Reviewerly/exHarmony huggingface dataset:
Batch Bayesian Optimization via Maximizing Variance Change

This paper proposes a new Bayesian optimization (BO) framework to avoid the unclear termination conditions in existing BO methods. Based on this framework, this paper uses Gaussian process as the surrogate model and constructs an acquisition function by minimizing the prediction error on the potential sample set, thus designing the batch BO algorithm via maximizing variance change (BOM algorithm). Simulation and experimental results show the effectiveness of the proposed BO framework and BOM algorithm.
Modified Unbiased Converted Measurement Kalman Filter with Outlier Detection

Aiming at the problem that the tracking performance of nonlinear measurement system is disturbed by outliers, a modified unbiased converted measurement Kalman filter with outlier detection (OD-MUCMKF) is proposed. In this algorithm, each measured value after unbiased conversion is assigned an index variable subject to Beta distribution to identify outlier. By using variational Bayesian method, state estimation is iterated, which is then treated as a new measurement to perform a second unbiased conversion. At this time, the measurement update can avoid the influence of outliers and improve the accuracy of the algorithm. The simulation results show that the proposed filter has the best performance when the nonlinear measurement system is disturbed by outliers.
Multi-Agent Path Finding Based on Graph Neural Network

In order to improve the efficiency of logistics warehouses, multi-agent path finding (MAPF) has become a hot research. Although most of the current decentralized MAPF algorithms can obtain good results in experiments, the agents in the system do not communicate with each other, and do not fully utilize the scalability of decentralized control. The decentralized framework of this paper combines convolutional neural networks (CNN) and improved graph neural networks (GNN) to train a decentralized controller to simulate a coupled central controller. The decentralized framework based on GNN is used to find the path of the multi-agent system, and the optimal or near-optimal agent action sequence is obtained. This paper assumes that under the premise that the agent's hardware conditions are limited and can only perceive the local environment within the field of view. And realize efficient collision-free path planning by exchanging local information observed by agents through the GNN. Experiments show that the results obtained by the path finding algorithm based on GNN in this study are close to the optimal results, and have good scalability.
Curvature-Continuous RRT-Based Path Planning with Enhanced Efficiency

The trajectory composed of straight segments and sharp turns generated by the existing global path planning algorithm does not consider the kinematic and dynamic characteristics of mobile robots. For mobile robots with different characteristics, it is necessary to propose a method to meet its constraints. For mobile robots, when its trajectory is a path with continuous curvature, the difficulty of control will be greatly reduced. Therefore, this paper proposes an improved RRT algorithm named Curvature-Continuous RRT-based (CC-RRT) path planning algorithm using clothoid curve for local smoothing. This method can transform the path generated by RRT algorithm into a curvature continuous path, which is helpful to improve the operating efficiency, operating stability and service life of mobile robot. Finally, Matlab is used for analysis, and the results verify the effectiveness of this algorithm.
Efficiently Solving the Max-cut Problem via a Quantum Qubit Rotation Algorithm

Optimizing parameterized quantum circuits promises efficient use of near-term quantum devices to achieve potential quantum advantage. However, there is a notorious tradeoff between the expressibility and trainability of the parameter ansatz. We find that in combinatorial optimization problems, since the solutions are described by bit strings, one can trade the expressiveness of the ansatz for high train ability. To be specific, by focusing on the max-cut problem we introduce a simple yet efficient algorithm named Quantum Qubit Rotation Algorithm (QQRA). The quantum circuits are comprised with single-qubit rotation gates implementing on each qubit. The rotation angles of the gates can be trained free of barren plateaus. It is demonstrated that the approximation ratio of QQRA can be close to 1 for complete graphs. To illustrate the effectiveness of QQRA, we compare it with the well known quantum approximate optimization algorithm and the classical Goemans- Williamson algorithm.
Three-dimensional UAV track planning based on the GB-PQ-RRT<sup>*</sup> algorithm

Progressive optimal fast search random tree algorithm (Rapidly exploring Random Tree star, RRT *) has powerful random search ability, therefore, it is widely used in the navigation track planning. However, the proposed algorithm still has the problems of low node utilization rate, slow convergence speed and high generation path cost, in order to make up for these deficiencies, a new algorithm of target bias, gravitational potential field and fast RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> fusion (Goal Biased-Potential Quick-RRT*, GB- PQ-RRT*) is proposed. The target bias is introduced to optimize the selection method of the sampling point, so that the search tree grows in the direction of the target point with a certain probability. The gravitational potential field is added to optimize the generation mode of new nodes and speed up the convergence speed of the algorithm; The Q-RRT* algorithm was then combined to optimize the path. The simulation results show that the new algorithm generates shorter, smoother paths and consumes less time in 3D environments.
Deep Learning-Based Anomaly Detection for Time-Series Data in Industrial Control Systems

Industrial control systems(ICS) are widely used in key areas such as industry, energy, transportation, and water conservancy that can control the operation of production equipment. With the widespread use of sensors and actuators in ICS, these systems generate large amounts of multivariate time series data. Currently, there remain significant challenges in anomaly detection in ICS due to the lack of anomaly labels, high data volatility, and the demands of ultra-low inference times. The paper proposes a deep learning-based multivariate anomaly detection method that combines Transform and GAN to detect anomalous behaviours in ICS. The method was tested on the SWaT and WADI datasets collected from industrial control systems, and experimental results show that the model proposed in this paper can outperform state-of-the-art baseline methods in anomaly diagnosis performance and timeliness.
A Self Attention-Based Graph Convolutional Approach for Suspension Magnetization Roasting Soft Sensor Model

Soft sensor methods based on deep neural networks have been widely developed nowadays. However, traditional deep learning models have insufficient ability to construct spatial dependencies between process variables. It is difficult to add prior knowledge of the system to the model, and the model explanation is poor. This paper proposes a new soft sensor method based on Graph Convolutional Networks (GCN). Specifically, firstly, the adjacency matrix is constructed based on the prior knowledge of the connection relationship between entities. Secondly, a Self-Attention Mechanism (SAM) is used to calculate the dynamic correlation strength between connected nodes and capture the correlation between nodes in the spatial dimension. Then, the diffusion process of the graph signal in diffusion convolution is used to simulate the flow of materials in the industrial process. The multi-step signal diffusion process is used to find high-order neighbor nodes, and then the node information is aggregated to model spatial dependencies and extract spatial features. The obtained features are fed into the fully connected layer for regression prediction, and the prediction results are obtained. Finally, an explanation analysis is performed. A practical industrial example demonstrates the effectiveness of the model.
Aircrafts Scheduling Based Target Cooperative Tracking with Bearings-Only Measurements

This paper focuses on the problem of scheduling several aircrafts to accurately track traget while using angle-only measurements. The target kinematics model and the cooperative mesurement model are introduced firstly. Then, the observability analysis is presented by using Posterior Cramer-Rao lower bound (PCRLB), which is a sensor performance metric based on Fisher information matrix. Considering the tracking accuracy and computational complexity, the optimization objective function is established. A method of aircraft scheduling for muti-traget tracking is proposed to obatin the optimal observation combination, and the target motion information is estimated by utilizing extended Kalman filter. Finally, extensive numerical simulations validate the effectiveness of the proposed target tracking method.
Enhanced Data Augmentation for Denoising and Super-Resolution Reconstruction of Radiation Images

X-ray is widely used in security inspection systems for nondestructive testing, aiding inspection staff in identifying dangerous goods. Higher signal-to-noise ratio and resolution in radiation images can significantly enhance the staff’s ability to detect potential threats. This article introduces a novel data augmentation (DA) method aimed at improving the performance of neural network models for noise reduction and super-resolution (SR) reconstruction (such as EDSR, RCAN, and so on). The proposed method enables the models to be region-aware, allowing adaptive processing of different regions with varying degrees of noise or blurriness. Compared with traditional DA methods, the proposed method can effectively prevent the output image from being too smooth or producing artifacts while improving the performance of the models. Experiments indicate that the performance of the models trained with the proposed method shows consistent improvements, with the highest to be 0.26 dB in peak signal-to-noise ratio (PSNR).
Multi-Scale Aggregation and Scene Parsing for Crowd Counting

With the acceleration of urbanization, the number of urban residents increases dramatically, and large-scale crowdy scenes become more common. There are many public security risks in these scenes. Therefore, it is of great significance to do crowd counting in the scenes. To solve these problems, a Multi-scale Aggregation and Scene Parsing (MASPNet) is proposed in the paper. To do the precision test and robustness test, MASPNet was employed on the ShanghaiTech dataset, UCF-QNRF dataset, and UCF Crowd Counting 50 (UCF_CC_50) dataset. The Experimental results demonstrate good performance on counting accuracy and robustness.
VS-DDP: Variable Splitting Based Differential Dynamic Programming Algorithm for Autonomous Trajectory Planning

In this article, we introduce a novel trajectory planning approach for autonomous vehicle, called as Variable Splitting based Differential Dynamic Programming (VS-DDP). Utilizing variable splitting method, VS-DDP can successfully handle the constraints presented in trajectory planning problem, which extremely extends the power of differential dynamic programming. Simulation and real world experiments are conducted to validate the effectiveness and efficiency of VS-DDP. With a comparison between VS-DDP and direct collocation based method existing in famous Apollo project [1], we find that VS-DDP greatly outperforms the comparison method in terms of the computation efficiency.
Few-Shot Image Classification Based on Deep and Shallow Information Metric Network

Few-shot image classification aims to identify new classes of samples that have been seen only a few times, by transferring the knowledge learned on the base classes of images. This paper proposes a new Deep and Shallow Information Metric Network (DSIMNet), improving the existing methods from the perspectives of both feature extraction and distance metric. The proposed method can extract both shallow and deep information of the sample, and set different metric functions for deep and shallow features based on their characteristics, effectively improving the classification capability. The comparison experiments on three popular few-shot benchmarks demonstrate the excellent performance of the proposed method. The ablation study further demonstrates the effectiveness of the proposed DSIMNet.
A Ground-Based Cloud Motion Prediction Method

Cloud conditions play a significant role in affecting the output power of solar photovoltaic (PV) panels, and ground-based cloud images can provide valuable data for cloud monitoring and ultra-short-term PV power forecasting. From the perspective of image sequence prediction, a cloud motion prediction method based on Cascade Causal LSTM is proposed by using the ground-based cloud image data taken by Total Sky Imager, realizing the prediction of cloud contour change. On this basis, digital image processing technology is used for features extraction from the ground-based cloud images, including clear sky irradiance, clusters, color statistics, and texture that affect PV power generation. Finally, the ultra-short-term PV power forecasting model with multi-information fusion is established. Experimental results demonstrate that the proposed PV power prediction method can effectively improve the accuracy of ultra-short-term PV power forecasting. It is further shown that the effectiveness of the proposed cloud motion prediction method.
Distributed Malicious Maneuvering Target Tracking Algorithm With Finite-Time Fusion Analysis

This paper proposes a distributed tracking framework for high-maneuvering and malicious maneuvering target based on the sensor network. Considering some of sensors in the network can only observe partial system output, the collective observability is guaranteed. Based on the collective observability, the distributed target tracking algorithm is designed by local filter phase and consensus fusion phase. In local filter phase, the local estimate results can be obtained by each sensor node. Based on the target kinematic and sensor observation equations, the Kalman-like estimation algorithm is proposed to obtain the local estimate results. In most of the exist distributed state estimation (DSE) algorithms, the excellent target tracking performance is based on the assumption that the fusion times is infinity in consensus fusion phase. In practical application, however, the consensus fusion phase should be completed during two sampling instants. Besides, based on the finite-time stability theory, the error covariance matrix is processed as the solution of discrete-time algebraic Riccati equation (DARE). Then the stability of the proposed algorithm with finite-time consensus fusion is analyzed. Finally, a numerical example for tracking maneuvering target is given to validate the effectiveness.
Cross-Domain Object Detection Based on Attention Mechanism

A sizable training dataset is always necessary to ensure the robustness of object detection algorithms. However, the number of training datasets is limited by the shortage of real sample data and the high cost of labeling, so we want to improve the cross-domain performance of object detection from virtual to real-world domains. In this work, we use Faster R-CNN as the basic model and supplement it with an attention mechanism. It then confirms the effectiveness of the attention module in cross-domain object detection tasks by comparison. Additionally, to further enhance algorithm performance, we create a new attention model based on the structure of the classical attention model, develop several optimization strategies, and evaluate each one through experiments to identify the best model. This model is preferable to the existing attention model cited in this work since it has fewer parameters, a similar computational load, and higher accuracy. It also offers a novel attention-assisted model for research on cross-domain object detection algorithms.
CFSeRec: A Contrastive Framework for Sequential Recommendation

In sequential recommender systems, the main problems are the long-tailed distribution of data and noise interference. A Contrastive Framework for Sequential Recommendation (CFSeRec) is proposed to solve these two problems respectively. Token shuffling and adversarial attack data augmentation methods are used in the framework to improve the quality and quantity of training data, so that the long-tailed problem is mitigated. Through the application of projection head method, the sequence representation becomes more general and robust, rather than just adapted to the task of contrastive learning. Therefore, the impact of noise on sequence recommender systems is effectively alleviated. Experiments on four public datasets show that CFSeRec achieves state-of-the-art performance in the metrics of hit ratio and normalized discounted cumulative gain, when comparing to the seven previous frameworks.
Lightweight Dynamic Hybrid Attention Network for Single Image Super-Resolution

Single image super-resolution (SISR) is of great importance and has found wide applications. In recent years, convolutional neural network (CNN) has been implemented to SISR and achieved great successes. However, many existing CNN-based SR networks improve the performance by stacking a large number of blocks, resulting in more parameters and higher computation cost. To solve this problem, we propose a lightweight dynamic hybrid attention network (DHAN) which is made up of chained dynamic hybrid attention blocks (DHABs). A DHAB is mainly composed of a non-attention branch and a hybrid attention branch based on 3D convolution kernels, and can combine channel attention and spatial attention to extract powerful representations of inter-channel and intra-channel feature response. Furthermore, we design a dynamic weight module of DHAB, which generates two sum-to-one weights according to the input features of each block and dynamically adjusts the contribution of the hybrid attention branch and the non-attention branch. Based on the hybrid attention branch and the dynamic weight module, DHAN can not only efficiently reduce the number of multiply-add operations (Multi-Adds) and the number of parameters, but also accurately recover the textural details of the high-resolution image from the low-resolution feature map. Experiments on benchmark datasets are done to demonstrate that the proposed DHAN achieves excellent performance compared to some state-of-the-art networks of similar sizes.
Combined UGV Path Planning Based on Improved D* Lite and Jump Point Search Algorithms

For the path planning problems of an unmanned ground vehicle (UGV) in complex unknown environments, a combined path planning algorithm based on improved D* Lite and jump point search (JPS) algorithms is proposed in this paper. Firstly, the jump rule and forced neighbor filtering rule of the JPS algorithm are modified, and obstacle and terrain constraints are integrated into the cost functions of D* Lite and the JPS algorithm. A threshold combination strategy based on the UGV detection range is developed in order to incorporate the two algorithms. Finally, the generated paths are optimized by removing redundant paths using jump point information and smoothing the paths with third-order B-spline interpolation. Several different environments are constructed in the Gazebo simulation software. The experimental results show that the proposed algorithm can plan secure UGV paths in complex environments with faster planning speeds and lower memory consumption, and is suitable for path planning in dynamic unknown environments.
A Sound Event Recognition Method of Crop Shear Scrap Falling State Based on Log-Mel Spectrogram and MobileNetV2

The scrap falling state in the shearing process of a Wide Heavy Plate Mill depends on manual monitoring and judgment. The steel plate can only move forward after the scrap falls, otherwise, the equipment will be damaged. An experienced operator can use the sound they hear to estimate the falling state. To automatically recognize sound events, a scheme of sound data acquisition processing and recognition for monitoring the scrap state of the shearing process is proposed in this paper. Firstly, a pickup is installed at the monitoring point of the crop shear to collect the sound. Afterward, the sound data is processed and finally fed into the model for recognition. Since many sound segments in the field are not related to key actions, an endpoint detection technique based on a short-term energy threshold and the variable window length strategy is applied in the data processing section to obtain the sound segments to be identified. The Log-Mel spectrogram feature of the sound segment is then extracted. After a data filling to ensure a uniform data shape, the feature map is fed into a lightweight deep convolution neural network called MobileNetV2 to quickly identify sound events. For the shearing process events, the model recognition accuracy can reach 95% of the F1 score in a realistic noise environment and we can identify the scrap falling 0.3s after the action starts. Finally, the model was applied to the UrbanSound8K dataset and achieved 95% recognition accuracy, validating the performance of the method.
Research on UAV Vision Landing Target Detection and Obstacle Avoidance Algorithm

Aiming at the problem of target recognition and obstacle avoidance during Unmanned aerial vehicles (UAVs) autonomous landing in a complex environment, a solution based on target detection and obstacle avoidance is proposed. Firstly, the UAV obtains the position and attitude of the target through the recognition of the cooperative QR code. Secondly, in the process of the UAV moving toward the target point, the control algorithm is used to impose constraints and optimize the trajectory to ensure the safety of the trajectory and the feasibility of higher-order dynamics. Finally, a simulation example is given to verify the effectiveness and practicability of the control algorithm.
YOLODM-Net: You Only Look Once Based on Depthwise Convolution and Multi Scales Features Fused

Most of the existing object detection systems adopt 3x3 convolution kernels for feature extraction, which leads to a problem that the receptive field of the features extraction net is always 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sup> • Network features are not rich enough and lack accurate learning of features with pixel size not 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sup> • To solve this problem, convolution with different kernel sizes is introduced for feature extraction. However, a large convolution kernel may lead to a rapid increase in the Parameters and FLOPs. In this paper, we propose an object detection network based on depth-wise convolution and multi-scale feature fusion (YOLODM-Net). Specifically, a feature extraction module named multi-scale feature fusion (MSFF) block is constructed, which uses depth-wise convolution of different kernel sizes to extract features and mixes them to enrich learning contents. In addition, we propose a multi-scale spatial attention module based on the Efficient Channel Attention (ECA) module. In this module, multi-scale information is added to make the extracted features more fine-grained. The proposed method was evaluated on the VOC2007 dataset and compared with the previous methods. The mAP of the model is better than that of the YOLOv7, YOLOx, etc. And the Parameters and FLOPs are also improved.