Miracle-dz/UniFAR-FTUs huggingface dataset:
Gate-level Circuit Partitioning Algorithm Based on Cut Vertex and Betweenness Centrality

Circuit partitioning is a key link in gate-level parallel simulation. Existing circuit partitioning algorithms usually require the number of cells included in each subset to be balanced first, and secondly, the number of interconnections between each subset is as small as possible. The time and computing resources overhead of gate-level parallel simulation will increase significantly as the number of interconnections between the partitioned subsets increases, and the requirement for balanced result of the partitioning takes second place. To this end, a gate-level circuit partitioning algorithm based on cut vertex and betweenness centrality was proposed, which evaluated cut vertex through betweenness centrality, searched and partitioned based on the optimal cut vertex that met the equilibrium condition, minimized interconnections first, and made the number of logic gates contained in each subset relatively balanced. The experimental results of this algorithm in the actual gate-level circuit partitioning show that compared with the KL algorithm and the METIS partitioning tool, the algorithm can get fewer interconnections.
Resilient flight control for unmanned aerial vehicle based on neural network and disturbance observer

In this paper, a resilient flight control scheme is proposed for the unmanned aerial vehicle (UAV) with system uncertainties, external disturbances, actuator faults and controller gain perturbations. Radial basis function neural network (RBFNN) is employed to tackle actuator faults and system uncertainties. The nonlinear disturbance observer (NDO) is used to estimate the approximation errors of RBFNN and the external disturbances. By using RBFNN and NDO, the resilient flight control scheme is developed for the UAV attitude system based on the command filtered backstepping control method. An optimization method using the linear matrix inequality is studied for the feedback gain matrix design under gain pertubations. Under the resilient flight control scheme, the uniformly ultimate bounded convergence of all closed-loop signals is guaranteed via Lyapunov analysis. Simulation results show the effectiveness of the proposed resilient flight control scheme.
Modeling and Research of Drivers’ Learning Process

Understanding the human driving process is one of the key factors in human-robot co-driving. In the real driving process, participants in driving are of varying levels and show different driving styles. In this paper, a driver learning model is built based on the students who are learning to drive, by observing the changes in the driving skills and styles of them in the process of learning. And a driver self-learning iterative process is built to characterize the learning level improvement process of the students. The simulation results show that the different parameter combinations of the learner driving model can represent drivers of different levels and driving styles, and the learner model reflects the self-learning ability during the training process.
Hybrid Beamforming Design with Finite Resolution Phase Shifters in Multi-User Systems

Hybrid beamforming is a key technology in the next generation wireless communication. It achieves similar performance as full digital beamforming with much reduced power consumption and hardware complexity. Compared with fully-connected hybrid beamforming architecture, the partially-connected architecture is of particular interest as it further simplifies the hardware design. In this work we study the partially-connected beamforming with finite resolution phase shifters in downlink massive MIMO systems. Fairness or the quality of service (QoS) of each user is considered. In this prob-lem, partially-connected architecture and finite resolution phase shifters put additional constraints to the system. We propose an efficient penalty-based algorithm to jointly optimize the digital and analog beamforming, which yields analog beamforming in discrete values. Numerical results show that the proposed algorithm performs better than other benchmark algorithms. It achieves satisfactory performance with low resolution phase shifters.
Service-Oriented Topology Reconfiguration of UAV Networks with Deep Reinforcement Learning

The high mobility of UAVs makes it flexible to provide on-demand service function chains (SFCs) for users in a large geographic area where the terrestrial network is usually not available. Considering sequential and sparsely geographically distributed service requests, the UAV network topology should be dynamically adjusted to provide guaranteed quality of services. This is especially challenging since the UAV movement, data transmission, and virtual function deployment and computing are highly coupled. In this paper, we investigate the topology reconfiguring of UAV networks to construct SFCs by jointly programming multi-UAV trajectories intelligently. Specifically, the dynamic UAV-SFC construction problem is formulated to maximize the net benefit of constructing the SFC by optimizing the UAV trajectory. The net benefit is defined as the delayed benefit deducting energy consumption costs. Then, we propose a deep Q-network (DQN)-based algorithm for real-time decision-making of multi-UAV actions to program multi-UAV trajectories jointly. Simulation results show that our proposed approach of topology reconfiguration can significantly reduce the delay in completing services and save the energy consumption of UAVs.
A parallel approach to the fractional time delay model for predicting the spread of COVID-19

Due to the importance of forecast accuracy for diseases such as COVID-19, the existence of a mathematical model is particularly important. In this research, first, a model to describe the spread of the COVID-19 pandemic is examined. This model is based on a fractional ordinary differential equation. Then the predictor-corrector numerical method is presented to solve this model. Due to the computational challenge of numerically solving fractional models, a task-parallel approach with coarse granularity is presented to solve this model on shared memory systems. The initial data for testing the proposed approach is the data reported on December 31, 2019 by the Wuhan Municipal Commission of the outbreak of the COVID-19 pandemic in the city of Wuhan, China. The numerical results obtained from the proposed parallel approach show that the speedup of the parallel method compared to the sequential method reaches 2.76 in the prediction of 1000 days.
Citation Count Prediction Using Different Time Series Analysis Models

The paper helps to predict the future citation value of a fresh dataset of research papers by considering the past values of the citation count of paper using univariate time series analysis models and evaluating their performance through various evaluation metrics. It is important to predict future citation count as it helps to assess researcher's achievements, promotions, fund allocation, etc. This research is in addition to past research where for prediction, different parameters like content of paper, author details, venue impact etc. were considered. The real and original data for the dataset was extracted from the Google Scholar profile of top ranked authors. Three models of time series, Autoregressive Integrated moving average(ARIMA), Simple exponential smoothing (SES), and Holt winter's exponential Smoothing (HWES) are applied to observe the result variations. The models obtained error metric values for the complete dataset. All four-evaluation metrics were calculated. The best results for the predictions for citation count were obtained from the Simple exponential smoothing and Holt winter's exponential Smoothing models, whose values were almost the same for all evaluation metrics because of almost no change in formula. Among all fourerror metrics mentioned in the design, MASE gave sensible results, with almost all values being less than 1. The results showed similar graphs for both Simple exponential smoothing and Holt winter's exponential smoothing models for actual and predicted values of citation count as there is negligible difference in formula.
Artificial Intelligence Powered Chatbot for Mental Healthcare based on Sentiment Analysis

The importance of good mental health has been realised in recent times especially after the advent of the pandemic which has further worsened the mental health conditions. An individual facing mental disorders or having a tough time mentally often faces social stigma. Hence Chatbots can potentially prove to be a powerful tool for such people who are afraid of stigmatisation. This paper has been divided into two parts. The first part covers a detailed literature survey of the work that has been carried out in the field of medical chatbots and the algorithms that have been used in the proposed approach. The second part covers the implementation of the proposed system and the results obtained followed by the conclusion and future scope. Bidirectional LSTM model was used for performing sentiment analysis on user's text and an accuracy of 80.88% was obtained. The chatbot functions on the basis of a neural network model that provides a minimal loss.
CLUSTERING SENTENCE LEVEL TEXT USING A HIERARCHICAL FUZZY RELATIONAL AND K-MEANS CLUSTERING ALGORITHM

Clustering is one of the widely used data mining techniques and most important un-supervised learning technique. Most of the clustering methods group data based on distance and few methods cluster data based on similarity. In this paper, similarity relationship among relational input data with similar expression patterns are considered so that a consequential and simple analytical decision can be made from the proposed Hierarchical Fuzzy Relational K-means Clustering Algorithm (HFRECCA). This work is an extension of FRECCA which is used for the clustering of text data. HFRECCA and K-means clustering algorithm are compared with each other on the basis of various parameters such as entropy, purity and V-measure. Experimental results prove that the proposed method is better than the existing method.
Highlighting Prominent Features for Size Reduction in Time Series Data using Clustering Techniques

There exist many techniques for feature selection and reduction to reduce dimensions of the large sensor dataset. For real time data processing, compressed and prominent feature of highest significance is desirable for efficient way of resource optimization and computation cost reduction. The goal of this research study is to highlight most significant feature of the dataset and to generate compressed time series by highlighting it. The highlighted feature of accelerometer sensor dataset is extracted, and a more compressed form of time series is generated using statistical and clustering methods like k-means, Partition around Medoids (PAM), Max-Value, 95% Confidence Interval values and Ceil Function calculations. As a result, around 80 % reduction in dataset with the similar pattern as of original time series is achieved. The original time series is compared with generated output series using Dynamic Time Warping method, where, we have obtained normalized error distance of 0.02. (Accuracy 98%)
Open-ended Microwave Biconical Cavity for the Determination of the Average Electron Density in a Low-temperature Plasma

Studies have been conducted with the goal of obtaining an estimate of the range of applicability of open-ended biconical microwave resonators for determination of the average electron density in low-temperature plasma. It is shown that open-ended biconical microwave resonators make it possible to measure the plasma electron density over a wider range than cylindrical resonators.
Ag-Modified Ti3C2Tx MXene as Sensing Material for Low Concentration H2S Detection

The study synthesized Ag-doped Ti<inf>3</inf>C<inf>2</inf>T<inf>x</inf> by self-reduction of Ag<sup>+</sup> and the formed Ag-Ti<inf>3</inf>C<inf>2</inf>T<inf>x</inf> chemiresistive-type sensors exhibit an excellent sensitivity and recovery velocity to different concentrations of H<inf>2</inf>S from 0.1 to 10 ppm at room temperature. The promoted sensing performances were attributed to two main factors. On one hand, the introduction of Ag nanoparticles expands the specific surface area of two-dimensional Ti<inf>2</inf>C<inf>2</inf>T<inf>x</inf> MXene, on which exposes more active sites for H<inf>2</inf>S molecule to adsorb. On the other hand, Ag NPs play a role of capturing and releasing electrons to regulate the electron depletion layer with Schottky barrier formed between Ag NPs and Ti<inf>3</inf>C<inf>2</inf>T<inf>x</inf> MXene. Furthermore, the sensitivity in of H<inf>2</inf>S from 0.1ppm to 10ppm has a linear relationship with concentration and Ag-Ti<inf>3</inf>C<inf>2</inf>T<inf>x</inf> sensor exhibits an extremely fast recovery time of ~5s in RT. This study provides a possibility to take Ag-Ti<inf>3</inf>C<inf>2</inf>T<inf>x</inf> as promising sensing materials for low-concentration H<inf>2</inf>S detection.
Grade Monitoring using Semantic Features of Flotation Froth Image

In the industrial flotation circuits, grades are the key performance indicators for flotation condition recognition and operations. Modeling based on machine vision is an effective tool for grade monitoring, and froth image feature extraction as a model input plays a crucial role. Given the low efficiency of hand-crafted features and the poor interpretability of high-dimensional features extracted by convolutional neural networks, a grade monitoring method with human-understandable semantic features as input is proposed in this paper. First, based on the combination of generative adversarial networks and encoder, we design a new network structure that maps the froth images data space to the latent semantic space. Then the semantic features of froth images can be automatically extracted by decomposing the constructed matrix with rich latent semantics. Finally, as demonstrated in the industrial experiment, the extracted semantic features can not only be visually interpreted but also can be effectively used in grade monitoring.
Motor Imagery EEG Recognition Based on Weight-Sharing CNN-LSTM Network

The brain-computer interface technology enables the disabled to control external devices through the motor imagery EEG. Due to the complex changes of EEG in the time domain and frequency domain, classifiers play an important role in EEG recognition. Convolutional neural network is an excellent deep learning method, but most papers usually use one-dimensional convolution to identify EEG, and rarely consider comprehensive feature extraction and classification of time-frequency map through two-dimensional convolutional network. In this article, the time-frequency graphs of different EEG channels are superimposed by referring to the color dimension of the picture. A weighted shared two-dimensional convolutional CNN-LSTM network is proposed, which shares convolution kernels for feature maps of different channels. Compared with CNN and CNN-LSTM, the weight-sharing CNN-LSTM reduces the amount of calculation, speeds up the network training and improves the classification performance, the highest accuracy rate is 82.3%.
Reliability and Aging Analysis for Power cables

The paper proposes a method for life estimation of power cable under different conditions. Due to the complex application environment, the power cable is easy to have aging issues and lead to the failure of the entire system. Especially when it is used in a high temperature and high humidity environment, it will accelerate the aging of the cable. When the electrical parameters of the system transient changes dramatically, the operating temperature on the cable will rise sharply, thus affecting the reliability and service life of the cable. Therefore, it is necessary to carry out an aging analysis to predict the service life of the cable. The method is based on Arrhenius model, which predicts the life of the cable by detecting the leakage current and discharge characteristics at different temperatures and humidity.
Constrained Neural Adaptive Control for Scramjet-Powered Hypersonic Space Vehicle

A neural adaptive control for hypersonic space vehicle (HSV) with angle of attack (AOA) constraint is investigated. The control system is decomposed into velocity subsystem and altitude subsystem. For velocity subsystem, a neural adaptive controller is proposed to handle the saturation problem of the scramjet input (i.e., fuel-to-air equivalency ratio, FER). Aiming at the AOA constraint problem of the altitude subsystem, a neural adaptive controller based on barrier Lyapunov function is designed for altitude tracking. Simulation shows that the proposed control has fast tracking performance and can synthetically deal with constrained AOA, model uncertainty and external disturbances.
New Single-Phase Voltage Source Inverter Based on Modified Cuk Cell with High Efficiency

The application of solar panel in utilizing the generated DC source requires a DC-AC converter (inverter) so that it can be used by the load. In general, conventional inverters have disadvantages, namely square waveforms and high-power losses so that efficiency is small. The general solution that can overcome these shortcomings is to add large filters at the input and output. This paper proposes a voltage source inverter based on modified Cuk cell so that it can produce a sinusoidal output voltage and high efficiency. The proposed topology does not require the additional of input and output filters. Analytical expressions have been derived to confirm the operation of the proposed inverter. Experimental works has been carried out in the laboratory to verify the proposed inverter.
Interaction between a spatially growing turbulent boundary layer and embedded streamwise vortices

The interaction between a zero-pressure-gradient turbulent boundary layer and a pair of strong, common-flow-down, streamwise vortices with a sizeable velocity deficit is studied by large-eddy simulation. The subgrid-scale stresses are modelled by a localized dynamic eddy-viscosity model. The results agree well with experimental data. The vortices drastically distort the boundary layer, and produce large spanwise variations of the skin friction. The Reynolds stresses are highly three-dimensional. High levels of kinetic energy are found both in the upwash region and in the vortex core. The two secondary shear stresses are significant in the vortex region, with magnitudes comparable to the primary one. Turbulent transport from the immediate upwash region is partly responsible for the high levels of turbulent kinetic energy in the vortex core; its effect on the primary stress 〈u′v′〉 is less significant. The mean velocity gradients play an important role in the generation of 〈u′v′〉 in all regions, while they are negligible in the generation of turbulent kinetic energy in the vortex core. The pressure-strain correlations are generally of opposite sign to the production terms except in the vortex core, where they have the same sign as the production term in the budget of 〈u′v′〉. The results highlight the limitations of the eddy-viscosity assumption (in a Reynolds-averaged context) for flows of this type, as well as the excessive diffusion predicted by typical turbulence models.
A Comparative Study Of Heart Disease Prediction Using Tree-Based Ensemble Classification Techniques

In recent years, the prediction of heart disease has been one of the most complicated tasks in the medical field. Approximately one person dies per minute due to heart disease in the modern era. To help the healthcare industry experts give an early detection in preventing the progression of the disease, Machine Learning offers various algorithms and techniques to achieve this goal. This study compares the performance of tree-based models Random Forest, Decision Tree, Extra Trees, and Gradient Boosting using the Cleveland heart dataset. It investigates whether applying two different ensemble techniques Voting and Stacking, to tree-based models improves heart disease diagnosis performance. The obtained results revealed that the Extra Trees model outweighs the other three models with an accuracy of 92%, whereas the Decision Tree model has the lowest accuracy with 84%. Applying feature selection has enhanced the performance of Random Forest and Gradient Boosting models. As for the ensemble techniques, the Staking ensemble model had the same performance as the Extra trees, with an accuracy of 92%. In contrast, the Voting ensemble model has a lower performance with a 90% accuracy.
Learning to Predict Stability of Visual Features

Visual SLAM usually stores many features and descriptors in the mapping phase. In the case of long-term visual SLAM, however, a large amount of the features in the map are unstable. In this paper, we propose a new method to predict the cross-seasonal stability of visual features. Our method is easy to incorporate with the mapping phase and retains those visual features with high repeatability, an essential property in the face of irregular environmental changes. We train the network and conduct experiments on a long-term outdoor data set. The experimental results show that our method can maintain a good prediction in different environments.
An Improved SLAM Based On The Indoor Mobile Robot

To solve the problems of trajectory drift and low precision in synchronous positioning and map building of indoor mobile robots, an improved depth SLAM algorithm is proposed. By adding scale and rotation invariant information to image features, the algorithm can obtain robust feature matching point pairs in the case of jitter and rotation. According to the set of matched point pairs, a hybrid PnP-ICP algorithm is used to estimate the pose transformation matrix. At the same time, the dictionary is built by the word bag algorithm and the image is solved by the word frequency-inverse document frequency algorithm. Similarly, loop detection is constructed to effectively eliminate the influence of accumulated errors, better solve the problem of trajectory drift, and achieve relocation in the case of lost tracking. Experiments show that the root means a square error of the translation matrix and rotation matrix is 0.043 m and 1.84 deg, and the average processing speed is 17.6 frames/s. Compared with the RGB-D SLAM algorithm in the literature, the accuracy of this algorithm is improved by about 60%, which can meet the functional requirements of realtime high precision positioning and map building for indoor mobile robots in larger scenes.
Two-Branch Convolutional Neural Network Based on Multi-Source Information for Flotation Overflow Identification

The overflow identification of flotation process is important to ensure the flotation quality and improve the comprehensive economic efficiency of the enterprise. However, direct monitoring of flotation froth overflow is difficult due to the strong nonlinearity, information redundancy and uncertainty of industrial processes. To solve the above problems, a two-branch convolutional neural network model (MSI-two-branch CNN) based on multi-source information is proposed. By merging multiple network branches, the features of industrial processes can be extracted effectively. In addition, the one-dimensional time series are transformed using Gram angular domain (GAF) to generate two-dimensional images, which not only can introduce process variables into the overflow recognition model, but also can improve the recognition efficiency by using the correlation between variables. Finally, the method was applied to the gold flotation process. The experimental results show that MSI-two-branch CNN can perform overflow recognition with high accuracy and efficiency in a practical industrial context.