Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Scheme of Image’s Regional Colorization Using Mask R-CNN and Poisson Editing

Image colorization is a creative process of reasonably adding colors on gray-scale images to generate well-pleasing colorized images. The existing colorization methods normally require user-supplied hints of color points and doodles, or handpicked color reference images for transferring colors, or diverse color images for predicting the colorized results; but the final colorized results generated by most of them may seem unnatural as a consequence of the unprofessional users’ skills, inaccurately color transferring, or limited scale of color image collection. To overcome these limitations, a hybrid scheme consisting of two modules is proposed for images’ region colorization by combining semantic segmentation and seamless fusion techniques in this paper. In the first module, the masks and category of input image’s regions and background are derived from a Mask R-CNN model, and the corresponding reference images of each region are selected from a pre-classified color image database. In the second module, the background and various regions of an image are colorized by a U-Net model and a VGG model respectively. Then, the Poisson editing technique is applied for fusing all the colorized results to generate the final whole colorized image. The experiments show that our scheme can not only flexibly select the appropriate reference images for different regions of the image according their semantic information, but also effectively merge colorized results to generate a plausible colorful image. By reasonably combining different CNN-based models according to their superiority, our scheme avoids the limitation of failures caused by single-step methods, and achieves richer artistic visual effect compared with other existing methods.
A Method Based on the Combination of Laxity and Ant Colony System for Cloud-Fog Task Scheduling

In today’s Internet of Things research community, Cloud-fog framework is a potential technology for Internet of Things to support energy consumption of an IoT system and delay-sensitive applications that require almost real-time responses. However, how to schedule the computational tasks which is to offload to fog nodes or cloud nodes is not fully addressed until now. In this paper, in order to solve the complex task scheduling problem with some priority constraints of IoT applications taking into account the energy consumption and reducing energy consumption on the condition of satisfying the mix deadline, we formulate an associated task scheduling problem into a constrained optimization problem in cloud-fog environment. A laxity and ant colony system algorithm(LBP-ACS) is put forward to tackle this problem. In this algorithm, a strategy of task scheduling is not only considering the priority of a task, but also its finished deadline. In order to handle the sensitivity of task delay, the laxity-based priority algorithm is adopted to construct a task scheduling sequence with reasonable priority. Meanwhile, to minimize the total energy consumption, the constrained optimization algorithm based on ant colony system algorithm is used to obtain the approximate optimal scheduling scheme in the global. Compared with other algorithms, the experimental results show that the proposed algorithm can effectively reduce the energy consumption of processing all tasks, while ensuring reasonable scheduling length and reducing the failure rate of associated tasks scheduling with mixed deadlines.
Comparison of ReLU and linear saturated activation functions in neural network for universal approximation

Activation functions used in hidden layers directly affect the possibilities for describing nonlinear systems using a feedforward neural network. Furthermore, linear based activation functions are less computationally demanding than their nonlinear alternatives. In addition, feedforward neural networks with linear based activation functions can be advantageously used for control of nonlinear systems, as shown in previous authors’ publications. This paper aims to compare two types of linear based functions - symmetric linear saturated function and the rectifier linear unit (ReLU) function as activation functions of the feedforward neural network used for a nonlinear system approximation. Topologies with one hidden layer and the combination of defined quantities of hidden layer neurons in the feedforward neural network are used. Strict criteria are applied for the conditions of the experiments; specifically, the Levenberg-Marquardt algorithm is applied as a training algorithm and the Nguyen-Widrow algorithm is used for the weights and biases initialization. Three benchmark systems are then selected as nonlinear plants for approximation, which should serve as a repeatable source of data for testing. The training data are acquired by the computation of the output as a reaction to a specified colored input signal. The comparison is based on the convergence speed of the training for a fixed value of the error function, and also on the performance over a constant number of epochs. At the end of the experiments, only small differences between the performance of both applied activation functions are observed. Although the symmetric linear saturated activation function provides the lesser median of the final error function value across the all tested numbers of neurons in topologies, the ReLU function seems to be also capable of use as the activation function for nonlinear system modeling.
Multi-lane Formation Assignment and Control for Connected Vehicles

This paper is concerned with coordinated formation assignment and control of multiple connected vehicles in highway scenario. A dynamical interlaced layered formation generation method is introduced to provide safe distance among vehicles and efficiency for coordinated lane changing and formation switching simultaneously in real time. To assign vehicles to the generated formation, the optimal problem is modeled by introducing the function of numbers of changed lanes for all the vehicles with respect to the kinematic constraints, and on-board local controllers for the vehicles are developed. Simulation result indicates that, comparing to existing methods for lane assignment in multiple traffic scenarios, the method provided by this paper could increase the traffic efficiency by utilizing maximum road capacity while decreasing travel time for all the vehicles.
Near-Optimal Control of Net Output Power for PEMFC System

The interior of a fuel cell is a complex chemical reaction process. How to design an algorithm to achieve energy optimization is a challenging problem. Due to the nonlinearity of the system, it is usually difficult to obtain the real-time global optimal control strategy directly. In this paper, a near-optimal controller is proposed for a polymer electrolyte membrane fuel cell (PEMFC) air supply system. The control goal is to maximize net output power of the PEMFC system by tracking a desired optimal oxygen excessive ratio. The tracking problem is formulated as a receding-horizon optimal control problem and a predictive timescale approximation technique is adopted to solve the optimal control law and effectively reduces the computational complexity especially with nonlinear systems like PEMFC. The simulation of dynamic excess oxygen ratio adjustment and various comparison with other control methods show the rapidity, high accuracy and fine capacity of resisting disturbance of the proposed method.
Fully Scaled Monocular Direct Sparse Odometry with A Distance Constraint

Existing methods for solving the inherent scale-ambiguity of monocular visual odometry adopt two main techniques: using an inertial measurement unit and multiple cameras. The distance between poses, which can be obtained from GPS navigation, wheel encoders, and some types of odometry, can also restore the scale of motion. In this work, we propose a fully scaled monocular direct sparse odometry, using the distance between image frames as a constraint to maintain a trajectory and create a map at a suitable scale based on direct sparse odometry. First, the modulus of the translation vector of the current frame, related to the reference keyframe, is corrected in the tracking unit. Additionally, a distance error term is proposed and adapted into the optimization as a further constraint between keyframes. We evaluate our method on the challenging European Robotics Challenge (EuRoC) dataset; our method shows advantages in accuracy compared with the state-of-the-art OKVIS method.
Application of a Combined Denoising Method in Ground Penetrating Radar Signal Pre-processing

Aiming at the difficulties of low signal-to-noise ratio (SNR) and difficulty of extracting the features in step frequency continuous wave ground penetrating radar (SFCW-GPR) echo pre-processing, a new method of echo pre-processing combined with wavelet threshold denoising algorithm and high-pass filter is proposed. The combined method realizes the extraction of useful signals with slowly changing speed by multi-resolution analysis of signals containing Gauss white noise. Compared with traditional processing methods, the wavelet threshold denoising algorithm has higher SNR and smaller mean square error (MSE) and has better processing effect and imaging effect.
Chaotic Systems Based Pseudo-Random Signal Generators Realized Over a Galois Finite Field

The statistical characteristics and results of the FIPS 140–2 tests of a new class of pseudo-random signal generators based on the modified Lorentz, Chua, Dmitriev-Kislov and Anischenko-Astakhov systems over the finite Galois field are considered. The estimates of the signal correlation intervals, as well as histograms of the probability distributions of the generated numbers, are obtained. Evaluation of the compliance of binary sequences formed on the basis of systems with the requirements of the FIPS 140–2 test with variation of systems parameters was obtained. The results of the work can be used in the construction of communication systems with broadband signals, modeling and cryptography.
Control Allocation Based Sliding Mode Fault Tolerant Control

This paper describes a novel fault tolerant control using robust sliding mode control strategy. This scheme can also be employed as actuator redundancy management for over-actuated uncertain linear systems. In contrast to many existing methods in the literature that assume the control input matrix is not of full rank such that it can be factorised into two matrices, this scheme can be applied to systems whose control input matrix has full rank. The so-called virtual control, in this scheme, is designed to be robust against uncertainties emanating from visibility of the control allocator to the controller and imperfection in the estimated effectiveness gain. Then using a static real-time control allocator, the obtained virtual control signal is redistributed among remaining (redundant or non-faulty) set of actuators. The proposed scheme is a unified, control allocation-based fault tolerant control which does not need to reconfigure the control system in the case of actuator fault or failure. The effectiveness of the proposed schemes is discussed with a numerical example.
Connected-Autonomous Electric Vehicles based on DC-Bus Stabilisation Using Hybrid Storage System

This paper proposes a novel DC-bus voltage control technique for a connected-autonomous electric vehicle (CAEV) using a hybrid supercapacitor-battery energy storage system. The verification by simulation of the control system that manages the current flow from the supercapacitor by using a rule-based algorithm within a CAEV is described. A feedforward compensator is included within the control scheme to suppress DC-bus voltage excursions under highly variable driveline torque conditions. Furthermore, the proposed control system has been verified by the case of CAEV being subject to a real-world driving cycle and acceleration/deceleration modes in order to reflect realistic electric vehicle powertrain operations.
LLOAM: LiDAR Odometry and Mapping with Loop-closure Detection Based Correction

Re-observed places recognition problem has always been a key issue in the areas of mobile robot navigation including Simultaneous Localization and Mapping (SLAM), it is also called loop-closure detection. A global optimization back-end is usually required to correct global poses and map when a loop closure is detected. It is widely acknowledged that a standard SLAM system should be comprised of odometry front-end, loop-closure detection and a back-end responsible to global optimization. The previous SLAM researches are focused on the images based visual SLAM or lidar based odometry systems, there are still few complete 3D LiDAR based SLAM systems. This paper proposes a complete 3D LIDAR based SLAM system, LLOAM, which includes a front-end with a point clouds segmentation matching based loop-closure detection and a back-end based on factor graph optimization. The proposed SLAM system has been evaluated using a KITTI dataset of large-scale outdoor urban street environment. The results indicate that the proposed SLAM system outperforms others odometry system in local accuracy and global consistency.
Realtime l1-fault-and-state estimation for multi-agent systems

In this paper, we propose a state and fault estimation scheme for multi-agent systems. The proposed estimator is based on an l1 -norm optimization problem, which is inspired by sparse signal recovery in the field of compressive sampling. Moreover, we provide a necessary and sufficient condition such that state and fault signals are correctly estimated. The result presents a fundamental limitation of the algorithm, which shows how many faulty nodes are allowed to ensure a correct estimation. An illustrative example of a vehicle platoon is given to validate the effectiveness of the proposed approach.
On the use of a computed-torque control law for the terminal region of an NMPC scheme

This work addresses the advantages of using a CTC (Computed-Torque Control) law within a NMPC (Nonlin-ear Model Predictive Control) scheme. More precisely, the CTC law leads to stable linear closed-loop dynamics after the end of the prediction horizon. By choosing appropriate control gains, a positive invariant ellipsoidal set in which the input constraints are satisfied is determined. Using this set as terminal region in the NMPC problem, together with additional assumptions provides recursive feasibility and asymptotic stability guarantees. This proposed approach can be applied for any robotic manipulators admitting a CTC law, as a particular example. To prove its benefits some simulation results and comparisons with quasi-infinite horizon NMPC over the classical inverted pendulum dynamics are presented.
Smart Household Electricity Usage Optimization Using MPC and MILP*

This paper suggests scheduling of electric appliances in a smart household if a Time of Use pricing scheme is applied by the utility company. The algorithms discussed in this paper enable the control and the balancing of the energy consumption to ensure minimal electricity costs of the household. The optimal appliances’ schedule and the energy consumption pattern are the results of a two-level optimization strategy. The home appliances are scheduled based on a Mixed Integer Linear Programming approach. If an electric vehicle (EV) is also present in the household, its charging is scheduled to achieve the State of Charge desired by its departure time. The EV battery is also considered as power storage device. The second level is reserved to control the electric heating system with a Model Predictive Controller (MPC) considering the results of the first optimization level as energy constraints. The MPC may use the EV battery as a power source where the Vehicle-to-Home technology is adopted. Moreover, we present an EV scenario to discuss the battery lifetime. Simulation results show the energy balancing along with an optimal electricity cost based on the strategy proposed.
Stochastic Driver Modeling and Validation with Traffic Data

This paper describes a stochastic modeling approach for predicting driver responses in highway traffic. Different from existing approaches in the literature, the proposed modeling framework allows simultaneous decision making for multiple drivers (>100), in a computationally feasible manner, instead of modeling the decisions of an ego driver and assuming a predetermined driving pattern for other drivers in a given scenario. This is achieved by a unique combination of hierarchical game theory, which is used to model strategic decision making, and stochastic reinforcement learning, which is employed to model multi-move decision making. The proposed approach can be utilized to create high fidelity traffic simulators, which can be used to facilitate the validation of autonomous driving control algorithms by providing a safe and relatively fast environment for initial assessment and tuning. What makes the proposed approach appealing especially for autonomous driving research is that the driver models are strategic, meaning that their responses are based on predicted actions of other intelligent agents in the traffic scenario, where these agents can be human drivers or autonomous vehicles. Therefore, these models can be used to create traffic models with multiple human-machine interactions. To evaluate the fidelity of the framework, created stochastic driver models are compared with real driving patterns, processed from the traffic data collected by US Federal Highway Administration on US101 (Hollywood Freeway) on June 15th, 2005.
A Fourier Domain Training Framework for Convolutional Neural Networks Based on the Fourier Domain Pyramid Pooling Method and Fourier Domain Exponential Linear Unit

convolutional neural networks (CNNs) in the frequency domain is of great significance for extending the deep learning principle to the frequency domain. However, the frequency domain representation of the convnet architecture is highly demanding due to their complicated Fourier domain training features. Therefore, high accuracy and unambiguous representation strategies are needed for training convolutional neural networks entirely in the Fourier domain. Being founded on the bin decomposition mechanism and the non-saturated activation theory, this paper proposes an accurate, stable and efficient Fourier domain training framework for convolutional neural networks. The framework contains two important Fourier domain representations: one is the Fourier domain exponential linear unit, and the other is the pyramid pooling layer. The former alleviates the vanishing phenomenon and makes CNNs easier to converge in the Fourier domain; the latter avoids the original cropping or warping steps and improves the classification accuracy. With the framework, the Fourier domain training accuracy is improved without sacrificing the throughput of the graphic processing unit (GPU). With the Re-50 as the backbone, the top-1 and top-5 classification errors are reduced from 28.85 and 9.55 to 18.63 and 4.05, respectively, while the speedup ratios of the framework can reach up to 4.9877 and 1.8997, respectively, at a batch size of 128 on an NVIDIA GEFORCE RTX 2080 GPU (8.92 TFLOPS). The average difference between the classification value and the ground truth value is only 0.21 on the MetaGram-1 set, which indicates great goodness-of-fit and robustness of the framework. This investigation illustrates that the proposed Fourier domain CNN framework using the sophisticated Fourier domain representation strategy is highly efficient and accurate. Therefore, it may serve as a baseline framework to establish the training pipelines for Fourier domain CNNs, which can improve the deep learning accuracy of CNNs and extend the Fourier domain representation strategy to other deep learning networks.
Distributed Control of Stochastic Manufacturing Processes Based on Consensus Algorithms

In this paper, we deal with a distributed control of stock levels of buffers in a large-scale manufacturing factory. We suppose that the manufacturing factory is composed of many production processes and transportation processes. Each production process has several input buffers to stock materials for production temporarily and an output buffer to stock the produced materials temporarily. The transportation process is assumed to be realized by a swarm of vehicles, which flexibly move between buffers and transport the materials conveniently. We model the production processes as deterministic dynamical systems and the transportation processes as stochastic events. Then, we propose a distributed control method for the production rates and the transportation probabilities by employing an idea of consensus algorithms and rigorously prove that all the normalized stock levels of the buffers are globally stable at consensus values in a probabilistic sense. We finally demonstrate the effectiveness of our proposed method by numerical simulations.
Deep Learning With Customized Abstract Syntax Tree for Bug Localization

Given a bug report, bug localization technique can help developers automatically locate potential buggy files. Information retrieval and deep learning approaches have been applied in bug localization by extracting lexical features in bug reports and syntactic features in source code files, though they fail to utilize the structural and semantic information of source code files. In this paper, we present a bug localization system CAST, which exploits deep learning and customized abstract syntax trees of programs to locate potential buggy source files automatically and effectively. Specifically, CAST extracts both lexical semantics in bug reports (e.g., words) and source files (e.g., method names) and program semantics in source files (e.g., abstract syntax tree, AST). Moreover, CAST enhances the tree-based convolutional neural network (TBCNN) model with customized ASTs, which distinguish between user-defined methods and system-provided ones to reflect their contributions leading to defects. Furthermore, customized ASTs group the syntactic entities with similar semantics and prune the ones with little or redundant semantics in order to facilitate the learning performance. Experimental results on four widely-used software projects show that CAST significantly outperforms the state-of-the-art methods in locating the buggy source files.
Millimeter-Wave Communications With Non-Orthogonal Multiple Access for B5G/6G

In order to further improve the system capacity, we explore the integration of non-orthogonal multiple access (NOMA) in millimeter-wave communications (mmWave-NOMA) for future B5G and 6G systems. Compared with the conventional NOMA, the distinguishing feature of mmWave-NOMA is that, it is usually characterized by transmit/receive beamforming with large phased arrays. In this paper, we focus on the design challenges of mmWave-NOMA due to beamforming. Firstly, we study how beamforming affects the sum-rate performance of mmWave-NOMA, and find that with conventional single-beam forming, the performance may be offset by the relative angle between NOMA users. Then, we consider multi-beam forming for mmWave-NOMA, which is shown to be able to achieve promising performance enhancement as well as robustness. Next, we investigate the challenging joint design of the intertwined power allocation and user pairing for mmWave-NOMA. Relevant challenges are discussed and some potential solutions are proposed in detail. We further consider hybrid spatial division multiple access (SDMA) and NOMA in mmWave communications, where some possible system configurations and the corresponding solutions are discussed to address the multi-user issues including multi-user precoding and multi-user interference mitigation. Finally, we present future directions in mmWave-NOMA and summarize the paper.