Reviewerly/exHarmony huggingface dataset:
CARDNet: A Denoiser Based on Contrast-Aware and Residual-Dense Block

In recent years, deep convolutional neural networks have shown good performance on images with spatially invariant noise, but their performance is limited on real-world noisy images. In order to improve the practicality of denoising algorithms, this paper proposes a simple one-stage blind real image denoising network with a modular structure, which combines the local modeling capability of residual dense convolutional layers with the global modeling capability of spatial and channel attention blocks and inserts them as the main building blocks into widely used encoder–decoder architectures to achieve end-to-end denoising. To preserve image structure information as much as possible during the denoising process, this paper uses different lengths of residuals between different modules to mitigate the flow of low-frequency information, and applies Contrast-Aware channel attention to enhance the dependency relationship of channel activation. Furthermore, this paper evaluates state-of-the-art algorithms on different noise datasets using quantitative metrics and visual quality, experimental results demonstrate the superiority of the proposed algorithm.
LSTM-TD3-Based Control for Delayed Drone Combat Strategies

Reinforcement learning has made great achievements in the field of game confrontation, and military rendition intelligence is also imminent. In this paper, we propose a game confrontation game model based on LSTM and deep reinforcement learning for the red and blue sides of the drones in the delayed environment. An action buffer pair is added to the original battlefield environment to create a large latency environment, a TD3 algorithm is designed to join LSTM to predict the state trend, and a historical data pool is built using PER. The training results in the simulation platform show that the model has a significant improvement in effectiveness in the large latency environment, with a 0.22 improvement in battle damage ratio and a 34.76% improvement in win rate over the original model.
A Remote Mobile Image Acquisition System and Experimental Simulation of Indoor Scenes Based on an RGB-D Camera

In order to solve the problems of camera shake affecting the acquisition effect, inability to accurately judge the camera movement and rotation speed, and great physical and mental exertion arising from handheld RGB-D cameras during 3D data image acquisition, this paper designs an indoor remote movable image acquisition system based on RGB-D cameras. First, a motion control module is built based on the ROS system and a microcontroller, then the RGB-D camera is driven through Jetson Nano for image acquisition and the optimized AKAZE algorithm is used to perform feature processing on the images. Simulation results have shown that this platform has high accuracy in image acquisition and feature processing in complex and wide indoor scenes.
Highway Abandoned Object Detection Based on Foreground Extraction

With the development of highways, abandoned objects pose significant threats to driving safety. This paper proposes an abandoned object detection method for highways based on YOLOv7 and Background-Separated Gaussian Mixture Model(BS-GMM). Firstly, BS-GMM is employed to extract the foreground from the video and obtain the binary image of the abandoned objects. Finally, the Intersection over Union(IoU) matching algorithm is applied to differentiate abandoned objects from vehicles. Through validation with highway videos, experimental results demonstrated that the pixel-level error rate of BS-GMM algorithm was 2.01 $$\%$$ , which outperformed the Gaussian Mixture Model (GMM) algorithm’s 3.67 $$\%$$ . Moreover, the stationary object’s duration of BS-GMM algorithm was 500 frames, which was higher than the traditional GMM algorithm’s 200 frames.
Nighttime Vehicle Object Detection Based on Improved YOLOv7

Detecting vehicles using deep learning is a crucial area of research in computer vision. Detecting vehicles at night can be challenging due to factors such as low illumination, complex lighting, and other environmental conditions, which can negatively impact the accuracy of detection. As a result, achieving high detection accuracy for night vehicle images can be difficult. Therefore, this paper combines image enhancement algorithm and object detection algorithm to study vehicle object detection in night scene. Firstly, Laplacian sharpening algorithm is applied to enhance the images. Then, the improved YOLOv7 algorithm is applied to detect the enhanced images. Compared with the original YOLOv7 on the self-made night vehicle dataset, improved YOLOv7 only has 1.6% higher parameters, but has 2.5% less computation, and brings 1.9% higher mAP0.5 and 1.7% higher mAP0.5:0.95.
DETR with Recursive Gated Convolution Encoder

This paper proposes a novel approach for improving token interaction modeling in object detection by replacing the self-attention module in the DETR encoder with the Recursive Gated Convolution ( $$g^n$$ Conv) module. Our method utilizes a fixed order setting for $$g^n$$ Conv and shares weights between encoder layers to seamlessly integrate it into the transformer encoder. By replacing the conventional attention encoder, our method can be easily integrated into any single-scale DETR-like detector, resulting in higher accuracy with comparable parameters and computational complexity. At the COCO datasets challenge, the proposed GConv encoder achieves a significant improvement (+0.9AP) on DAB-DETR, demonstrating its effectiveness in any single-scale DETR architecture.
Key-Agent Based Dynamic Prioritized Planning for Multi-agent Systems

Multi-agent path finding (MAPF) is the basis of multi-agent systems. The current priority-based MAPF algorithm has a high performance in solution efficiency, but is sensitive to priority order. In order to reduce priority sensitivity, this paper proposes a key agents based priority adjustment method. Firstly, the key agents affecting the problem solution is extracts level by level to construct a multi-level structure. Then, the priority order is adjusted according to the key agents and the level they are in. Finally, a key agents based dynamic priority safe interval path planning (KD-SIPP) algorithm is constructed. Simulation results show that the proposed algorithm has a significant improvement in success rate compared with existing algorithms, and is able to solve problems containing hundreds of agents. Moreover, there is only a 6.7% increase in path costs compared to the optimal solution.
A Visual-LiDAR Object Tracking Method Using Correlation Filter and Potential Matching

Visual based single-object tracking is a classical task in computer vision, which uses texture and color to distinguish objects and backgrounds. However, visual based methods are sensitive to illumination change and motion blur. So, we propose a coarse-to-fine visual-lidar fusion algorithm. This algorithm integrates a visual subsystem and a lidar subsystem. We use the visual subsystem to calculate a coarse position of the object, and then we use the lidar subsystem to optimize it to obtain an accurate position. Compared to visual tracking algorithms, our algorithm is more accurate and robust.
Heuristic-Based Bi-RRT* Path Planning Algorithm for Unmanned Systems in Complex Channel Environment

In order to solve the problems low success rate and slow search rate of Bi-RRT* algorithm in multi-obstacle environment with complex channel, a local guided-based heuristic bidirectional RRT*( LGHB-RRT*) was proposed, which uses hybrid search strategy to carry out heuristic planning for different characteristic regions of the map. The algorithm uses the channels and recognition points information obtained after environmental map preprocessing to conduct heuristic growth. And non-uniform sampling search is carried out in the channel region to make the random tree pass through the channel region. The path optimization is added to remove unnecessary path points with hope of improving the optimality. The proposed algorithm is simulated and compared with the comparison algorithm. The results show that the proposed algorithm has higher success rate and search speed, which has strong adaptability in different types of complex channel obstacle environments.
UAV Path Planning Based on Improved Artificial Potential Field Method

This paper proposes an improved artificial potential field method (APF) and applies it to UAV path planning, aiming to solve the local optimal solution, unreachable target, non-smooth path and excessive gravity often encountered in traditional artificial potential field methods. In order to solve the limitations of the traditional artificial potential field method, this paper solves the local minimum problem by modifying the repulsion potential field function; the smooth force and inertial force are introduced to balance the force field, and the smooth force is used to reduce the jitter phenomenon of the UAV movement, Inertial force adjusts the movement direction of the UAV to prevent it from falling into a local optimal state; solve the problem of excessive gravity by setting the gravity compensation coefficient and the maximum influence range of gravity; the NSGA-II multi-objective optimization algorithm is used to optimize the initialization parameters of the algorithm, so that a better combination of algorithm initialization parameters can be obtained, thereby obtaining better UAV path planning results. The experimental simulation results show that the improved artificial potential field method in this paper can quickly and effectively plan a shorter and smoother path, avoiding a series of problems existing in the traditional artificial potential field method, and applied to UAV path planning in complex environments It has certain practical value.
Logic for reasoning about bugs in loops over data sequences (IFIL)

Classic deductive verification is not focused on reasoning about program incorrectness. Reasoning about program incorrectness using formal methods is an important problem nowadays. Special logics such as Incorrectness Logic, Adversarial Logic, Local Completeness Logic, Exact Separation Logic and Outcome Logic have recently been proposed to address it. However, these logics have two disadvantages. One is that they are based on under-approximation approaches, while classic deductive verification is based on the over-approximation approach. One the other hand, the use of the classic approach requires defining loop invariants in a general case. The second disadvantage is that the use of generalized inference rules from these logics results in having to prove too complex formulas in simple cases. Our contribution is a new logic for solving these problems in the case of loops over data sequences. These loops are referred to as finite iterations. We call the proposed logic the Incorrectness Finite Iteration Logic (IFIL). We avoid defining invariants of finite iterations using a symbolic replacement of these loops with recursive functions. Our logic is based on special inference rules for finite iterations. These rules allow generating formulas with recursive functions corresponding to finite iterations. The validity of these formulas may indicate the presence of bugs in the finite iterations. This logic has been implemented in a new version of the C-lightVer system for deductive verification of C programs.
Quality Evaluation of Airfoil Hybrid Mesh Based on Graph Neural Network

In airfoil numerical simulation, the mesh quality has an important influence on the accuracy and error of numerical simulation. The existing mesh quality evaluation requires a lot of manual interaction, which greatly reduces the efficiency of mesh generation and necessitates the implementation of intelligent mesh evaluation methods. Graph neural networks can extract features from graph data, possess self-adaptability and generalization ability, and have been successfully applied in many industries. In this paper, we propose a deep graph neural network, SDeepNet, to evaluate mesh quality and construct a large-scale mixed mesh dataset, MixSet, for training and validating the model. We test and compare the performance of the mesh quality evaluation models GridNet, GMeshNet, and SDeepNet on the mesh dataset MixSet. The experimental results show that the SDeepNet model can achieve high accuracy and recall in the mixed mesh quality evaluation task.
Research on Adversarial Robustness Properties of Image Classification Networks Based on Deep Vision

In response to the problem of significant performance decline of existing deep learning-based intelligent recognition algorithms under adversarial sample attack conditions, this research investigates the intrinsic mechanisms and description methods of adversarial samples. Quantitative linear characteristic analysis is conducted on sub-operations of convolutional neural networks, a model is established to compute the incremental output corresponding to perturbed inputs of sub-operations, and the internal mechanism of adversarial sample generation is explored. Using the fast gradient descent method, sensitivity coefficients and offset coefficients are introduced in RestNet networks to establish a relationship model between input perturbations and outputs. The linear characteristics in high-dimensional space are demonstrated to be the cause of adversarial sample generation. Finally, using the projection gradient descent method, a relationship model is established between the number of iterations and outputs to solve the mapping relationship between sensitivity coefficients and the number of iteration attacks. This provides guidance for the design of deep learning attack-defense algorithms.
Edge Detection of Carbon Electrode Image Based on Improved Image Restoration and Improved Canny Operator Fusion

Aiming at the problem that the low contrast of carbon electrode, the reflection phenomenon caused by strong light reflection of metal strip and too much background noise affects the edge detection of carbon electrode image in complex industrial environment, an edge detection algorithm of carbon electrode image based on improved image inpainting, and improved Canny operator fusion was proposed. Firstly, the improved color enhancement algorithm was used to enhance the contrast of the image. Then, the image inpainting algorithm was used to partially repair the metal strip of the carbon electrode, which improved the uneven brightness problem caused by strong light interference. At the same time, the guided filter was used to improve the problem of image edge blurring caused by Gaussian filtering in noise reduction. Secondly, the 3 $$\,\times \,$$ 3 gradient template Sobel operator was used to calculate the gradient amplitude and direction to improve the accuracy of edge location. Finally, the discontinuous edges were repaired by morphological closing operation to obtain the final detected edges. The experimental results show that compared with other algorithms, the proposed algorithm can better protect the edge details, and the index of the edge connection degree is greatly improved, which can effectively improve the edge detection effect of carbon electrode images based on complex environment.
MQB-RRT*:An Improved Path Planning Algorithm Based on Improving Initial Solution and Fast Convergence

In order to enhance the efficiency of the RRT*(rapidly exploring random trees*) algorithm in the search for initial paths and the generation of high-quality paths, an enhanced variant known as MQB-RRT*(Multipoint sampling and Back-end optimization based on RRT*) is proposed. Primarily, the incorporation of the depth parent node concept is employed to enhance path smoothness. Subsequently, a novel multi-point sampling method, inspired by the roulette wheel selection strategy, is introduced. Finally, a sophisticated back-end optimization technique based on triangle optimization is employed to refine the paths generated iteratively by the algorithm. The experimental results demonstrate the superiority of the MQB-RRT* algorithm in terms of both efficiency in searching for initial paths and the quality of generated paths.
Construction of Knowledge Graphs Related to Industrial Key Production Processes for Query and Visualization

In the era of “Industry 4.0”, there is an urgent need for industries to realize knowledge informatization and intelligence. Calcination is a very important and complex production process in many industrial production lines. Taking the cement calcination section as an example, this paper constructs a relevant knowledge graph and designs corresponding knowledge extraction methods for several different data sources. Finally, the knowledge graph consists of 1284 entities, 1547 relations, and 4115 attribute values; on this basis, query and Q &A algorithms are designed. A networked software platform that can provide knowledge retrieval, relationship query, full KG display, and other service functions for calcined section-related production equipment, production process, and production flow is developed. This method can promote the application of knowledge graph in the field of industrial key production processes.
Early Gastric Cancer Screening Framework Based on Multimodal Fusion

Gastric cancer is one of the leading causes of cancer deaths worldwide, and early detection of gastric cancer is crucial to improving overall survival. Early gastric cancer screening prevents poor prognosis of gastric cancer through early diagnosis and early treatment, but requires high-quality endoscopy. We hope to improve the performance of early gastric cancer screening by using multimodal data in artificial intelligence-based systems. Therefore, we designed a multimodal fusion-based early gastric cancer screening framework that leverages both modal data, gastroscopic images and corresponding text, through multimodal bilinear fusion to improve performance.
Fusing Multi-scale Binary Convolution with Joint Attention Face Expression Recognition Algorithm

Addressing the challenges of wild facial expression datasets being affected by illumination and pose variations, and the expression features being dispersed across multiple easily overlooked facial regions, this paper proposes a facial expression recognition network called Binary and Joint Attention Network. Firstly, The multi-scale binary convolution module integrates texture features of different granularities. Subsequently, the proposed Multi-head Joint Attention module focusing on multiple distinct facial areas through multiple attention heads. Lastly, the inter-attention map loss is designed to prevent attention overlap while assisting in classification. Experimental results on multiple datasets, including SFEW2.0, RAF-DB, FER2013, demonstrate that the proposed network can effectively recognize various facial expressions.
Path Planning of Mobile Robot Based on DBSCAN Clustering and Improved BA*-APF Hybrid Algorithm

To effectively improve the problems of the traditional A* algorithm in mobile robot path planning, such as insufficient accuracy, long search time, slow convergence and unsafe, a DBSCAN(Density-Based Spatial Clustering of Applications with Noise)-based clustering and improved BA*-APF(Bidirectional A*-Artificial Potential Field) hybrid algorithm is proposed. First, the DBSCAN algorithm is adopted to reduce the environmental complexity by clustering the obstacles. Second, a four-eight hybrid switching neighborhood search method is proposed to improve the search efficiency while ensuring the path security. Third, the bidirectional search method is introduced to further increase the convergence speed and considering the problem that the bidirectional search cannot converge as the map expands, the artificial potential field method is introduced to improve the heuristic function. Finally, a third-order Bessel curve is used to improve the smoothness of the generated optimal path. The proposed algorithm is proven to work effectively with significant improvement through simulations.
Design of a Multi-axis Motion Control System for Smart Hands Based on SOC

In order to address the issues of low flexibility and poor synchronization in multi axis motion control systems, as well as the integration and coordination of dexterous hands, a multi axis motion control system for dexterous hands based on System on Chip was designed and implemented. This system is composed of upper computer software and motion controller. The upper computer is responsible for setting parameters and receiving feedback information; The motion controller is responsible for the planning of the control algorithm, the driving of the motor and the real-time acquisition of the signal, and storing the alarm information for the user to analyze the abnormality. Finally, perform performance testing on the entire system. The results indicate that the control system has good stability, strong controllability, and high synchronization, which is helpful for the collaborative control of dexterous hands and is of great significance for the development of motion control systems.
Assessment Method for Fine Configuration of Basic Resource of Information System

Based on resource pooling technology and breaking down system into different configuration items, this paper establishes a refined resource allocation and evaluation mechanism. The mechanism can allocate and optimize different VCPU and memory resources for different configuration items mainly according to user concurrency, user behavior, authoritative computer hardware performance measurement system and early usage of configuration items. And It will bring a set of scientific, reasonable, economical and practical resource allocation management methods for enterprise information construction.
UCB-Based Architecture Search for Remaining Useful Life Prediction

Remaining useful life (RUL) is a significant challenge in prognostics and health management. With the development of data science and deep learning, neural network technology has been applied to this problem. In this study, we present a self-optimized mechanism for neural architecture search. Based on upper confidence bound (UCB) algorithm, we construct a reinforcement learning method for search strategy. UCB explores the combinatorial parameter space of a multi-head convolutional neural network with a recurrent neural network to find a suitable architecture. We conducted experiments on the C-MAPSS dataset, and our proposed model achieved better results in measured by RMSE and Scoring function. Compared with other data-based approaches, our method had better performance in terms of both precision and efficiency.
State Estimation and Fusion for Multisensor Systems with False Data Injection Attacks and Non-Gaussian Noises

In this paper, a state estimation and fusion problem is investigated for multisensor systems with false data injection attacks (FDIAs) during sensor measurement and data transmission and non-Gaussian noises. A maximum correntropy criterion unscented Kalman filter based on Cauchy kernel function (CK-MCC-UKF) is designed against FDIAs and non-Gaussian noises. Meanwhile, pseudo-random numbers are applied as data watermarking for secure transmission and attack detection. To further improve the accuracy of the state estimation, an order insensitive sequential fast covariance intersection (SFCI) algorithm is applied for the fusion of local estimates. Finally, numerical examples are given to verify the effectiveness of the algorithm proposed.