Miracle-dz/UniFAR-FTUs huggingface dataset:
Faster BiSeNet: A Faster Bilateral Segmentation Network for Real-time Semantic Segmentation

Since the rising demand for dense estimation tasks in mobile applications, real-time semantic segmentation is increasingly desirable. Our work proposes a faster bilateral segmentation network (Faster BiSeNet) based on BiSeNetV2, which promotes feature fusion of the spatial and semantic branch with the more compact structure to improve real-time performance. Our light-weight design enhances the mutual connection between two branches in the shallow and deep layers by 1) Shallow feature sharing: using the simple linear operation to transform shallow features of the spatial branch as those needed in the semantic branch; 2) Deep feature aggregation: introducing a Gated Guided Aggregation Layer to guide appropriate spatial information to supplement the missing details of the semantic branch through gating mechanism, which avoids using unnecessary convolutions to select the aggregated features; 3) Auxiliary edge loss: making the gated output of the spatial branch focus on important boundary-related information, which facilitates the fusion of valuable edge information into the predicted results. Extensive experiments demonstrate that our approach obtains better realtime performance against a few state-of-the-art real-time semantic segmentation methods. Compared with BiSeNetV2, Faster BiSeNet respectively achieves 72.8% $(\uparrow 0.2\%)$ I, 74.5% $(\uparrow 2.1\%)$) Mean IoU at the inference of 187 FPS $(\uparrow 31$ FPS), 154 FPS $(\uparrow 29.5$ FPS) on the challenging Cityscapes test set and CamVid validation set on one NVIDIA GeForce GTX 1080Ti card. This demonstrates that our method achieves the competitive segmentation performance at real-time speed.
Copper Large-Scale Grain Growth by UV Nanosecond Pulsed Laser Annealing

UV nanosecond pulsed laser annealing (UV NLA) enables both surface-localized heating and short timescale high temperature processing, which can be advantageous to reduce metal line resistance by enlarging metal grains in lines or in thin films, while maintaining the integrity and performance of surrounding structures. In this work UV NLA is applied on a typical Cu thin film, demonstrating a mean grain size of over 1 μm and 400 nm in a melt and sub-melt regime, respectively. Along with such grain enlargement, film resistivity is also reduced.
Surface Defect Detection of Wet-Blue Leather Using Hyperspectral Imaging

Detection of surface defects on wet-blue leather is much more challenging than raw-hide leather. Since wet-blue leather turns blue and contains moisture after pre-treatment, it is a semi-product of the cowhide processing. At present, the defect detection of wet-blue leather is mostly carried out manually and is time-consuming and labor-intensive for professional inspectors. This paper is the first to use hyperspectral imaging (HSI) to implement the surface inspection of five wet-blue leather defects including brand masks, rotten grain, rupture, insect bites, and scratches in the pixel level detection. Hyperspectral Leather Defect Detection Algorithm (HLDDA) including Hyperspectral Target Detection (HTD) and Deep Learning (DL) techniques was proposed in this paper. In HTD, Weighted Background Suppression Constrained Energy Minimization (WBS-CEM) and WBS-Hierarchical CEM (WBS-hCEM) were developed in this paper by using weighting to suppress the background and enhance the contrast between the target and background. Experimental results showed that the overall performance of WBS was better than the original CEM. In the DL part, 1D-Convolutional Neural Network (CNN), 2D-Unet and 3D-UNet architectures were designed to segment defect areas. For various characteristics of defects, 1D-CNN emphasizes on defects with spectral features, 2D-Unet emphasizes on defects with spatial features, and 3D-Unet can simultaneously process spatial and spectral information in HSI. The experimental results verified that the proposed HLDDA could effectively quantify and estimate the size of the defect, thereby accelerating the leather inspection process by professional inspectors and develop an automated leather grading system towards Industry 4.0.
Vehicle Pairing, Spectrum Assignment and Power Control for Non-Safety Critical Services in Heterogeneous Vehicular Networks

To accommodate the ever-increasing demands for non-safety critical services in vehicular networks, heterogeneous vehicular networks (HVNs) have been proposed and drawn considerable attention. It is known that the performance of HVNs suffers from a shortage of spectrum resource, and the relatively low utilization of licensed spectrum makes the spectrum shortage even worse. To cope with this, by introducing unlicensed spectrum into HVNs, we investigate the spectrum efficiency (SE)-oriented collaborative transmission. The SE for HVNs depends highly on the channel state information (CSI) of the effective communication links which are normally determined by the vehicle pairing. So, we first propose a joint social-physical domain vehicle pairing algorithm (JSPD-VPA), and then formulate an optimization problem to maximize the SE of vehicle-to-everything (V2X) communications while guaranteeing the quality of services (QoS) requirements of cellular users in licensed spectrum and users in unlicensed spectrum. Considering that the formulated problem is NP-Hard problem, to solve it, the formulated problem is decomposed into two subproblems, i.e., spectrum assignment subproblem and power control subproblem. An optimal hypergraph coloring algorithm is proposed to solve the spectrum assignment subproblem, and a low-complexity three-dimensional geometry approach is developed to solve the power control subproblem. Simulation results reveal that the proposed vehicle pairing, spectrum assignment and power control algorithm (VP-SA-PCA) can greatly enhance the SE and outperforms the baselines around 7%-37% within a certain range. Moreover, the superiority of the proposed JSPD-VPA has been validated.
External Magnetic Field Minimization for the Integrated Magnetics in Series Resonant Converter

The resonant dc–dc converter is preferred for high-efficiency applications due to its less switching loss and better electromagnetic interference (EMI) characteristics. However, in a wide range of steady-state voltage gain applications, such as the on-board charger application, the wide output voltage range will lead to a bulky resonant inductor, which will decrease the power density. Magnetic integration of the inductor and transformer is an effective way to reduce the total magnetic volume. However, the traditional integrated magnetics of transformer and inductor will result in high magnetic field intensity in the external space nearby the integrated magnetic component. This external magnetic field will cause large eddy current losses in the neighboring metal heatsink and serious EMI issues. In order to deal with this problem, an integrated winding structure with a negligible external magnetic field is proposed in this article. By rebuilding the external winding segments with a better coupling coefficient, the external magnetic field intensity is canceled without efficiency and size sacrifices. Detailed analysis and design procedures of the proposed structure are presented. A 6.6-kW dc–dc prototype is built to verify the cancelation effect of the magnetic field intensity in the neighboring external space.
Energy Harvesting and Resource Allocation for Cache-Enabled UAV Based IoT NOMA Networks

This paper investigates a novel joint content caching and energy harvesting scheme to enhance unmanned aerial vehicle (UAV) communications in Internet of Things non-orthogonal multiple access (NOMA) network, where a UAV is acted as an aerial relay to serve users on demand. In particular, a neoteric gravitational search-based multi-constraint optimization algorithm is designed to maximize the throughput of the served users by jointly optimizing power allocation, energy harvesting, and time scheduling schemes. The algorithm engages two main characteristics: 1) classification of multiple constraints, which is for reducing the complexity of the problem solving process due to multiple iterations, and 2) design of force sets, which aims to enhance the global search for the optimal solution at the feasible region boundary. Finally, simulations are provided to show the effective convergence and superiority of the proposed algorithm.
Spatial Correlation Modeling for Optimal Power Flow With Wind Power: Feasibility in Application of Superconductivity

The integration of wind power into the power system causes difficulties in system operations due to its associated uncertainties. Spatial correlation is one aspect of uncertainties and derives from geographically dispersed distribution. However, spatial correlation is ignored by most studies, which decreases the modeling accuracy. In this paper, a high-dimensional t-copula model is first proposed to preserve spatial correlation characteristics. Through this model, adequate simulated data are sampled, which forms scenario generation. K-means clustering method is further used to reduce the number of scenarios generated. A case of 4 wind farms selected from AEMO dataset has been implemented to illustrate the establishment of the spatial correlation model. Then, a modified IEEE 24-bus test system is utilized to conduct optimal power flow (OPF) with and without the consideration of spatial dependence. The comparison results show that with spatial correlation accessing, the generation cost would have a certain increase. This implies, if the spatial correlation is ignored, the overall cost of OPF is underestimated. Furthermore, with the penetration level of wind power increasing, a lager cost gap is obtained, thus proving there exist benefits with the inclusion of spatial correlation during system modeling.
Regime-Switched Neural Networks: Flow Stress Modeling Strategy of 310s Stainless Steel During Hot Deformation

This study examines the high temperature deformation and constitutive modeling of flow stress for 310s stainless steel. To this end, hot tensile experiments were conducted under the temperatures of 700° and 800° at the strain rates of 0.0002/s, 0.002/s, and 0.02/s. Flow stress was modeled using the Arrhenius type constitutive equation and neural network approach. Specifically, Regime-Switched Neural Networks (RSNN), a set of neural networks with switching, has been newly proposed as a better predictive model for flow stress. The modeling performance of the RSNN model was evaluated through a comparison with traditional Arrhenius-type constitutive equations and the single neural network model. The results showed that the accuracy of the proposed RSNN was substantially higher than those of the existing Arrhenius-type equations, and the prediction performance was therefore significantly improved. In addition, the accuracy of the proposed RSNN was improved by approximately more than 24% in comparison with the existing global model—a single neural network—thus confirming the superiority of the proposed switching model.
React‐on‐Demand (RoD) Fabrication of Highly Conductive Metal–Polymer Hybrid Structure for Flexible Electronics via One‐Step Direct Writing or Printing

As a fast prototyping technique, direct writing of flexible electronics is gaining popularity for its low‐cost, simplicity, ultrahigh portability, and ease of use. However, the latest handwritten circuits reported either have relative low conductivity or require additional post‐treatment, keeping this emerging technology away from end‐users. Here, a one‐step react‐on‐demand (RoD) method for fabricating flexible circuits with ultralow sheet resistance, enhanced safety, and durability is proposed. With the special functionalized substrate, a real‐time 3D synthesis of silver plates in microscale is triggered on‐demand right beneath the tip in the water‐swelled polyvinyl alcohol (PVA) coating, forming a 3D metal–polymer hybrid structure of ≈7 µm with one single stroke. The as‐fabricated silver traces show an enhanced durability and ultralow sheet resistance down to 4 mΩ sq−1 which is by far the lowest sheet resistance reported in literatures achieved by direct writing. Meanwhile, PVA seal small particles inside the film, adding additional safety to this technology. Since neither nanomaterials nor a harsh fabrication environment are required, the proposed method remains low cost, user friendly, and accessible to end users. With little effort, the RoD approach can be extended to various printing systems, offering a particle‐free, sintering‐free solution for high‐resolution, high‐speed production of flexible electronics.
Saliency Guided Discriminative Learning for Insect Pest Recognition

Recognition of insect pests in the wild plays a key role in crop protection. Large-scale pest recognition in natural scenes is extremely challenging due to significant intra-class variation and small inter-class variation within sub-categories. Existing works typically use state-of-the-art convolutional neural networks (CNNs) to extract global features directly for pest classification, while neglecting the effectiveness of fine-grained features for identifying visually similar pest categories under a specific super-category. In this paper, we propose a saliency guided discriminative learning network (SGDL-Net) to tackle these problems. The proposed SGDL-Net simultaneously mines global features and fine-grained features in a multi-task learning manner. We design two branches with shared parameters for pest datasets with a hierarchical structure: the raw branch and the fine-grained branch. The raw branch is utilized to extract coarse-grained features, i.e., global features, and the fine-grained branch mines fine-grained features through a fine-grained feature mining module (FFMM) as a way to constrain feature learning in the raw branch. In particular, we leverage a salient object location module (SOLM) to locate the salient object in the image and feed it to the fine-grained branch. Finally, through the co-training of the two branches, SGDL-Net is able to learn coarse-grained and fine-grained combined discriminative features via a single CNN. Experimental results show that SGDL-Net achieves state-of-the-art performance on the benchmark dataset IP102 used for insect pest recognition. Meanwhile, ablative studies demonstrate the promise of its application on other hierarchically structured datasets (e.g., CIFAR-100).
Finite-Time Synchronization Under Aperiodically Intermittent Control and its Application on Spatially Coupled Reaction-Diffusion Neural Networks

We investigate a general way to achieve finite-time synchronization (FnTSYN) for networks via aperiodically intermittent control (AInC). At first, we propose a general sufficient criteria under AInC for finite-time stability (FnTSta). Then, a general model for complex networks under this sufficient condition is given. Furthermore, we apply the proposed condition to the spatially coupled reaction-diffusion neural networks (RDNNs) to realize its FnTSYN. Finally, simulations are given to verify the results.
A News Image Locating Approach Based on Cross-Modal RNN with An Encoding Updating Mechanism

Image plays an important role in news articles. Appropriate image arrangements can well transmit scene information and help readers understand more clearly. This paper proposes a neural model based on the cross-modal RNN equipped with an encoding updating mechanism to insert images into proper positions of a news document sequentially. For each image, our model selects the position of which the surrounding sentences are most relevant to the image. Two methods are employed to score the relevance between the image and surrounding sentences: the one employs a layer of convolution network before scoring, and the other employs the pooling operation after the scoring. It is reasonable to believe that inserting an image in news will render the information of the news. We hence introduce an updating mechanism to update the encoding of the news document after the image is inserted. To the best of our knowledge, we are the first to focus on the news image locating problem, as well as provide a feasible solution. We create the dataset for the problem by extending the DailyMail corpora and carry out experiments. Experiments show that the proposed model outperforms four baselines including the pointer network according to two evaluation metrics. The code is released on Github11https://github.com/njupt-summ/ImageLocating.
Co-Design of Consensus-Based Approach and Reliable Communication Protocol for Vehicular Platoon Control

Automated and coordinated vehicle platoon driving is challenging as the complexity of vehicle control and the presence of unreliable wireless inter-vehicle communication. This subject has an interdisciplinary nature, involving control theories, vehicle dynamics, and communication. Due to the constant interaction between control logic and communication topology, a co-design approach for the controller and for reliable communication protocol becomes necessary. This paper describes the AddP-CACC - a consensus-based solution to vehicle platoons. The proposed solution turns the communication topology into a design parameter that dynamically reconfigures the controller. In addition, the controller automatically compensates for outdated information caused by network losses and delays. AddP-CACC was implemented in the PLEXE tool and was compared with other related works. Simulation results have shown the robustness and performance of the proposed solution in various scenarios that would include realistic propagation conditions with interference caused by other vehicles. The results have also evidenced the ability of the proposed solution to maintain a stable vehicular platoon by using different communication topologies, different vehicle flows, even in the presence of strong interferences, delays, and fading conditions.
A Switched-Capacitors-Based 13-Level Inverter

The merit of switched-capacitors-based multilevel inverters (SCMLIs) is generally quantified in terms of a “cost function” (CF) that incorporates parameters such as voltage gain, component count, total standing voltage (TSV), and number of levels. In this article, a 13-level inverter is proposed with the aim of achieving a low value of CF. The proposed single-stage SCMLI uses one input source and three capacitors to attain a voltage gain of 3. It requires 13 power switches, of which the peak inverse voltage (PIV) of nine switches is restricted to the source voltage. The remaining four switches have PIV equal to twice the source voltage and they operate at low frequency. Thus, for all switches, the PIV is less than the amplitude of the output voltage. Moreover, the capacitors are self-balanced at all regions of modulation index values. The proposed inverter is validated through simulation and experimental results. A comparison of the proposed topology with other contemporary SCMLIs shows that it is highly competent in terms of CF, PIV and TSV requirements, waveform resolution, and capability to achieve voltage balancing of capacitors at low values of modulation index.
Efficiency Angle for Wireless Power Transfer Systems with Multiple Receivers

The efficiency angle is a figure of merit for the performance of a wireless power transfer system. However, it is only defined for a system with a single transmitter and receiver. In this work, the definition of the efficiency angle is extended to include multiple receiver configurations. It is exemplified on an inductive wireless power transfer set-up. A graphical representation depicts the relationship between the efficiency angle, the extended kQ-factor, and the maximum efficiency of the system, facilitating the system design process of multi-receiver wireless power transfer systems.
A novel convolutional auto-encoder networks for daily stock market prediction

In financial markets, it is important and challenging to predict the daily stock price movement. In stock price movement prediction domain, feature learning from financial data is one of the most important problems. With the development in deep learning, convolutional neural networks (CNN) have been used for feature learning and prediction, and achieved good forecasting results. However, in predicted methods reported so far, less focus has been paid to different source of information for learning effective features. In this paper, we propose a Convolutional Auto-Encoder (CAE) networks, which can be used to process different sources data, including different stock markets. The CAE networks are applied to predict the next day’s direction of movement for the stock indices of Shanghai Stock Exchange Composite (SSEC), NASDAQ, S&P500 and DJIA based on various of initial financial and economic variables. The experimental results on different stock indices demonstrate the outstanding performance of proposed method compared to the baseline methods
Four Novel Embedded Z-Source DC–DC Converters

This article develops a series of improved Z-source dc–dc converters to realize additional voltage pumping and high power density through two propositions. Proposition 1 develops two improved positive- and negative-connected Z-source dc–dc converters (IPZSC and INZSC), using the same number of components as in the existing voltage pumping Z-source converter. Based on Proposition 1, Proposition 2 proposes four novel embedded Z-source dc–dc converters (EZSCs) by placing the source in a specifically designed position, which realizes lower voltage stresses across capacitors. Various technical aspects of the proposed EZSCs, including operations, power losses, and small signal stability analyses are detailed in this paper. Besides, comparisons between the proposed series of improved Z-source dc–dc converters and other existing Z-source dc–dc converters are thoroughly made to demonstrate the better performance of the EZSCs. Finally, experiments are conducted to well validate the effectiveness and superiority of the proposed converter circuits.
Three-Phase Trinary Asymmetric Multilevel Inverter With Single DC Source and Open-Loop Control

A three-phase trinary-asymmetric cascaded H-bridge multilevel inverter topology with single dc source per phase is presented in this paper. Only one of the H-bridges (called Main bridge) is fed from a dc source and all others (called auxiliary bridges) derive their dc links from floating capacitors. Existing literature mostly depend on closed loop control schemes to stabilize the dc link voltage. This paper proposes a new open loop control strategy for balancing of voltage across floating capacitors naturally. The dc bus voltages of auxiliary bridges are converged to maintain a trinary asymmetric ratio with the main-bridge by selecting the right set of space-vectors. Modulation of the inverter is carried out by exploring the space-vector redundancies that will help in achieving natural balancing of capacitor voltages. Selection of number of bridges to be cascaded, depends on criteria such as the allowable time of convergence, number of switching devices, acceptable power quality indices etc. Considering such constraints, a system with three cascaded cells per phase is found to exhibit satisfactory performances. Hence in this paper, simulation results obtained in MATLAB/Simulink based model corresponding to such a system are presented. Experimental results observed on a laboratory prototype matched well with the simulation.