Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Many-Objective Flexible Job Shop Intelligent Scheduling Problem Based on Improved NSGA-III

With the development of intelligent manufacturing and the customized product demand of customers, manufacturing enterprises are urgently required to carry out high-efficiency, high-quality, flexibility and low-cost manufacturing to enhance the competitiveness of enterprises. Intelligent job shop scheduling problem is the core decision of intelligent manufacturing production management. Many-objective job shop scheduling algorithms can effectively solve this problem. However, existing optimization algorithms cannot effectively solve many-objective flexible job shop scheduling problem. This paper establishes the many-objective job shop intelligent scheduling model with complex constraints, and proposes an improved intelligent decision optimization algorithm named NSGA-III-APEV based on NSGA-III. This algorithm uses the penalty-based boundary intersection distance that takes into account both convergence and diversity simultaneously to define the distance between the population individual and the reference vector in the association operation. This paper exploits the penalty-based boundary intersection distance-based elimination mechanism to preserve individuals and reduce the computational cost in the individual preservation strategy. Meanwhile, the adaptive mutation strategy based on consanguinity is employed in genetic operators. The presented method effectively improves the convergence and diversity of the population. Finally, NSGA-III-APEV with other algorithms was compared through benchmarks. Experimental results demonstrated the effectiveness and superiority of the improved method. The feasibility of the improved method in solving the many-objective flexible job shop scheduling problem are verified by engineering examples.
Stereo Vison and Mask-RCNN Segmentation Based 3D Points Cloud Matching for Fish Dimension Measurement

Fish dimensions information is of great significance for aquaculture. Traditional manual measurement is labour intensive, time-consuming and may infulence the growth of fishes. A Stereo vison and Mask-RCNN based Dimension Measurement Scheme(SMDMS) is proposed to get dimensiones of mutiple fishes without constraining their movement. The SMDMS is consist of three parts including stereo vision, fish instance segmentation, 3D points cloud process. In stereo vison, a binocular camera is calibrated and the captured images are rectified to row-aligned. The 3D information is subsequently calculated by 3D reconstruction with a stereo match method. In detection and segmentation part, a Mask Region Convolution Nerual Network(Mask-RCNN) is trained to achieve fish detection and initial segmentaion simultaneously. The segmentation is then refined with morphological process and an interactive segmentation method called GrabCut. In the last part, the 3D points cloud is extracted from the 3D information according to the refined segmentation and some coordinate transformations are subsequently applied to make the estimation more robust for fishes with various orientations and positions. The transformation is calculated by some fitting process. The estimation result is compared with the manual measurement of fishes with different dimensions. It turned out that the proposed scheme achieved high accuracy both in length and width estimation.
Active fault detection for spacecraft attitude control system

This paper proposed an active fault detection (AFD) method to detect the incipient fault occurred in the spacecraft system. Since there’s no abnormal change in the characteristic of the incipient fault system, AFD can be carried out by injecting a designed excitation signal into the system to activate the minor fault signature. Based on the Youla parametrization, the closed-loop residual signal and the performance criteria are constructed. Then the excitation signal of AFD is optimized to not only realize incipient fault detection, but also affect the system as little as possible. What’s more, the residual evaluation function and the threshold are designed to guarantee the robustness of the proposed AFD method to the external disturbances, which can prevent the false alarms and omission of the incipient fault. The comparative simulations are carried out to verify the efficiency of the proposed method.
Robust control for a quadrotor UAV based on linear quadratic regulator

In the paper, a linear quadratic regulator is developed for the control of a quadrotor subject to external disturbance. The nonlinear mathematical model of quadrotor is converted to a linear model by using feedback linearization technique. The control inputs of quadrotor system are derived based on linear quadratic regulator (LQR) theory. The effectiveness of the proposed algorithm has been demonstrated via simulation and real experiment.
Screening the RAPD markers linked to the gene resistant to downy mildew in Chinese wild species of Vitis

The F2 population derived from the cross between the selection Baihe-35-1,resistant to downy mildew and be-longing to a Chinese wild species of Vitis(Vitis pseudoreticulata) and V.vinifera L.cv. Carignane was used as material for screening the RAPD markers linked to the gene resistant to downy mildew.A total of 520 random primers were used to screen RAPD markers which linked to resistance to downy mildew in Chinese wild Vitis.21 random primers could amplify polymor-phic DNA fragments,among which 6 RAPD markers were obtained from the resistant parentage Baihe-35-1 and were cloned and sequenced.They were named as S294-369,S202-527,S382-615,S221-677,S416-1224,S390-725,respectively.Co-segregation analysis of the 6 markers in 111 segregated F2 progenies confirmed that all markers were linked to the gene resistant to downy mildew disease.This provides a molecular basics for the marker assisted selection in downy mildew resis-tance breeding.
Spatial Dynamics of the Public Goods Game with Probabilistic Participation

We consider the stochasticity of individuals’ behavior on participating in the spatial public goods games by endowing cooperators and defectors with two different participating probabilities, and study its effects on the evolution of cooperation. By investigating the dependence of the potential cooperator density on the participation probabilities of cooperators and defectors, we show that cooperation can be promoted in the most parameter space of these two parameters. In particular, full cooperation can be achieved if both participation probabilities are not large. We also find that, by comparing the equilibrium fraction of potential cooperators in an infinitely well-mixed population with that on the square lattice, spatial structure has a sort of double-edged sword effect on the evolution of cooperation in the coexistence dynamic regime; whereas in other dynamic regions spatial structure is beneficial for cooperation. Interestingly, it is shown that cooperation may be unexpectedly inhibited with the increment of the enhancement factor in the spatial public goods game with probabilistic participation. These results can help better understand how cooperation evolves in the structured population where individuals probabilistically participate in the interactions depending on their strategy choices.
Tool Remaining Useful Life Prediction based on Edge Data Processing and LSTM Recurrent Neural Network

The real-time prediction of tool remaining useful life is a challenging problem. This paper proposes a Long Short-Term Memory (LSTM) recurrent neural network model, which is combined with an edge data processing method to predict the tool remaining useful life in real-time. Data cleaning and feature extraction are carried out at the edge node to reduce the transmission time, save the transmission cost and improve the real-time performance of life prediction. After further feature selection in the cloud, a simple three-layer LSTM recurrent neural network model is established. Compared with the tree model and the ordinary neural network model, the experimental results show that the LSTM model has better performance of the tool remaining useful life prediction.
Determination of RMS Current Load on the DC-Link Capacitor of Voltage Source Converters Using Direct Current Control

RMS current load is one of the most important design criteria for the dc-link capacitors of voltage source converters. In the literature, determination of this rms current load is well documented for state-of-the-art modulation schemes, such as space vector modulation. But the methods presented cannot be applied for direct current control algorithms. Therefore, this article introduces a new approach especially for direct current controllers, which reflects and compares to the state-of-the-art calculation method for space vector modulation. As direct current control is a feedback control, no closed-form solution can be given. Instead, the dc-link capacitor rms current load is determined by an iterative calculation scheme as a function of the modulation level and the phase angle. Although the scheme presented is applied to a three-phase two-level voltage source converter, it can be adapted to any voltage source converter topology. An example is presented applying the calculation method to one specific direct current control algorithm. The calculated results are verified via a digital simulation model as well as test bench measurements showing good correlation. For correct comparison, different effects are considered and discussed between simulation, calculation, and measurement results. These contain transients, the commutation process between insulated gate bipolar transistor (IGBT) and diode, the current-dependent voltage drop of the semiconductors, and the stationary current error of the hysteresis control. The dc-link capacitor current load for space vector modulation is calculated analytically as a reference by using the state-of-the-art method. The comparison of results obtained in this article and the reference are very similar, which is discussed in the conclusion.
State Change Trend Prediction of Aircraft Pump Source System Based on GRU Network

Aiming at the problem that the traditional trend prediction method has a low accuracy in predicting the state change of the pump source system due to the high working intensity and bad working environment of the aircraft pump source system, this paper designs a GRU network prediction model and studies the state change trend of the aircraft pump source system. Firstly, the acquired data of aircraft pump source system are preprocessed, and the processed data are reconstructed in phase space to construct the training sample set of network parameters. The network is trained with training samples, and the optimal trend prediction model is created by constantly adjusting the number of hidden layer neurons. The optimal trend prediction model is used to obtain the trend prediction results. In order to demonstrate the effectiveness of the GRU trend prediction model, several other prediction methods have been compared with the GRU in this paper. The results show that GRU trend prediction model has higher prediction accuracy than ARMA model and traditional BP network. Compared with the LSTM prediction model which has similar prediction performance, GRU prediction model has more advantages in model training speed and better engineering application value.
A Multiport DC–DC Converter Based on Two-Quadrant Inverter Topology for PV Systems

This article presents an isolated high-frequency (HF) multiport dc–dc converter for photovoltaics (PV) interfacing systems. In the proposed multiport converter, an HF transformer with multiple input windings and single output winding is utilized. Each input port is based on a proposed two-quadrant inverter topology, which connects a PV module to the HF transformer. The output port is tied to a dc bus using a rectifier. The proposed two-quadrant inverter topology prevents current flow from the ac side to the dc side. This action prevents circulating currents between different ports. Each two-quadrant inverter is controlled to harvest maximum power from its associated PV module. The analysis and operation scenarios of the proposed multiport converter are discussed. PSCAD/EMTDC software package is used to simulate the proposed PV interfacing system to investigate the operation of the proposed multiport converter topology and to examine the dynamic system behavior. In addition, an experimental prototype is developed to validate the operation scenarios of the proposed multiport dc–dc converter and to examine the performance of the presented PV interfacing system.
Event-Triggered Finite-Time Consensus for Nonlinear Leaderless Multi-Agent System with Uncertain Disturbances

This paper focuses on the finite-time consensus problem of disturbed nonlinear multi-agent system, in which the uncertain disturbances are bounded by an unknown constant and the nonlinear dynamics are Lipschitz continuous. Based on finite-time Lyapunov theorem and adding a power integrator technique, an energy-efficient control system is constructed with distributed event-triggered control strategy. It is proved that Zeno behavior can be excluded in the proposed event-triggered protocol. The theoretical analysis is illustrated by simulation results.
FairHym: Improving Inter-Process Fairness on Hybrid Memory Systems

Persistent memory (PMEM) is an emerging byte-addressable memory device that sits on a memory bus like conventional DRAM. A first PMEM product, Intel® Optane™ DC Persistent Memory (DCPM), has a larger capacity and lower cost per gigabyte than DRAM, although its performance is lower than that of DRAM. Therefore, hybrid memory systems that combine DRAM and DCPM for main memory are recommended to take advantage of both of them. However, our previous work revealed significant unfairness between two processes co-running on a real hybrid memory system; the performance of a process accessing DRAM is significantly degraded by another performing frequent writes to DCPM, but not vice versa. In this work, we propose FairHym that is a dynamic frequency scaling technique to improve the inter-process fairness on hybrid memory systems. It decreases the operating frequencies of CPU cores that run a process performing frequent DCPM writes in order to throttle its access frequency and prevent DRAM accesses from being blocked. We implement FairHym as a user-level runtime system and evaluate it with 36 two-process workloads on a real server. The evaluation results show that FairHym improves a fairness metric from 0.66 to 0.86 on average, compared to the default setting that maximizes the frequencies of all cores.
A Novel Positioning System of UAV Based on IMA-GPS Three-Layer Data Fusion

In recent years, the positioning technology for unmanned aerial vehicle (UAV) has been developed rapidly. However, there are still challenges in terms of improving the computational efficiency, accuracy, and robustness of the algorithm for applications in fast flight with UAV. In this paper, we present a novel positioning system of UAV based on IMA-GPS (inertial measurement unit array - global positioning system) three-layer data fusion, which effectively solves the positioning problem of UAV. The first layer is the distributed filtering layer, the unscented Kalman filter serves as the local filter to process the measurements generated by each IMU in the IMA. The second layer is the IMA compensation fusion layer, the data of GPS is introduced to compensate for the deviation of the navigation results calculated by each IMU. The third layer is the GPS-IMA joint optimization layer, where the outputs of the IMA and GPS are fused to obtain the attitude, speed, and position. Simulation results show that the proposed method can realize an integrated navigation system which has low cost, high accuracy, and high computational speed, so that the performance of the UAV positioning system will be improved greatly.
High Performance and Scalable Simulations of a Bio-inspired Computational Model

The Network of Polarized Evolutionary Processors (NPEP) is a rather new variant of the bio-inspired computing model called Network of Evolutionary Processors (NEP). This model, together with its variants, is able to provide theoretical feasible solutions to hard computational problems. NPEPE is a software engine able to simulate NPEP which is deployed over Giraph, an ultra-scalable platform based on the Bulk Synchronous Parallel (BSP) programming model. Rather surprisingly, the BSP model and the underlying architecture of NPEP have many common points. Moreover, these similarities are also shared with all variants in the NEP family. We take advantage of these similarities and propose an extension of NPEPE (named gNEP) that enhances it to simulate any variant of the NEP’s family. Our extended gNEP framework, presents a twofold contribution. Firstly, a flexible architecture able to extend software components in order to include other NEP models (including the seminal NEP model and new ones). Secondly, a component able to translate input configuration files representing the instance of a problem and an algorithm based on different variants of the NEP model into some suitable input files for gNEP framework. In this work, we simulate a solution to the “3-colorability” problem which is based on NPEP. We compare the results for a specific experiment using NPEPE engine and gNEP. Moreover, we show several experiments in the aim of studying, in a preliminary way, the scalability offered by gNEP to easily deploy and execute instances of problems requiring more intensive computations.
Two Consensus problems of Homogenous Multi-Agent Systems with Directed Topology: A Unified Viewpoint

This paper considers both the leader-following and leaderless consensus problems of homogenous multi-agent systems with directed topologies. Different from the existing results, in which these two consensus problems are investigated separately using different approaches, M-matrix theory based approach in the leader-following case and the eigenvalues based approach in the leaderless case, by introducing some related properties of the graph Laplacian matrix, a unified analysis framework for these two consensus problems is first proposed. This fills the gap between these two consensus problems. It is shown that the leaderless case can include the leader-following case as a special case. Not only analytical consensus value but also the disagreement values are also clearly induced here. Since the proposed properties of the graph Laplacian matrix are independent of the graph being symmetric or not, it allows us to overcome the difficulties induced by the asymmetry of the directed topology for analysis. Moreover, the conclusions are obtained with general dynamics and topology conditions, then the analysis framework for consensus problem introduced here can be seen as a fundamental tool to deal with other consensus problems.
Graph Optimization based Visual SLAM fusing KeyPoints and Markers

Simultaneous localization and mapping (SLAM) plays an important role in autonomous navigation for mobile robots. Most of the visual SLAM approaches use keypoints for tracking, whose performance however suffers from the unstable landmarks during task due to uncertain light condition and frequently changeable viewpoint. The situation even becomes worse for visual SLAM in low texture environment especially in indoor buildings, where the supplementary artificial markers can be used to improve the robust detection under a wider range of circumstances. Inspired by this thought, this paper developed a visual SLAM system integrated with keypoints and artificial markers. A graph optimization problem is constructed to optimize the trajectory by taking both the reprojection error of keypoints and the influence of markers into consideration. The experimental results on SPM dataset demonstrate the superior accuracy of the proposed graph optimization algorithm compared with the start-of-the-art ORB-SLAM2.
Distributed Coverage Control for Multi-Agent Systems with Energy Constraint and Balance

Coverage control is one of the main applications of multi-agent systems. In this paper, we study the distributed dynamic coverage control problem for the multi-agent system with second-order Euler-Lagrangian dynamics, considering energy limitation and energy consumption balance. First, EVoronoi partition, i.e., Voronoi partition based on remaining energy is designed. We prove that the EVoronoi partition coverage is distributed, and its local minimum is the optimal location for deployment. Each agent only needs to exchange information with its neighbors, and no global information is needed. Then, we design a control law based on the energy constraints of the agents. It is proved by Lyapunov theory that the control law will make the system converge to the optimal coverage position, such that the coverage is maximum, and it ensure that each agent’s energy is not lower than the specified lower bound, while maintaining the energy balance in the multi-agent system. Simulation results are given to validate the theoretical results.
Moving Target Localization in Multiple-Input Multiple-Output Radar Systems Without the Prior Knowledge of Measurement Noise Powers

This letter investigates the problem of locating a moving target using bistatic range (BR) and bistatic range rate (BRR) measurements in multi-input and multi-output (MIMO) radar systems. The proposed estimator constructs two separate semidefinite programming (SDP) problems, in which both the BR and BRR measurement noise powers can be neglected. By utilizing the optimization toolbox, we efficiently solve the SDP problems and give the source position and velocity successively. Unlike the traditional techniques, the proposed method does not require the exact prior knowledge of the measurement noise powers. Simulation results validate the performance of the proposed method.
Automated System for Chromosome Karyotyping to Recognize the Most Common Numerical Abnormalities Using Deep Learning

Chromosome analysis is an essential task in a cytogenetics lab, where cytogeneticists can diagnose whether there are abnormalities or not. Karyotyping is a standard technique in chromosome analysis that classifies metaphase image to 24 chromosome classes. The main two categories of chromosome abnormalities are structural abnormalities that are changing in the structure of chromosomes and numerical abnormalities which include either monosomy (missing one chromosome) or trisomy (extra copy of the chromosome). Manual karyotyping is complex and requires high domain expertise, as it takes an amount of time. With these motivations, in this research, we used deep learning to automate karyotyping to recognize the common numerical abnormalities on a dataset containing 147 non-overlapped metaphase images collected from the Center of Excellence in Genomic Medicine Research at King Abdulaziz University. The metaphase images went through three stages. The first one is individual chromosomes detection using YOLOv2 Convolutional Neural Network followed by some chromosome post-processing. This step achieved 0.84 mean IoU, 0.9923 AP, and 100% individual chromosomes detection accuracy. The second stage is feature extraction and classification where we fine-tune VGG19 network using two different approaches, one by adding extra fully connected layer(s) and another by replacing fully connected layers with the global average pooling layer. The best accuracy obtained is 95.04%. The final step is detecting abnormality and this step obtained 96.67% abnormality detection accuracy. To further validate the proposed classification method, we examined the Biomedical Imaging Laboratory dataset which is publicly available online and achieved 94.11% accuracy.