Miracle-dz/UniFAR-FTUs huggingface dataset:
Overall Locomotion Control of a Quadruped Robot with Running Trot Gait

With the four-leg flying phase, the locomotion control of a quadruped robot with running trot gait is much more challenging than normal trot gait. This work firstly decouples the robot’s 6-DOF postures & poses, splitting into five DOFs of a 3D five-linkage mechanism and one DOF of a planner inverted pendulum model. Based on the established approximate kinetics of the 3D five-linkage mechanism, a control method based on virtual spring-damper models is proposed fundamentally, along with a pertinent open-loop hopping controller for the vertical hopping motion, which achieves the stable control on the 3-DOF postures, forward velocity and vertical hopping motion of the torso during the stance phase. The lateral velocity is controlled by selecting the foot positions of the planner inverted pendulum model during the flying phase. A novel event-driven-based finite state machine is presented to solve the sequential switching problem on the torso’s control mode between the stance and flying phase. Final simulation results suggest the feasibility and effectiveness of the proposed locomotion control method.
F1-ECAC: Enhanced Evolutionary Clustering Using an Ensemble of Supervised Classifiers

Clustering is an unsupervised learning technique used in data mining for finding groups with increased object similarity within but not between them. However, the absence of a-priori knowledge on the optimal clustering criterion, and the strong bias of traditional algorithms towards clusters with a specific shape, size, or density, raise the need for more flexible solutions to find the underlying structures of the data. As a solution, clustering has been modeled as an optimization problem using meta-heuristics for generating a search space to favor groups of any desired criterion. F1- ECAC is an evolutionary clustering algorithm with an objective function designed as a supervised learning problem, which evaluates the quality of a partition in terms of its generalization degree, or its capability to train an ensemble of classifiers. This algorithm is named after its previous version, ECAC (Evolutionary Clustering Algorithm Using Supervised Classifiers), considering its main point of difference, which is the inclusion of the F1-score instead of the Area Under the Curve metric in the objective function. F1- ECAC shows a significant increase in performance and efficiency to ECAC and is highly competitive to state-of-the-art clustering algorithms. The results demonstrate F1-ECAC’s benefits in usability in a wide variety of problems due to its innovative clustering criterion.
D-Net: A Generalised and Optimised Deep Network for Monocular Depth Estimation

Depth estimation is an essential component in computer vision systems for achieving 3D scene understanding. Efficient and accurate depth map estimation has numerous applications including self-driving vehicles and virtual reality tools. This paper presents a new deep network, called D-Net, for depth estimation from a single RGB image. The proposed network can be trained end-to-end, and its structure can be customised to meet different requirements in model size, speed, and prediction accuracy. Our approach gathers strong global and local contextual features at multiple resolutions, and then transfers these to high resolutions for clearer depth maps. For the encoder backbone, D-Net can utilise many state-of-the-art models including EfficientNet, HRNet and Swin Transformer to obtain dense depth maps. The proposed D-net is designed to have minimal parameters and reduced computational complexity. Extensive evaluations on the NYUv2 and KITTI benchmark datasets show that our model is highly accurate across multiple backbones, and it achieves state-of-the-art performance on both benchmarks when combined with the Swin Transformer and HRNets.
BiCord: Bidirectional Coordination among Coexisting Wireless Devices

Cross-technology interference is a major threat to the dependability of low-power wireless communications. Due to power and bandwidth asymmetries, technologies such as Wi-Fi tend to dominate the RF channel and unintentionally destroy low-power wireless communications from resource-constrained technologies such as ZigBee, leading to severe coexistence issues. To address these issues, existing schemes make ZigBee nodes individually assess the RF channel's availability or let Wi-Fi appliances blindly reserve the medium for the transmissions of low-power devices. Without a two-way interaction between devices making use of different wireless technologies, these approaches have limited scenarios or achieve inefficient network performance. This paper presents BiCord, a bidirectional coordination scheme in which resource-constrained wireless devices such as ZigBee nodes and powerful Wi-Fi appliances coordinate their activities to increase coexistence and enhance network performance. Specifically, in BiCord, ZigBee nodes directly request channel resources from Wi-Fi devices, who then reserve the channel for ZigBee transmissions on-demand. This interaction continues until the transmission requirement of ZigBee nodes is both fulfilled and understood by Wi-Fi devices. This way, BiCord avoids unnecessary channel allocations, maximizes the availability of the spectrum, and minimizes transmission delays. We evaluate BiCord on off-the-shelf Wi-Fi and ZigBee devices, demonstrating its effectiveness experimentally. Among others, our results show that BiCord increases channel utilization by up to 50.6% and reduces the average transmission delay of ZigBee nodes by 84.2% compared to state-of-the-art approaches.
On State-based Evolutionary Games

The modeling and control of state-based evolutionary games(SEGs) are investigated via algebraic state space approach, which converts the game systems to equivalent classical multi-valued logical systems. Two kinds of state-based evolutionary games are considered: pure strategy and mixed strategy time-varying evolutionary games.An auxiliary system is proposed, which is time-invariant. The trajectories of the auxiliary system and the original one are compared. Certain properties of time-varying SEGs are obtained. Particularly, the stability and stabilization problems have been studied in detail. Some numerical examples are also demonstrated.
Connectivity preserving design strategy for distributed adaptive cooperative control of networked uncertain nonholonomic mobile robots with unknown control directions

This brief addresses a distributed adaptive asymptotically synchronous tracking problem based on guaranteed connectivity for networked uncertain nonholonomic mobile robots (NMRs) with unknown control directions. First, a radial basis function (RBF) neural network is used to approximate the unknown nonlinear functions, and a distributed nonlinear error surface is introduced to achieve synchronous tracking between NMRs and maintain the initial connectivity patterns. Then, a conditional inequality that allows multiple piecewise Nussbaum functions to achieve robust control is proposed to solve the problem of unknown control directions. Moreover, the proposed protocol ensures that all signals in the closed-loop system are globally bounded and the tracking errors converge asymptotically to zero. Finally, a simulation example verifies the effectiveness of the proposed adaptive laws.
A Real-Life Machine Learning Experience for Predicting University Dropout at Different Stages Using Academic Data

High levels of school dropout are a major burden on the educational and professional development of a country’s inhabitants. A country’s prosperity depends, among other factors, on its ability to produce higher education graduates capable of moving a country forward. To alleviate the dropout problem, more and more institutions are turning to the possibilities that artificial intelligence can provide to predict dropout as early as possible. The difficulty of accessing personal data and privacy issues that it entails force the institutions to rely on the Academic Data of their students to create accurate and reliable predictive systems. This work focuses on creating the best possible predictive model based solely on academic data, and accordingly, its capacity to infer knowledge must be maximised. Thus, Feature Engineering and Instance Engineering techniques such as dealing with redundancy, significance of the features, correlation, cardinality features, missing values, creation or elimination of features, data fusion, removal of unuseful instances, binning, resampling, normalisation, or encoding are applied in detail before the construction of well-known models such as Gradient Boosting, Random Forest, and Support Vector Machine along with an Ensemble of them at different stages: prior to enrolment, at the end of the first semester, at the end of the second semester, at the end of the third semester, and at the end of the fourth semester. Through the construction of these predictive models that serve as inputs to a decision support system, the application of effective dropout prevention policies can be applied.
Application of Energy Shaping of Port-Hamiltonian System to Chaos Synchronization

This paper investigates the issue of chaos synchronization of coupled dynamical systems by the use of energy shaping in port-Hamiltonian systems. A energy-based control scheme is developed to realize chaos synchronization in term of port- Hamiltonian systems, and the asymptotical stability for the closed-loop Hamiltonian system is derived by using the LaSalle’s invariance principle. It is shown that the developed energy-based synchronization scheme is relatively simple and easy to implement compared with some recent literatures. Finally, two simulation examples are performed to demonstrate the effectiveness of the proposed synchronization strategy method.
Improvement of Non-frontal Face Skin Color Model Based on YCgCr Color Space

In non-frontal situations, the image generally does not contain complete facial information, and skin color is the most important feature for detecting human faces. In order to improve the detection effect of non-frontal face skin color and applicable scenarios, this paper first analyzes and compares the YCbCr and YCgCr color spaces. Then, by comparing the fixed threshold method, the Gaussian skin color model method and the Otsu method, it is found that the Gaussian skin color model method is the most effective method, but still needs improvement. This paper proposes the Otsu method to optimize the threshold calculation in the Gaussian skin model, and the nonlinear segmented ellipse model reduces the impact of brightness. Experimental results show that this method can eliminate skin-like backgrounds more effectively, and can still maintain a better detection rate under dim lighting, and has a more ideal detection effect.
Unmanned Vehicle 3D Lidar Point Cloud Segmentation

The solid state lidar is one of important tools for environment sensing of unmanned platform, and has been widely used in vehicle environment modeling. However, due to the low resolution, sensitive noise and complex scene, the effective segment of the whole scene is a key issue during unmanned platform data processing. In the paper, an improved 3D point clouds segmentation method is proposed for multi-line lidar in practice. After extraction building façade based on curvature segmentation, weighted Euclidean clustering is utilized to classify buildings and vegetation bodies. Then, experiments are performed on the real data acquired by the unmanned platform and the effectiveness of the proposed method is verified by comparing with the commonly used building growth segmentation algorithm.
Bidirectional Isolated DC/DC Dual-Active-Bridge Converters Optimum Soft-Switching Control Method for Electrical Vehicle Applications

Electrical vehicles (EV) are appropriate alternative to the conventional internal combustion engines. The conventional grid-connected EV chargers consist of an AC/DC power-factor-correction (PFC) converter, and an isolated DC/DC converter connected to the battery. The isolated DC/DC converter can be used as a bidirectional converter providing power to the grid in the peak hours. During the entire charging process of the EV battery, the voltage varies significantly, and the DC/DC converter should operate under wide input and output voltage range. In this paper, the bidirectional dual-active-bridge (DAB) converter with both full-bridge and half-bridge topologies is studied. In the light-load condition, the zero-voltage-switching (ZVS) cannot be attained. Thus, a switching control method with emphasis on variable frequency and duty-cycle (VFD) is suggested at light-loads to improve overall efficiency of the DAB converters. Simulation results verifies the accuracy of this method.
Online Optimal Transmission Scheduling of an Energy Harvesting Sensor for Remote State Estimation

We investigate an online optimal sensor transmission scheduling problem for remote state estimation. We consider the energy harvesting sensor, which can absorb external energy and use the energy to transmit the local state estimates to the remote estimator over an unreliable wireless channel. Furthermore, we consider that the sensor has two different transmission energy levels, where different energy levels correspond to different data reception ratios. Considering the trade-off between estimation performance and energy consumption, we hope to design an optimal transmission policy that minimizes the convex combination of the estimation error covariance and the energy for data transmission. We formulate the optimization problem as a Markov decision process, which can be solved by dynamic programming. Then, we introduce the concept of submodularity to prove that there is an optimal threshold policy on the error covariance, given sensor battery and collected energy. Eventually, simulations are given to illustrate the aforementioned results.
Vehicle State Estimation Based on Recurrent Neural Network

For vehicle state estimation, a new method based on recurrent neural network is proposed to improve the accuracy of the estimation and to simplify the algorithm. The method not only eliminates the necessity of constructing complex vehicle models, but is also suitable for non-linear systems and has good convergence. Through simulations under different operating conditions, the results show that this new method can accurately estimate many parameters that are difficult to estimate, such as the vehicle's sideslip angle, road adhesion coefficient, etc.
Re-architecting Distributed Block Storage System for Improving Random Write Performance

In cloud ecosystems, distributed block storage systems are used to provide a persistent block storage service, which is the fundamental building block for operating cloud native services. However, existing distributed storage systems performed poorly for random write workloads in an all-NVMe storage configuration, becoming CPU-bottlenecked. Our roofline-based approach to performance analysis on a conventional distributed block storage system with NVMe SSDs reveals that the bottleneck does not lie in one specific software module, but across the entire software stack; (1) tightly coupled I/O processing, (2) inefficient threading architecture, and (3) local backend data store causing excessive CPU usage. To this end, we re-architect a modern distributed block storage system for improving random write performance. The key ingredients of our system are (1) decoupled operation processing using non-volatile memory, (2) prioritized thread control, and (3) CPU-efficient backend data store. Our system emphasizes low CPU overhead and high CPU efficiency to efficiently utilize NVMe SSDs in a distributed storage environment. We implement our system in Ceph. Compared to the native Ceph, our prototype system delivers more than 3x performance improvement for small random write I/Os in terms of both IOPS and latency by efficiently utilizing CPU cores.
Very Deep Learning-Based Illumination Estimation Approach With Cascading Residual Network Architecture (CRNA)

For the imaging signal processing (ISP) pipeline of digital image devices, it is of high significance to remove undesirable illuminant effects and obtain color invariance, commonly known as ‘computational color constancy’. Achieving the computational color constancy requires going through two phases: the illumination estimation, which will be the primary focus of this work, and the human visual perception-based chromatic adaptation. At the first phase, illumination estimation is to predict RGB triplets, the numeric representations of incident illuminant colors, by calculating the values of image pixels. How much the network can increase its estimation accuracy is a key to realizing computational color constancy. With recent advances in deep learning (DL), a lot of deep learning-based approaches have been suggested, bringing higher accuracy to computer vision applications, but there are still quite a few obstacles to overcome such as instability of learning. In an attempt to address this ill-posed problem in the illumination estimation space, this article presents a novel deep learning-based approach, the Cascading Residual Network Architecture (CRNA), which incorporates the ResNet and cascading mechanism into the deep convolutional neural network (DCNN). The cascading mechanism enables the proposed network to restrain from suddenly varying in size, serves to mitigate learning instability, and accordingly reduces the quality degradation. This is attributed to the ability of the cascading mechanism that fine-tunes the pre-trained DCNN. Considerable amounts of datasets and comparative experiments highlight that the proposed approach delivers more stable and robust results and imply the potential for generalization of the proposed approach across deep learning applications.
Simulation-based Model Learning for Optimization of Building Energy Management

In this paper, we present a reinforcement learning based method for building energy management, which introduces a transition-model learning from building energy simulation software. The transition-model receives state from simulation model and returns cost-to-go to control strategy. The method is intended to combat two main gaps when integrating simulation program and optimization algorithm in building energy management, which are nonanalytical and nonlinear computation for simulation program and requirement of optimization algorithms for system dynamics. The method is realized on EnergyPlus, which interact with python program in functional mock-up interface (FMI) standard. The numerical test results show that the transition-model introduced can estimate cost-to-go to a certain extent and significantly improve efficiency of control.
Improved Visual Odometer Based on Two-stream Convolutional Neural Networks

The purpose of the visual odometer is to use the image information input by the camera to estimate the motion information of the camera. As a positioning method, it has been widely used in fields such as autonomous driving and mobile robots in recent years. Because of the disadvantages of traditional visual odometers such as cumbersome implementation process and complicated calculation, the improvement of visual odometers has always been a hot research topic. In this paper, a two-stream convolutional neural networks is used, while RGB images and depth images are input to the model for training, and the Inception module is used to improve the convolutional layer and reduce the number of parameters. At the same time, an attention model is added to the convolutional layer to improve the network’s recognition of image features and the robustness of the system. Finally, the model was trained and tested on the KITTI data set. The results show that the model we proposed has achieved a certain improvement compared with VISO2-M and SfMLearner.
Coplanar capacitor probes design for a moisture soil sensor operating at high frequencies

In this work, we study the suitability of a coplanar capacitor (CPC) for a low-cost soil volumetric water content (VWC) sensor in the context of vegetable growing automation. As an operating frequency above 50MHz is desired for better measurement accuracy, its compatibility with a high-frequency oscillating circuit is verified through the extraction of its electrical parameters after electromagnetic simulations, such as capacitance, self-resonance frequency (SRF), and parasitic inductance, while varying the conductive layer dimensions, the length of feeding traces, the number of conductive layers, and the number of vias. Moreover, a 137MHz SRF is measured in an implemented single-layer CPC while buried in wet soil. These results should serve as guidance during the design of the complete acquisition circuit.
Essential Oil Composition of Tilia cordata Flowers

Abstract The purpose of the study was to analyze the chemical composition of the essential oil (EO) obtained from the Tilia cordata flowers from collected (“W”) and two commercially available products (“F” and “K”). Hydro-distillation of essential oils was carried out according to the Polish Pharmacopoeia VIII. Qualitative and quantitative analysis of the essential oil was carried out by GC/MS and GC/FID. The content of essential oils in the analyzed samples was 0.07 % v/w for “K” and 0.08 % v/w for “F” and “W”. In the essential oil obtained from commercial sample “F”, 71 compounds was detected, while in the EO hydro-distilled from sample “K” and “W”, 50 and 49 compounds were identified, respectively. GC/MS analysis showed that aliphatic hydrocarbons are the main fraction occurring in the analyzed essential oils. The dominant components present in all EOs included: 6,10,14-trimethyl-2-pentadecanone (11-20 %), tricosane (6-17 %), heneicosane (3-9 %), nonanal (7 %), octadeca-9,12-dienoic acid (7 %). Among terpenoids, linalool (4 %), menthone (3 %), borneol (2 %), menthol (3 %), terpinen-4-ol (1 %), p-cymene-8-ol (2 %), carvone (6 %), E-anethole (8 %), thymol (3 %) of E-β-damascenone (4 %), methyl eugenol (4 %), neryl acetone (4 %), δ-amorphene (2 %), lilial (2 %), caryophyllene oxide (1 %), humulene epoxide II (1 %), epi-α-muurolol (1 %), kaurene (4 %) were identified.
Event-Triggered Fault-tolerant Model Predictive Control of Nonlinear Multi-agent System with Time Delay and Parameter Uncertainty

This paper proposes a compound control method combining with robust model predictive control (RMPC) method and the event-triggered mechanism for a class of nonlinear multi-agent system (NMAS) with time delay, uncertainties, actuator faults and input constraints. Specifically, at each sampling time, the control action is calculated by solving an online constrained optimization problem, using Lyapunov-Krasovskii functions to design state feedback control laws to ensure that the consensus error of the NMAS tends to zero and the event-triggered mechanism can save communication resources. Finally, the effectiveness of the proposed event-triggered fault-tolerant RMPC method is verified by an example.
A new flavonoid from Ophiopogon confertifolius

From the methanolic extract of the roots of Ophiopogon confertifolius N. Tanaka (Convallariaceae) a new flavonoid named ophiofolius A ( 1 ) and two known compounds ( 2, 3 ) were isolated. Their structures were elucidated as 6,8-dimethyl-4’methoxy-5,7,3’,5’-tetrahydroxyflavanone (1), 6,8-dimethyl-3’,4’-dimethoxy-5,7,5’-trihydroxyflavanone ( 2 ), and 6-methoxy-5,7-dihydroxyflavanone ( 3 ) by the spectroscopic evidences (1D NMR, 2D NMR, ESI-MS).