the cubic spline interpolation is used for time alignment of multi-source data. Then, a reinforcement learning based data fusion method is proposed. The fusion accuracy reinforcement is realized by the error between observations and actual value. With an example, the simulation results indicate that the developed method is effective and feasible for the multi-sensor data fusion.
Kinship Understanding for a Family Photo

In social media analysis technology, kinship mining and understanding is an import issue. Previous works mainly focus on kinship verification between two people, while there has been less research on multiple person kinship recognition of the entire family. In this paper, we present a model to analyze the kinship of all members in a single family photo. Assume that a standard family relationship framework already exists. For an original photo, pre-processing and feature extraction are performed first. Then, we use kinship constraint rules to fill the framework all potential position with members in the photo. Next, calculate the genetic similarity information of the person to determine more specific position. At last, adjust and output the corresponding family tree. Extensive experimental results demonstrate the effectiveness of the proposed approach.
Rapid Detection of Inactive Channels during Multi-unit Intracranial Recordings

Intracranial microelectrode arrays (MEAs) are ubiquitous tools used for recording brain activity. During recordings, a fraction of channels in an array can be inactive for various reasons. The inclusion of data from inactive channels compromises the accuracy of any technique that pools data from multiple channels. Modern MEAs can contain hundreds to thousands of channels, and the inactive channels may change from experiment to experiment, rendering manual identification impractical. Therefore, we have developed a technique to automatically and rapidly identify active channels based on the presence of brain waves, quantified in terms of power spectral density from 4–100 Hz. We applied this technique to MEA recordings in a non-human primate as it performed 120 trials of a directional joystick task, and found that decoding accuracy (as measured by mean correlation coefficient) increased from 0.38 to 0.63 after inactive channels were identified and removed. Our method constitutes a novel technique that can be used to improve the fidelity of MEA output, for applications such as brain-machine interfaces and neurophysiology.
A New Class of Nonlinear Conjugate Gradient Method for Solving Unconstrained Minimization Problems

Formulas of new “nonlinear conjugate gradient (CG)”, satisfying “the sufficient descent condition”, are proposed to solve problems of unconstrained optimization. Wolfe line search was employed to establish the global convergence of the new algorithm proposed in this study. The implementation of a numerical comparison against the well-known method of FR and its variant denotes practical insights into the effectiveness of proposed algorithms.
Detection of GH Pituitary Tumors Based on MNF

In recent years, more and more machine learning algorithms have been applied to the field of medicine, but the medical data is often very few because of the high cost of data acquisition. Although machine learning has recently made significant breakthroughs in many areas, learning from small datasets remains a challenge for machine learning. In contrast, many metric learning algorithms allow novel examples to be rapidly assimilated, rather than suffering from serious forgetting. This paper aims at the detection of GH pituitary tumors under a small dataset, and inspired by the attention mechanism, the Metric Learning Based on Neural Features (MNF) is proposed by constructing a kernel that combines metric learning with deep convolutional neural networks. In addition, this paper also uses Long Short-Term Memory (LSTM) to further optimize the MNF.Finally, this paper also uses transfer learning to train the convolutional neural networks and use data augmentation methods based on ROI to improve the final results. Experiments show that MNF can be used under the small dataset, and get an accuracy of 92.3% in the detection of GH pituitary tumors.