Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault Diagnosis for Robotic Fish Sensors Based on Spatial Domain Image Fusion and Convolution Neural Network

The accurate detection of faults in robotic fish allows for improving the safety and reliability of its operations. This paper proposes a depth sensor fault diagnosis method based on Gramian Angular Field Fusion and Convolutional Neural Network (GAFF-CNN). Firstly, the depth sensor signals are augmented by a sliding window with overlapping data. Secondly, the one-dimensional time series sensor signals are converted into two-dimensional images by using Gramian Angular Field (GAF). To improve fault diagnosis accuracy and accelerate the training speed, using a weighted fusion method to fuse Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF). After that, the model of CNN is established to train and test fused images for fault diagnosis. The result shows that the fault diagnosis accuracy is the highest at 97.22% when using a weighted coefficient of 0.3, and when the weighted coefficient is 0.4, the training speed is the fastest.
Combining Multi-scale and Self-Supervised Features for Speech Emotion Recognition

Speech emotion recognition (SER) plays a crucial role in classifying emotional information conveyed through audio signals, providing more accurate and convenient solutions for human-computer interaction, emotional analysis and other fields. The recent research has focused on training Transformer-based models on human-annotated emotional datasets to capture long-range dependencies by modeling fixed-scale feature representations, and processing time-varying spectral features as images. However, extracting efficient and robust common speech features from small-scale datasets is challenging, and dealing with scale variance is difficult due to the lack of inherent inductive bias (IB). To address these challenges, this paper proposes a noval architecture that extracts Multi-Scale features from raw signals and embeds them into a Self-supervised Features, i.e., MSSF. Technically, this paper first designs a spatial pyramid reduction cell that combines rich multi-scale speech features by utilizing multiple convolutions of different kernel sizes. Next, these features are embedded into a pre-trained self-supervised model to obtain multi-scale, discriminative, and common features for SER tasks. The predicted labels are then output through the final classification head. Additionally, this paper designs a convolution block in parallel, and its features are fused and fed into the multi-scale features. Finally, MSSF is fine-tuned on the benchmark corpus IEMOCAP for four emotions. Compared to previous methods, our proposed model demonstrates improvements on four common metrics, indicating its superiority.
UWIA-AODV: Improved Ad-Hoc On-Demand Distance Vector Routing Protocol for Underwater Acoustic Sensor Networks

In the past a few years, underwater acoustic networks have developed as a powerful technique used for variety of applications. Ad-Hoc On-Demand Distance Vector (AODV) is a popular and effective routing protocol, while it is not suitable for underwater usage due to the features of underwater acoustic channel such as low data rate, long delay, limited bandwidth and energy. In this paper, we propose an improved AODV based on implicit acknowledgement which employs broadcast features of nodes to overhear packets and retransmit those that fail to be transmitted. In addition, residual energy is considerded for energy balancing. Finally, the in-field experiment results shows that our proposed protocol can select the route with more residual energy for energy balancing. And our proposed protocol also outperforms AODV in terms of throughput and packet discovery ratio. So it is more suitable for underwater scenarios that require high transmission reliability.
Automatic identification algorithm of marine radioactive nuclides based on convolutional neural network

Currently, the primary means of automatic and continuous marine radioactivity monitoring is through sensors based on NaI(Tl) scintillation crystal. The identification of radioactive nuclides in seawater can be accomplished through analysis of the gamma-ray spectrum obtained by the sensor. However, conventional peak-seeking-based nuclide identification methods have problems, such as complex data preprocessing operations and low identification efficiency for weak and overlapping peaks. In light of these challenges, this study proposed a new method based on a convolutional neural network(CNN) to analyze the gamma-ray spectrum of seawater. The technique utilized in this study involves converting one-dimensional spectrum data into grayscale images using the grayscale image conversion method. This method is conducive to convolutional neural networks' powerful and flexible feature extraction and classification learning capabilities. By developing a multi-layer CNN model, we can achieve the goal of accurately identifying radioactive nuclides in the ocean. Simulation experiments have confirmed the effectiveness of this approach in automatically identifying radioactive nuclides in seawater, with satisfactory identification performance for weak or overlapping peaks that meet practical application requirements.
Optimal Formation Tracking Control for Multi-UAVs System with Uncertain Dynamics and Unknown Disturbance

This paper addresses the optimal control problem for multi-UAV system with uncertain dynamics and unknown disturbance. Firstly, a finite-time extended state observer is developed to solve the external disturbance problem. The compensation control is used to compensate for the influence of the disturbances. Secondly, the optimal control of the nominal system is obtained by the adaptive dynamic programming (ADP) method. Based on the output of the finite-time extended state observer, compensation control is used to compensate the disturbance to reduce its influence on system performance. By utilizing the Lyapunov's method, the stability of the closed-loop system and the convergence of the estimation weight for critic network are guaranteed. Finally, the simulation results show the effectiveness of the method.
Glioblastoma Cancer Stem-like Cells – Implications for Pathogenesis and Treatment

Glioblastoma remains one of the deadliest forms of cancer. Infiltrating cancer cells in the surrounding brain prevent complete resection and tumor cell resistance to chemoradiation results in the poor prognosis of the glioblastoma patient. Much research has been devoted over the years to the pathogenesis and treatment of glioblastoma. The tumor stem cell hypothesis, which was initially described in hematopoietic cell malignancies
Steel Surface Defect Detection Based on Improved YOLOv5

There are some problems in steel surface defect detection in industrial production, such as low detection rate and high missed detection rate. An improved YOLOv5 detector is proposed, which adds multi-scale attention mechanism in the feature pyramid structure, so that the model can pay more attention to the interesting region. In the process of down-sampling, the detailed information of the image is compressed, which may lead to the loss of accuracy. A Bottom-Up Fusion (BUF) module is used for down-sampling to obtain rich semantic information of deep network. The experimental results show that the improved YOLOv5 model mAP reaches 62.97%, which is 3.76% higher than the baseline, and the detection accuracy of all types has been improved, and there is no big change in parameter quantity
Dual Circularly Polarized Horn Antenna with Bandwidth Enhancement by Truncated Horn and Linear Tapered Curve

Dual Circularly polarized (CP) Pyramidal horn antenna has been designed for high data rate transmission in both C-Band and X-band. The ultra-wide band and dual CP performance has been achieved using linear tapered curve in waveguide region and truncated structure in the horn of proposed horn antenna. The proposed antenna has been simulated by electromagnetic computational tool CST. An impedance bandwidth (IBW) of 84.7% (4.6GHz-11.3GHz) and maximum gain of 20.16 dBi has been achieved in this design. Dual CP has been operated in 4.04-4.67 GHz (14% ARBW) and 5.081-5.81GHz (13.5% ARBW).
Study on Artificial Neural Network Based MPPT Algorithm in PV Application

This paper presents a descriptive study on Artificial Neural Network, and it solves the drawbacks of old MPPT techniques. This study includes a detailed analysis of the fundamental principles and operational aspects of ANN. The MATLAB Simulink is used to simulate the PV module, DC-DC boost converter, and the ANN MPPT algorithms of the MPPT control system. The simulation also compares the system's performance under varying solar irradiation rates, both fast and slow. A comparison is made with Perturb and Observe (P&O) MPPT method. The results on simulation demonstrate that ANN-based MPPT outperforms the P&O method in terms of efficiency and accuracy, particularly under dynamic weather and shading conditions. The proposed study provides a comprehensive understanding of the benefits and limitations of ANN and P&O MPPT methods and highlights the potential for future research.
Evaluation of Emotion Recognition in Facial Image Using Attentive Deep Convolutional Neural Network

Emotion recognition in facial images is a topic that has attracted many interests. Research on emotion recognition in facial images continues to face challenges such as variations of human faces due to environments or situations. Of all the techniques that have been used, the Deep Convolution Neural Network (DCNN) method reports a significant improvement compared to traditional machine learning methods. In recent years, self-attachment mechanisms have increased in popularity in DCNN, especially in Natural Language Processing. In this study, we propose to add attention networks to DCNN method to improve the performance of emotion recognition. By incorporating self-attention mechanisms into the model architecture, the network is able to recognize which facial features are most relevant for each emotion, increasing the accuracy and interpretation of the model. This study reports an accuracy of 69.55% which is 11.15% higher than using the traditional DCNN model.
Fast Robust Switching Control Scheme for PWR-Type Nuclear Power Plants

In sophisticated and complex systems such as nuclear power plant, maintaining the system's stability in the presence of uncertainties and disturbances is the most challenging problem. Thus, to assure the satisfactory and safe operation, this work proposes a novel fast robust optimal switching control strategy for pressurized water reactor-type nuclear power plants. The proposed control strategy guarantees a substantial degree of robustness, fast dynamic response over the entire operational envelope, and optimal performance during nominal operation of the plant. A bank of controllers is designed which consist of baseline controller, an optimal controller, and a robust controller. At any instant of time, the most suitable controller from the bank of controllers is selected using the switching logic unit that designates the controller by monitoring the health of the plant or transients. Simulation studies have been performed that demonstrate the applicability and effectiveness of the proposed switching control scheme over some conventional control techniques used to design it.
Real-Time Digital Simulation for Investigating Transient Stability of Power System Network

This paper examines the power swing explicitly before and after a fault, which plays a crucial role in stabilizing the power system network during transient conditions. It analyzes the transient stability of the IEEE 5 bus network during a three-phase symmetrical fault occurrence and load variation. The purpose is to limit voltage transients during the fault by selecting appropriate Critical Clearing Time (CCT) and breaker resistance. Additionally, the article investigates the impact of wind generator integration on the voltage and frequency improvement of the power system network during load variations. The study employs a Real-Time Digital Simulator (RTDS) and an open-source software Power World Simulator (PWS). Phasor Measurement Units (PMUs) are utilized to monitor the voltage and frequency of the network. The accuracy and effectiveness of the results obtained from the RTDS and PWS are compared to validate the power system transient stability analysis.
A Novel Attitude Control Strategy of Reusable Launch Vehicle via an Improved TD3 Reinforcement Learning

Reusable launch vehicle (RLV) has become a research hotspot in the field of aerospace because of its low cost, fast and reliable. However, since unknown disturbances and complex aerodynamic characteristics, the control system design of RLV is a challenging task, especially in the reentry phase. In this study, a model-free deep reinforcement learning controller is proposed, which can self-learn according to interacting with the environment and realize the attitude control of RLV without repeatedly adjusting the controller parameters. Considering the continuity of state space and action space, an improved Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is adopted for RLV reentry attitude control. For high-quality simulation of RLV control, a scenario of RLV reentry interaction environment is created by Unity3D for agent learning. The performance of the proposed algorithm is verified by simulation results.
HCRI: A ROS2 Human Collision Object Interface for Robotic Manipulation Planning

Human-Robot Collaboration (HRC) is people and robots work together in the same workspace to achieve shared goals. To ensure safety, robots need to perceive the position of the human body for planning purposes. However, there is no universal method for converting the human body into collision objects. To solve this problem, we propose an interface to convert the human body segments into collision objects in the robot planning scene. The interface estimates the human body Skinned Multi-Person Linear model (SMPL) by RGB image and measures the collision object size of the corresponding body segment. The corresponding size of collision objects are added to the corresponding positions of the human pose estimated by different types of sensors. The accuracy of human size estimation is verified on TotalMocap of AMASS. The interface with OctoMap is compared to evaluate the efficiency and the validity on interface is verified on the robot UR5e.
m-Segment Printed Log Periodic Dipole Array Antennas

In this paper, a miniaturized printed log-periodic dipole array (PLPDA) with a bandwidth of 1.3 – 6.0 GHz is presented. The miniaturization is achieved through m-segment fractal geometry. The m-segment PLPDA antennas presented in the paper has achieved a 24.32% width reduction when compared to the conventional PLPDA. Due to the low fabrication complexity of the m-segment fractal, and also due to the minimal effect it has on the bandwidth and gain performance of the antenna, this technique is an ideal choice for LPDA miniaturization.
Enhancing the Reliability of Hybrid MTJ/CMOS Circuits with Auto Write Termination

Magnetic tunnel junction (MTJ) is a commercially attractive choice for the non-volatile memory and hybrid MTJ/CMOS logic circuits due to their notable features, including non-volatility, scalability, low power, high speed, and 3D integration with CMOS. Despite this, process variation and stochastic switching pose a significant barrier to the commercialization of MTJs due to reliability concerns. In this work, we have improved the MTJ's reliability by incorporating the auto-write termination (AWT) scheme into the conventional writing circuits. Here, writing/switching failures are used to define reliability. The conventional four-transistor (4T) write circuit with the proposed AWT circuit enhances the reliability of MTJs by continuous monitoring of the writing/switching operation and prevents redundant MTJ writing, thereby reducing the write failures significantly. The AWT scheme reduces the write failures by almost 99% less, as compared to the conventional write circuit.
A Hybrid Approch for Noise Suppression Based on Recurrent Neural Network

Neural network is widely used in machine learning, aiming at the problem that the traditional noise suppression methods are not accurate enough in noise recognition. This article introduces a speech enhancement method based on the principle of spectral subtraction using the recurrent neural network (RNN) model. In the feature extraction process, Bark spectral coefficients are used so that reducing numbers of the features of the neural network model to reduce the computational complexity. The three steps in spectral subtraction are replaced with RNN model to improve recognition accuracy, constructing the gated recurrent unit (GRU) model by introducing update and reset gates into the RNN. Synthetic speech data and collected speech data are used for training to determine the parameters of each layer of the network and avoid problems such as vanishing gradients. The model's effectiveness is analyzed in test scenarios of canteens and convenience store with background noise on campus, and the RNN model shows more significant denoising effects compared to traditional spectral subtraction methods.
Clinical evaluation of BD Veritor TM SARS-CoV-2 and Flu A+B Assay for point-of-care 1 (POC) System

Differential diagnosis of COVID-19 and/or influenza (flu) at of care is critical for efficient 31 patient management and for either of these diseases. Clinical performance of the BD 32 Veritor™ System for Rapid 33 triplex assay was characterized. The performance for SARS-CoV-2 detection was determined 34 using two hundred and ninety-eight (298) specimens from patients reporting COVID-19 35 symptoms within 7 days from symptom onset (DSO) in comparison with Lyra® SARS-CoV-2 36 RT-PCR Assay (“Lyra SARS-CoV-2”). The Veritor SARS-CoV-2/Flu Assay met the FDA EUA 37 acceptance criterion with 95% overall agreement for SARS-CoV-2 test when compared to Lyra 38 SARS-CoV-2. The performance for Flu A and Flu B detection was determined using 75 39 influenza-positive and 40 influenza-negative retrospective specimens in comparison with the 40 previously FDA cleared BD Veritor TM System for Rapid Detection of Flu A+B (“Veritor Flu”). 41 The Veritor SARS-CoV-2/Flu also demonstrated 100% agreement with the Veritor Flu. 42
Department of Electrical and Computer Systems Engineering

This paper presents a navigation algorithm that enables mobile robots to retrace routes previously taught under the control of human operators in outdoor environments. Possible applications include robot couriers, autonomous vehicles, tour guides and robotic patrols. The appearance based approach presented in the paper is provably convergent, computationally inexpensive compared to map based approaches and requires only odometry and a monocular panoramic vision sensor. A sequence of reference images are recorded during the human guided route teaching phase. During the autonomous phase, current image is compared against the reference image using image cross-correlation performed efﬁciently in Fourier domain to recover the difference in relative orientation. Route following is achieved by compensating for this orientation difference. Extensive experimental results in urban environments presented in the paper demonstrate robustness against lighting variations and occlusion to an extend not seen in other literature. The cumulative distance traveled by the robot in these experiments exceeds 20km.
Knowledge Transfer for Few-Shot Vibrational Spectrum Recognition via Learning Unified Representations

Vibrational spectrum recognition has been playing a key role in many applications of vibrational spectroscopy including explosive detection, drug detection etc. While deep learning methods have shown promising performance in vibrational spectrum recognition, they still suffer from the problem of lacking training data, as there are usually only a handful of samples available for each class. To tackle this small data problem, we propose a novel approach which leverages the power of multi-task learning to help train deep neural networks for vibrational spectrum recognition. To be specific, auxiliary datasets/tasks are chosen to train a DNN along with the primary dataset in the setting of multi-task learning so that a unified representation across multiple datasets can be learned which lead to a superior performance on the primary dataset. Experiments demonstrated the feasibility of the proposed method.
Sparse Attention-based Graph Neural Networks for Traffic Forecasting

Traffic forecasting is a significant task for an intelligent traffic system, which requires models with high predictive power and needs to acquire the exact data dependence. But the complexity of traffic flow data makes this task quite challenging. Previous studies have shown the effectiveness of Transformer in improving the predictive ability of models. Sparse Attentional Graph Neural Network (SAGNN) based on Transformer is proposed for traffic forecasting, using dynamic graph convolution and a new attention mechanism to get the dependencies in the different dimensions, respectively. Specifically, the operations of sparsification and aggregation of contextual information to the self-attention mechanism are added to make it efficient and process the adjacency matrix before graph convolution to simulate the dynamics of the road network. In addition, SAGNN also considers the data's periodicity and optimizes the dynamic decoding process. Two open highway datasets are used to validate SAGNN. And the results show that SAGNN effectively extracts traffic flow spatio-temporal information. The forecasting results outperform the prevalent baselines.
Detection Model of Drainage Pipe Defect Based on Improved YOLOv5

A drainage pipe defect detection model based on improved YOLOv5 is proposed for the problem of leakage and error detection in the manual inspection of drainage pipes. First, the proposed CSSPPF module is added to the YOLOv5 network to enhance multi-scale feature learning, enhance feature map detail information extraction, and improve the detection precision of similar defect objects. Secondly, the attention mechanism CBAM is introduced to suppress the interference of irrelevant tube wall background information and improve the detection accuracy of the model. Finally, the YOLOv5 backbone network structure is modified to introduce a small object detection layer to obtain a larger feature map and improve the precision of the network for detecting small objects. The experimental results show that the precision, recall , and mAP of the improved model are improved by 0.5%, 2% , and 2.5%, respectively, compared with the original YOLOv5 network model. The experimental results show that the precision, recall , and mAP of the improved model are improved by 0.5%, 2% , and 2.5%, respectively, compared with the original YOLOv5 network model. Compared with the mainstream detection algorithms YOLOv4, SSD , and YOLOv7, the mAP of the improved model improved by 26.6%, 12.4% , and 7.5%, respectively. And it has stronger feature extraction ability and higher recognition rate for drainage pipe small object defect detection, which meets the demand for efficient and intelligent drainage pipe defect detection and recognition.
A Game-Based Opinion Dynamics for Multi-Group

In this paper, the problem that aggregation of opinions for several individuals which are divided into two different subgroups is considered. Firstly, we establish a new model for the evolution of all individual opinions in the system. Moreover, it's shown that the designed game admits a unique Nash equilibrium. Secondly, by using matrix theory and game theory, we obtain the sufficient condition for the consensus of opinions with co-evolution over time and issues. Finally, simulation examples are given to validate the effectiveness of the theoretical results.