Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty Quantification for SAE J2954 Compliant Static Wireless Charge Components

The present work aims at quantifying how, and how much, the uncertainties on the components and material parameters of a wireless power transfer (WPT) system for the static charge of electric vehicles affect the overall efficiency and functionality of the final produced device. With the aim of considering the perspective of a possible industrial developer, the parameters selected for the uncertainty quantification are chosen to be the capacitance values of the compensation capacitors and the electromagnetic material parameters used for the construction of the magnetic structure of a WPT system, i.e. the parameters of the elements to be purchased. The analysis is based on a standard system among the ones provided by the current SAE J2954 recommended practice.
Adaptive Mode Selection and Power Allocation for D2D Underlay Cellular Networks with Dynamic Fading Channel

This paper studies the adaptive mode selection and power allocation for D2D underlay cellular communication systems with the consideration of dynamic fading channel state information (CSI). It aims to maximize the throughput of D2D users while guaranteeing the required quality of service (QoS) of cellular networks. To solve this problem, the expression of throughput for D2D users under dynamic CSI is firstly derived. Subsequently, an adaptive mode selection and power allocation scheduling are obtained. Numerical simulations are provided to validate the analysis and throughput enhancement.
Towards Holographic Beam-Forming Metasurface Technology for Next Generation CubeSats

We present the concept of holographic beam forming metasurface antenna for CubeSat platforms at X-band frequencies. The proposed metasurface topology exhibits a flat-panel system layout, particularly desirable for integration with CubeSat platforms without a hardware-intense deployment mechanism for launch. It is shown that appropriately interacting the guided-mode (or reference-wave) with a metasurface layer makes it possible to realize the radiation pattern of interest as an objective function - similar to an optical hologram - without the need for dedicated phase shifting circuits. The full-wave simulations of the designed metasurface layer integrated with a 1U CubeSat reveals a high-fidelity beam-control with an aperture efficiency greater than 40% at 10 GHz operating frequency.
Theoretical and Applied Aspects of Use of a Filter with Integral Residuals to Control Dynamic Objects Under Disturbances

In this paper we propose a filtering algorithm, that, in contrast to the classical filtering algorithms, uses in its feedback not only the residuals between the measured and estimated coordinates, but also the integrals of these residuals. In addition to the phase coordinates estimation, the use of integrals from residuals allows to restore the unknown disturbances, acting on the control object. This, in turn, allows to apply the algorithm for real control objects in real operating conditions. External disturbances are treated as union of the external forces from the environment, and disturbances associated with inaccurate information about the model of the control object itself. In the work the analytical dependencies are obtained to evaluate feedback gains, which allows the use of this algorithm in real time with on-board computers. To be able to control the object in the real conditions, a law of control is proposed that includes the obtained estimates of the phase coordinates and unknown disturbances. This control algorithm is based on compensation of the total disturbance vector. The effectiveness of the proposed algorithms was tested on a model of an underwater vehicle that is described by a system of differential equations including the action of wave and spasmodic disturbances with incomplete information about the motion model.
Adaptive and Feature-Preserving Mesh Denoising Schemes Based on Developmental Guidance

Distinguishing among different kinds of features as well as noises on 3D mesh models is crucial for feature-preserving mesh denoising. This paper proposes to address this issue via in-depth analysis of the intermediate products of the denoising processes, and one framework is presented for raising adaptive and feature-preserving mesh denoising schemes. Firstly, by analyzing the changes of the facet normals during the denoising process, we propose the definition of developmental guidance, which helps to assess the current filtering status and predict the positions of feature and smooth regions. Then, by incorporating the guidance, we put forward one interpolation-based denoising scheme, which affords an efficient way to interpolate and recover different levels of features and is robust to severe noises. Besides, we also introduce the guidance to the optimization-based model, and the achieved global scheme is tested to be stable and robust to irregular samplings. Both the theoretical analysis and extensive experimental results on synthetic and real-world noises have demonstrated the attractive advantages of our whole framework, such as being adaptive, efficient, robust, feature-preserving, etc.
Temperature Estimation in a Magneto–Rheological Damper

Magneto-rheological dampers are employed in the automotive industry to control the vehicle dynamics by modulating the damping characteristics of the suspension system; these devices rely on a smart fluid which can change its viscosity when subjected to a magnetic field. The viscosity of this magneto-rheological fluid is significantly dependent on the operating temperature; this phenomenon is particularly critical in the automotive field since the working conditions span a wide range of temperatures and, furthermore, a commercial vehicle cannot be equipped to directly measure the temperature of the fluid. This article proposes a methodology for the temperature estimation which exploits the thermodynamic relationship between the resistance of the electrical circuit of the device and the temperature of the magneto-rheological fluid.
Reservoir Computing with Neuromemristive Nanowire Networks

We present simulations based on a model of self– assembled nanowire networks with memristive junctions and neural network–like topology. We analyze the dynamical voltage distribution in response to an applied bias and explain the network conductance fluctuations observed in our previous experimental studies. We then demonstrate the potential of neuromorphic nanowire networks as a physical reservoir by performing benchmark reservoir computing tasks. The tasks include sine wave nonlinear transformation, sine wave auto– generation and forecasting the Mackey–Glass chaotic time series.
Data Augmentation Using GANs for Crop/Weed Segmentation in Precision Farming

Farming robots need a fast and robust image segmentation module to apply targeted treatments, which require the ability to distinguish, in real time, between crop and weeds. Existing solutions make use of visual classifiers that are trained on large annotated datasets. However, generating large datasets with pixel-wise annotations is an extremely time-consuming task. In this work, we tackle the crop/weed segmentation problem by using a synthetic image generation method to augment the training dataset without the need of manually labelling the images. The proposed approach consists in training a Generative Adversarial Network (GAN), which can automatically generate realistic agricultural scenes. As a difference with respect to common GAN approaches, where the network learns how to reproduce an entire scene, we generate only instances of the objects of interest in the scene, namely crops. This allows to build a generative model that is more compact and easier to train. The generated objects are then placed into real images of agricultural datasets, thus creating new images that can be used for training. To evaluate the performance of the proposed approach, quantitative experiments have been carried out using different segmentation network architectures, showing that our method well generalizes across multiple architectures.
Synthesis of Plane Rectangular Array with Taking into Account the Mutual Influence of Radiators

The synthesis problem of plane rectangular array is considered in the variational formulation. The mutual influence of array’s radiators is considered in the process of solving the respective direct electrodynamical problem. The distribution of currents in the separate radiators is determined as the solution of the integral equation of Hallen’s type. The optimal excitation coefficients of radiators are determined as a result of minimization of used functional. The computational results confirm the effectiveness of the elaborated approach.
An Inhibition of Return Mechanism for the Exploration of Sensorimotor Contingencies

The modelling of cognition is fundamental to designing robots that are increasingly more autonomous. Indeed, researchers take inspiration from human and animal cognition in order to endow robots with the ability to learn and adapt to their environment. In specific cases, the robot has to find the right compromise between exploring the environment, or exploiting its own experience to advance its knowledge of a skill. Our approach considers a neurally-inspired model to learning sensorimotor contingencies based on exploration and exploitation. For the exploration, an inhibition of return mechanism is implemented that generates new actions. In this work, we investigate how the tuning of the inhibition of return affects the exploratory behavior. To do so, we set up an experiment where a 3D printed humanoid robot arm GummiArm has to learn how to move a baby mobile toy with only a visual feedback. The results demonstrate that the tuning of the inhibition of return influences the exploratory behavior, leading to a faster learning of sensorimotor contingencies as well as the exploration of a reduced motor space.
Analysis of Data Cleaning Techniques for Electrical Energy Consumption of a Public Building

Statistical Techniques and Artificial Intelligence are becoming much more a necessity in a fastened world rather than just a theoretical use case. In order to satisfy this need, the optimization process starts with data collecting and cleaning. The aim of this paper is to provide a short overview of the outlier detection methods and to explain the need for data cleaning in the field of energy consumption by analyzing the energetic profile data from the Technical University of Cluj-Napoca’s swimming complex. In the first and second parts of the article, a short overview of cleaning methods are presented. The third part compares the efficiency of the proposed methods. Finally, but not least the fourth part of the article is dedicated to conclusions and future work.
A Bayesian Model Averaging Method for Software Reliability Assessment

Due to the complexity and uncertainty of software failure behavior, the selection and application of reliability models can be challenging. To tackle the challenge of software reliability modeling and prediction, researchers have investigated abundant reliability models including combined models consisting of multiple models. This paper proposes to model and assess software reliability using the Bayesian model averaging method. The proposed modeling approach is based on Bayesian theory as well as selecting existing reliability modeling methods as candidate models. The posterior probability of a model being selected is obtained by Bayesian inference. The posterior probabilities are further used as weights to average the candidate base models. The key and difficulty of applying Bayesian model average method lies in the evaluation of weights. MCMC method is used to estimate the weights in BMA for software reliability modeling and assessment.
Control Design of a Single-Phase Inverter Operating With Multiple Modulation Strategies and Variable Switching Frequency

This article presents a control linearization technique for a single-phase single-stage inverter with multiple modulation strategies. The power topology is based on an flying capacitor multi-level (FCML) inverter, and the control is based on the plant inversion. The proposed technique allows decoupling the control of the input current and the output voltage, which simplifies the control in two ways: first, the control of both variables is independent and second, the controllers are independent of the modulation applied, thanks to the plant inversion. This control is implemented in an field-programmable gate array. Since the converter operates at variable frequency, two different data acquisition alternatives are explored: sampling at variable frequency (once per switching cycle) or sampling at constant frequency (higher than the switching frequency). The proposed control is validated by simulation and experimental results with a 1-kVA prototype.
Task Offloading Strategy Based on Reinforcement Learning Computing in Edge Computing Architecture of Internet of Vehicles

With the rapid increase of vehicles, the explosive growth of data flow and the increasing shortage of spectrum resources, the performance of existing task offloading scheme is poor, and the on-board terminal can’t achieve efficient computing. Therefore, this article proposes a task offload strategy based on reinforcement learning computing in edge computing architecture of Internet of vehicles. Firstly, the system architecture of Internet of vehicles is designed. The Road Side Unit receives the vehicle data in community and transmits it to Mobile Edge Computing server for data analysis, while the control center collects all vehicle information. Then, the calculation model, communication model, interference model and privacy issues are constructed to ensure the rationality of task offloading in Internet of vehicles. Finally, the user cost function is minimized as objective function, and double-layer deep Q-network in deep reinforcement learning algorithm is used to solve the problem for real-time change of network state caused by user movement. The results show that the proposed offloading strategy can achieve fast convergence. Besides, the impact of user number, vehicle speed and MEC computing power on user cost is the least compared with other offloading schemes. The task offloading rate of our proposed strategy is the highest with better performance, which is more suitable for the scenario of Internet of vehicles.
Broadband Circularly Polarized Microstrip Patch Antenna Using Circular Artificial Ground Structure and Meandering Probe

To enhance axial ratio (AR) bandwidth, this research proposes a circularly polarized (CP) single-fed microstrip patch antenna using a circular artificial ground structure (AGS) and meandering probe. To achieve broader AR bandwidth, the circular AGS is populated with rectangular unit cells and partially cut unit cells along the circular contour, while the meandering probe is used to improve the impedance bandwidth. Simulations are performed and results compared with that of conventional rectangular-AGS antenna. The simulation results show that the circular-AGS antenna, given 62 mm circular ground plane, achieves broader impedance (5.12 - 9.00 GHz and 54%), AR (5.21 - 8.27 GHz and 45%) and gain bandwidths (3.85 - 7.00 GHz and 58.06%), in comparison with the rectangular-AGS antenna (4.50 - 7.45 GHz and 49%; 4.52 - 7.42 GHz and 21%; and 4.00 - 6.80 GHz and 51.58% for impedance, AR and gain bandwidths). The circular-AGS antenna is capable of converting linear polarization in the off-axial ratio band into circular polarization. To verify, a circular-AGS antenna prototype is fabricated and experiments undertaken. The experimental impedance, AR and gain bandwidths of the circular-AGS antenna are 47.82% (5.17 - 8.42 GHz), 43.81% (5.24 - 8.17 GHz) and 60.74% (3.75 - 7.00 GHz). The proposed circular-AGS antenna can achieve broader AR bandwidth and is thus ideal for broadband CP applications. The novelty of this research lies in the use of circular AGS to effectively enhance AR bandwidth, as opposed to rectangular AGS which is conventionally used in CP polarizers.
Efficient Multicast Labelling for OpenFlow-Based Switches

Multicast is a one-to-many transmission, which can efficiently save bandwidth resources. It heavily relies on the support of network routers to replicate a packet to multiple egress ports. Large routers usually store local multicast labels (LMLs) for ports of packet replication. Owing to the limited number of LML entries, LMLs could be compressed to cause bandwidth waste. In an OpenFlow-based software-defined network, a switch can obtain the traffic volume of each passing-through flow. Our scheme of LML compression uses the information of traffic volume to reduce bandwidth waste.
Evaluating the Use of QoS for Video Delivery in Vehicular Networks

In a near future, video transmission capabilities in intelligent vehicular networks will be essential for deploying high-demanded multimedia services for drivers and passengers. Applications and services like video on demand, iTV, context-aware video commercials, touristic information, driving assistance, multimedia e-call, etc., will be part of the common multimedia service-set of future transportation systems. However, wireless vehicular networks introduce several constraints that may seriously impact on the final quality of the video content delivery process. Factors like the shared-medium communication model, the limited bandwidth, the unconstrained delays, the signal propagation issues, and the node mobility, will be the ones that will degrade video delivery performance, so it will be a hard task to guarantee the minimum quality of service required by video applications. In this work, we will study how these factors impact on the received video quality by using a detailed simulation model of a urban vehicular network scenario. We will apply different techniques to reduce the video quality degradation produced by the transmission impairments like (a) Intra-refresh video coding modes, (b) frame partitioning (tiles/slices), and (c) quality of service at the Medium Access Control (MAC) level. So, we will learn how these techniques are able to fight against the network impairments produced by the hostile environment typically found in vehicular network scenarios. The experiments were carried out with a simulation environment based on the OMNeT++, Veins and SUMO simulators. Results show that the combination of the proposed techniques significantly improves the robustness of video transmission in vehicular networks, paving the way, with a wise collaboration with other techniques, to achieve a robust video delivery system that supports multimedia applications in future intelligent transportation systems.
Analysis and Prospect for Application of Terahertz Band Technologies

In this paper, we analyze the application and prospect of terahertz band, especially in massive MIMO. Terahertz band has special channel characteristics which will bring potential challenges to massive MIMO. The shortcomings of the existing antenna technology in terahertz band and the next evolution direction of massive MIMO are analyzed in this paper. Based on these shortcomings and requirements, we propose key technical solutions of massive MIMO in terahertz band, including channel state information acquisition, transmission scheme design, beam management, beamforming and mobility management.
Effects of Chinese Medicine Polysaccharides Combined with Adoptive Immunotherapy on Ovarian Cancer

Objective:To study the killer activity of cytokine-induced killer(CIK) cells treated with Chinese medicine polysaccharides against the ovarian cancer cell line SKOV3 under different conditions,and explore the possibility of adoptive immunotherapy combined with Chinese medicine in the treatment of ovarian cancer.Method: The experiment were divided into four groups,such as CIK group,PBDC-CIK group,SKOV3Ag-PBDC-CIKgroup and SKOV3Ag-ADC-CIKgroup.CIK cells derived from the peripheral blood(PB) were induced by cytokine and grifola polysaccharide(100 mg·L-1),pachyman(100 mg·L-1) and astragalan(100 mg·L-1). while dendritic cells(DC) derived from PB and ascites by SKOV3 tumor cell lysate.The cytotoxic activities of CIK group,PBDC-CIK group,SKOV3Ag-PBDC-CIK group and SKOV3 Ag-ADC-CIK group against SKOV3 were analyzed,as well as the proliferation of CIK cells between groups.Result: ①The proliferation rate of CIK cells co-cultured with DC significantly increased. (P0.01) ②CIK cells induced by Chinese medicine polysaccharides were cytotoxic to SKOV3 cells,which increased with effector: target(E∶T) ratios,which were(9.12±0.21)%、(10.15±0.27)%、(11.20±0.34)% and(12.73±0.43)%(P0.05),③ The cytotoxic activities of SKOV3Ag-PBDC-CIK group against SKOV3 cells were significantly higher than that of both DC-CIK group and CIK groups(P0.05,P0.01). ④ There was no significant difference in killing activity between SKOV3Ag-PBDC-CIK group and SKOV3Ag-ADC-CIK group.Conclusion: DC pulsed with tumor lysate antigens of SKOV3 could enhance the proliferation and cytotoxicity of CIK cells induced by Chinese medicine polysaccharides.And DC derived from ascites could serve as an important source in adoptive immunotherapy.
Study on Daqu Quality Standards (III)——Characteristic Indexes of Daqu Aroma-producing Capability

Daqu,a microbial product containing multiple enzymes and bacterial species,its aroma-producing substances mainly come from proteins and fat of daqu-making materials and the degradation of amylum and those substances are composed of amino acids,fatty acid,polyose and its polymers etc.The characteristic indexes of Daqu aroma-producing capability include ammoniacal nitrogen and the specific consumption of amylum. (Tran.by YUE Yang)
The Polyhedral Methodology of Formalization of Discrete Terminal Control Processes with Resource Constraints

the polyhedral methodology for solving problems of discrete terminal control by dynamic objects under resource constraints conditions is discussed. It is based on formalism of multidimensional optimization and three theoretical constructions like predictive extrapolation of controlled object’s movements, the immersion principle of the input optimization problem to a number of more simple computational problems algorithmically, and also the mechanism of extreme guidance. Due to polyhedral formalization, all the considered optimization problems have the polyhedral structure and are reduced to linear programming problems. As an example, the problem of advanced impulse control for stabilization of the lateral hovercraft’s movement is considered.
Robust Tensor Factorization for Color Image and Grayscale Video Recovery

Low-rank tensor completion (LRTC) plays an important role in many fields, such as machine learning, computer vision, image processing, and mathematical theory. Since rank minimization is an NP-hard problem, one strategy is that it is converted into a convex relaxation tensor nuclear norm (TNN) that requires the repeated calculation of time-consuming SVD, and the other is to convert it into some product of two smaller tensors that are easy to fall into the local minimum. In order to overcome the above shortcomings, we propose a robust tensor factorization (RTF) model for solving LRTC. In RTF, the noisy tensor data with missing entries is decomposed into low-rank tensor and noisy tensor, and then the low-rank tensor is equivalently decomposed into t-products (essentially vectors convolution) of two smaller tensors: orthogonal dictionary tensor and low-rank representation tensor. Meanwhile, the TNN of low-rank representation tensor is adopted to characterize the low-rank structure of the tensor data for preserving global information. Then, an effective iterative update algorithm based on the alternating direction method of multipliers (ADMM) is proposed to solve RTF. Finally, numerical experiments on image recovering and video completion tasks show the effectiveness of the proposed RTF model compared with several state-of-the-art tensor completion models.