Miracle-dz/UniFAR-FTUs huggingface dataset:
A Sustainable Parallel-machine Scheduling Problem with Time Constraint Based on Hybrid Metaheuristic Algorithm

Sustainable scheduling is of great significance for modern manufacturing companies' long-term development. In this paper, we focus on a sustainable parallel-machine scheduling problem with time constraint, which comes from a rubber manufacturing enterprise. Sustainable demands of this problem include economic benefit (completion time constraint) and environmental effect (energy consumption objective). Besides, this problem not only schedule jobs but also decide the processing speed of machines. We analyze the complexity of the problem and develop a hybrid metaheuristic algorithm to effectively tackle the problem. Experimental results of a case study and comparative analysis show the effectiveness of the proposed algorithm.
Stacked Nanosheet Based Reconfigurable FET

In this paper, we present a vertically stacked (VS) nanosheet (NS) FET architecture that can realize device reconfigurability and inverter action at the device level of operation. NS are uniformly n+ and p+ doped regions as in the junctionless device, which then is combined with the stacked silicides at the drain end to perform complimentary operation. A single gate with gate-all-around (GAA) architecture provides improved electrostatic integrity. With proper biasing, the device operates as nFET and pFET, respectively. Further, the impact of gate length (Lg) and NS thickness scaling on device characteristics are also analyzed. We observe that at reduced NS thickness, much improved OFF-state device characteristics along with the sharp transition in voltage transfer characteristic (VTC) curve is obtained, due to efficient gate control of the channel region.
CatchIt: Large-scale Grasping combined with Preliminary and Precise Localization method for Aerial Manipulator

Grasping objects in a large-scale area has been investigated extensively for mobile robots. But for unmanned aerial manipulator(UAM), this is still a challenging task. In order to solve the problem of accurate grasping in a large-scale area, we propose a novel detection and control framework for UAM, which consists of two stages: preliminary localization and precise localization. At the preliminary localization stage, the RGB-D sensor mounted on the end-effector of the manipulator scans to obtain the point cloud surrounding the UAM. By bringing the known target shape and processed point cloud parameters into our proposed loss function, we select the highest priority area as the best potential region proposal, which can help the UAM to screen out the target for precise localization from the obtained point cloud. At precise localization stage, after UAM reaches the best potential region, the RGB-D sensor mounted on the drone uses the deep object pose estimation(DOPE) to estimate the 6D pose of the target. Through independently compensating for the disturbance of the UAV and manipulator, the UAM can accurately grasp the target with the estimated 6D pose. To evaluate the performance of the UAM, we conducted experiments in four different scenes. Experimental results demonstrate that the UAM can grasp the target with an average success rate of 83.4% for the large-scale scene. The above results prove the feasibility and robustness of the framework. The code is available at https://github.com/skywoodsz/CatchIt
Covid Detection from CXR Scans using Deep Multi-layered CNN

Severe Acute Respiratory Syndrome Corona virus 2 (SARS-COV-2) also known as COVID-19 has been emerged as a pandemic throughout the globe recently. Therefore, accurate diagnosis of COVID-19 is necessary to fight against this pandemic situation. In this context, chest X-ray (CXR) scans play an important role in the diagnosis of the corona virus. In this paper, an intelligent detection and classification technique of COVID-19 has been proposed to assist doctors in their diagnostic prediction. A deep multi-layered convolution neural network (CNN) has been proposed to detect COVID-19 accurately from CXR scans. The proposed methodology has experimented on a combination of multiple open source publicly available datasets. Experimental results demonstrate the efficacy of the proposed methodology in COVID-19 detection from CXR images.
A Game-Theoretic Reinforcement Learning Approach for Adaptive Interaction at Intersections

In this paper, we propose a bi-level algorithm for motion planning at intersections based on a scheme of reasoning game theory and heuristic reinforcement learning. In the upper level, a recurrent neural network is introduced to estimate the type of opponent agent. In the lower level, Q-networks are selectively connected to implement the game with different type. Then the ego agent could update its estimation step-by-step and conclude correspond action from historical joint state. The simulation results show that the bi-level controller improves pass times and collision avoidance performance.
Dissimilarity Analytics for Monitoring of Nonstationary Industrial Processes with Stationary Subspace Decomposition

The wide spread of dissimilarity analysis (DISSIM) gives rise to many useful process monitoring models. However, it is only limited to stationary processes. Reliable DISSIM based monitoring methods will encounter challenges of nonstationary behaviors including the time-varying mean or variance. In this work, a stationary subspace decomposition based DISSIM (SSD-DISSIM) model is developed to detect incipient faults sensitively for nonstationary industrial processes. As faults may disappear in time-varying process variations, the key is how to extract information with stationary characteristics from the complex process data. To eliminate the interference caused by the mixed nonstationary signals, the extraction of the stationary components is first conducted by projecting data into a low-dimensional subspace. As a type of distribution-based method, the DISSIM is combined to monitor the extracted stationary components in terms of not only the mean and variance, but also the correlations and distribution. Thus the proposed model can overcome the limitation of DISSIM that arises in nonstationary processes and enhance the sensitivity and reliability of the monitoring. The method effectiveness is demonstrated through the real thermal power plant example.
Gradient Descent Feedback Correction for Robust Deadbeat Predictive Control in Permanent Magnet Synchronous Motor System

In order to optimize the control performance of the permanent magnet synchronous motor (PMSM) system, the deadbeat predictive control (DPC) is introduced. But due to the lack of rolling optimization and feedback correction, the control performance suffers from the mismatch of model parameters. To deal with the problem, the gradient descent feedback correction (GDFC) method is proposed, which automatically corrects the resistance and inductance in the operation process without the need of additional observer or identification model, enhancing the robustness of the DPC greatly. The proposed method also has the advantages of simple operation and low time-consuming. Then the effectiveness and applicability of the proposed method are verified by the simulation results which show the convergence values of resistance and inductance under different working conditions.
MAC Aware Random Network Coding for Wireless Unicast Flows

In this paper we propose random linear network coding (RLNC) as an efficient encoding scheme for unicast flows in single-hop ad hoc network and multi hop wireless mesh networks (WMN). The traditional approach for encoding unicast flows, known as inter-flow network coding (NC), assumes instant decoding and assumption of reliable feedback channel to minimizes the total number of transmissions. But under the practical consideration of the wireless medium access control (MAC), the gain of inter-flow NC technique suffers from higher latency due to the MAC overhead. We validate the result of our work on TelosB motes testbed. Our testbed implementation shows that the proposed approach of using RLNC reduces the transmission completion time by up to 40% compared to the inter-flow NC scheme for single-hop data dissemination and multi-hop wireless routing problems.
Optimal Selection of Informed Agents for Influence Opposition

There is no doubt that the members of a society can influence each other and a minority of them may guide, in some situations, the whole society toward a particular opinion. However, the promoted opinion is not always desirable, and in some cases, it is desired to prevent its propagation in the network. This mission can be done with the help of informed agents that are common agents that act as hidden advertisers. In this article, we will discuss how many and which agents should be selected as informed agents in a way that the final opinions of agents satisfy some given constraints. In the line of solving this problem, the notion of equilibratability is considered, and the problem is formulated as minimizing the zero-norm of available solutions. Knowing that the mentioned problem is NP-hard, some relaxation methods are considered to solve this problem. The efficiency of the proposed methods is investigated for selecting the sparsest solution in a well-known graph. Finally, the required informed agents to resist a group of spreaders in some random graphs [generated with different methods, such as Erdős and Rényi (ER), Watts and Strogatz (WS), and Barabási and Albert (BA)] are compared, and some reasonable results are observed.
Mean amplitudes of vibration of SF6 and intramolecular multiple scattering

Precise gas phase electron diffraction data for incident electron energies of 43 and 54 keV have been collected for SF6 by counting techniques with emphasis toward a determination of root‐mean‐squared amplitudes of vibration and as a case study for the necessity of correcting the scattering cross sections for intramolecular multiple scattering. At first, the amplitudes and shrinkage values were found to disagree with predicted values based on spectroscopic results. However, after corrections for the multiple scattering had been applied, the values changed significantly. The new structural parameters obtained are ra (S–F) = 1.5622(7) A, l(S–F) = 0.0416(5) A, ra(F–F) (cis) = 2.2094(8) A, l(F–F) = 0.0604(4) A, ra(F–F) (trans) = 3.1240(21) A, and l(F–F) = 0.0551(17) A. The shrinkage values for the F–F trans distance changed from −0.0003(2) to +0.0018(2) A due to the corrections and are now in agreement with the spectroscopic values. The success of the modified Glauber approximation warrants an introduction of.
An adaptive PID control based on BP neural network for the voltage of MFC

Microbial fuel cell (MFC) has serious nonlinearity and external disturbances when operating. In order to keep the ideal output of MFC under different load disturbances, an intelligent optimization control method based on BP neural network PID(BP-NN PID) is proposed. This method uses self-learning ability to tune the parameter of the PID controller. In addition, the online real-time control of the BP-NN algorithm shows the ability of the BP-NN to handle nonlinear systems. The simulation experiment finally shows that compared with traditional PID and single neuron PID, the BP-NN PID algorithm has good dynamic response, steady-state accuracy, adaptive ability, and strong robustness, which can make the output voltage of MFC meet the requirements.
Spaced Repetition for Slow Learners

Spaced Repetition has proven to be an effective way in learning and memorizing complex topics. An algorithm ‘Spaced Repetition for Slow Learners’ (SRSL) is described to schedule repetitions which eventually adapts to the capacity of the learner. SRSL computes the score of learners for a particular assessment based on factors such as response time, difficulty and dependency of questions. The exponential forgetting curve model is the memory model assumed by SRSL. Based on this algorithm, a model has been proposed with experimental analysis of the same. Further, comparison of SRSL and the Leitner System demonstrates the adaptability of the algorithm to the learning curve of the learner.
On Series Connections of Fractional-Order Elements and Memristive Elements

This paper proposes a current-controlled fractional-order memristor emulator based on one active building block. The emulator consists of a multiplication mode current conveyor (MMCC) block with three passive elements. Additionally, the series connection of fractional-order inductor (FOI) and fractional-order capacitor (FOC) with memristive elements in the i – v plane is demonstrated numerically for different cases. Changing the order of the FOC or FOI and its effect on the pinched hysteresis loop area are investigated, which improve the controllability of the double loop area, the location of the pinched point, and the operating frequency range. Numerical, PSPICE simulation results, and experimental verification are investigated for different cases to approve the theoretical findings. Moreover, a sensitivity analysis using Monte Carlo simulations for the tolerance of the discrete components of the memristor emulator is investigated.
Selective Saccharide Recognition Using Modular Diboronic Acid Fluorescent Sensors

Modular diboronic acid receptors R-1 and S-1 were synthesized and found to be selective fluorescent chemosensors for saccharides. The fluorescence intensity of R-1 and S-1 increased on addition of saccharides due to the formation of intramolecular 1:1 cyclic complexes. In general, R-1 has higher binding constants and produces a higher fluorescent response with saccharides than S-1. The fluorescence intensity of receptors R-1 and S-1 increased on addition of saccharides due to the formation of intramolecular 1:1 cyclic complexes. In general, R-1 has higher binding constants and produces a higher fluorescent response with saccharides than S-1.
Herding of a Flock of Agents Using Single Robot

In this paper, an algorithm is proposed for enabling a single robot to herd a flock of agents to a designated space or tracking a specified trajectory. The herding algorithm is designed using a second-order dynamic model of flocking based on Reynolds rules. At first we analyze the stability of the flock with or without external effect (herder) and conclude the conditions of avoiding fragmentation of the flock. From these conditions a herding algorithm is proposed, and it is also demonstrated using numerical simulations.
A Novel RGB-D SLAM for Recognizing Features on Static Objects

This paper proposes a novel RGB-D SLAM algorithm which can recognize the features on static objects and estimate the ego-motion of RGB-D camera in dynamic scenes. Besides, a static score is established for each feature, which is used to indicate the static level of the feature. The static score consists of static density and static count. Static density represents the proportion of features of static objects around each feature, and static count represents the number of times each feature is recognized as a static feature. In the experimental section, we compare the proposed method with ORB-SLAM2 through the TUM RGB-D dataset. The results show that in dynamic scenes, the proposed method effectively improves the performance of the ego-motion of RGB-D camera.
Analysis of Modulation Performance of Underwater Visible Light Communication with Variable Wavelength

This paper evaluates the performance of Underwater Visible Light Communication (UVLC) with various modulation and wavelength. The first scenario will analyze the Signalto-Noise Ratio (SNR) of the UVLC with 450, 480, and 500 nm wavelength. The second scenario will compare the performance of Bit Error Rate (BER) with various modulation from Onoff Keying No-Return Zero (OOK-NRZ), On-Off Keying Return Zero (OOK-RZ), 8 Pulse Position Modulation (8-PPM), and 8 Pulse Amplitude Modulation (8-PAM). Same as the first scenario the comparison of BER use 450, 480, and 500 nm wavelength. From the simulation of the first scenario, the used of 500 nm wavelength get the result 13.1147, which is the best result of SNR in this simulation. Meanwhile, in the second scenario, the combination of 8-PPM with 500 nm wavelength get the result 1.8922 $\times 10^{-10}$, which is the best result and the value is smaller from Optical Wireless Communication (OWC) BER which is $10^{-9}$
A prediction method of NOx in thermal power plants using GC-LSTM neural network

The emission of NOx in thermal power plants would cause serious pollution to the environment, and effective prediction of NOx has important significance. With the development of deep learning, many methods can effectively solve the prediction problem. In this paper, a long short-term memory (LSTM) neural network based on the generalized correntropy (GC) loss function, which is called the GC-LSTM neural network here, is proposed to predict the NOx emission. In order to make the characteristics of high-dimensional data have the same measurement scale, normalization is used to preprocess data firstly. Then, the processed historical data is used as training data to train the GC-LSTM neural network. After that, the emission of the NOx at each time instant can be predicted according to the online measured relevant data based on the established GC-LSTM neural network. Finally, prediction results under the LSTM neural network with GC, mean square error (MSE) and the mean absolute error (MAE) loss functions are given. And according to the simulation results, it can be seen that the loss function curve of the LSTM network based on GC is more stable and has better convergence. It can be seen that the proposed GC-LSTM prediction is more accurate, by comparing the probability density function (PDF) and root mean square error (RMSE) of the error between the actual values and the prediction values.
A Novel Integrated Neural Network for License Plate Detection And Recognition

In order to achieve the tasks of license plate detection and recognition, a novel integrated neural network is proposed in this paper, which can accurately locate license plate and recognize characters at the same time. Different from the existing methods which take detection and recognition as two separate tasks, the proposed approach jointly solves these problems in an integrated network. Meanwhile, overall network can be trained end-to-end. First, the You Only Look Once (YOLO [1]) algorithm is applied to locate the license plate. Then, to get the features of the license plate and transform to the specific scale, an area transformer module (ATM) is designed. Next, to refine the features and achieve recognition task, characters recognition module (CRM) is designed. Last, The extensive experiments were performed on Chinese City Parking Dataset (CCPD) dataset [2] and demonstrate the effectiveness and the efficiency of the proposed method.
A Hierarchical Control Strategy for the Consensus of Networked Systems

The consensus problem is one of the fundamental problems studied in networked systems. In this paper, we first propose the hierarchical control strategy to solve the consensus problem, which is similar to the pyramid structure of human society. In the next step, we analyze the convergence properties of the proposed protocols. Finally, simulation results demonstrate the effectiveness and significance of the proposed model.
Lidar guided stereo simultaneous localization and mapping (SLAM) for indoor Three-dimensional reconstruction

Based on the requirement of industrial automation, this paper proposes a three-dimensional reconstruction algorithm of the dynamic environment for industrial operation environment perception, which can be applied to many different industrial fields. According to the actual situation of the inner wall of the rolling mill, a method of lidar raw data preprocessing is proposed in this paper. The original lidar data is tagged and the detailed local point cloud information of interest is isolated. Loop detection is introduced into the algorithm to establish a globally consistent point cloud graph. The overall environment modeling algorithm is constructed to realize the real-time dynamic modeling of indoor environment in the actual industrial environment operation. The method has been evaluated by real-time reconstruction of the three-dimensional model of the specified area, which provides prior information for the operation of the robotic manipulator. We compare the performance of three-dimensional with loam, using the actual industrial environment, and show that our algorithm achieves better accuracy with reduced computational expense.
Vision Based Autonomous Tracking of UAVs Based on Reinforcement Learning

Air-ground cooperation is of great importance in certain extreme environment missions, which require real-time tracking of the unmanned aerial vehicle (UAV) on the ground vehicle. As reinforcement learning (RL) has achieved great success in many challenges of planning and control, a research on path planning of UAVs for object tracking based on RL is presented, considering images from a visual sensor as the input. Convolutional neural networks (CNN) and a spatial soft-max layer are used to detect the object in the images. The tracking results of a filter CSR-DCF based on OpenCV are combined with the output of CNN to improve training efficiency and obtain better tracking performance. Three independent experiments are conducted with different conditions in simulated environments where a quadcopter is trained to track a ground robot based on V-REP. Valid results show that the UAV has good performance in the tracking work with MAE of 0.23m in x and 0.19m in y.
Improved Data-Driven Yaw Misalignment Calibration of Wind Turbine via LiDAR Verification

Yaw control system (YCS) is the critical part to actively control nacelle direction parallel to the inflow direction. Aiming at the aged units, the YCS would result in the loss of power production due to a shifting error. In this paper, a yaw zero-point misalignment calibration method is proposed to improve wind direction signal accuracy. A 2MW horizontal-axis wind turbine (HAWT) in Huashan wind farm Hubei Province, is selected as the studied object. From the purpose of quantitative analysis, the zero-point shifting error of wind direction sensor, i.e. wind vane, is firstly constructed to illustrate relevance between sensor data and control signal. The improved SCADA data-driven algorithm is then established to assess the value of misalignment, with the comparison of conventional method. Besides, field test and numerical analysis of misalignment calibration with nacelle-mounted lidar are carried out in the case studies, the calibration of both data-driven algorithm and LiDAR test show great consistency, while offset between LiDAR and wind vane measurement is unexpected. Result and following illustration of the proposed test is presented in conclusion, that data-driven algorithm could be used to assess yaw misalignment and refer to the calibration.
Observer-based Adaptive Sliding Mode Tracking Control for Soft Bending Actuators

This paper investigates the challenging problem of finite-time position tracking control problem for the soft bending actuators without angular velocity measurements, in presence of unknown model uncertainties and external disturbance. To improve the performance of system and simplicity of controller, this paper utilizes an accelerated super-twisting algorithm (STA) to construct a gain-adaption observer and a controller simultaneously. By the observer, the condition on the derivative of uncertainties is relaxed, and the adaptive law leads to attenuate the observer gains overestimation. Next, an observer-based adaptive STA control strategy is designed to attenuate chattering phenomenon and be robust for the system uncertainties. The finite-time convergence of the proposed control strategy is proved by the Lyapunov method. Under the proposed controller, the tracking errors and estimation errors all tend to zero in finite time in the presence of external disturbance, and all the signals and states are bounded. Simulation results verify the proposed controller.