Residual Vibration Suppression of Piezoelectric Inkjet Printing Based on Particle Swarm Optimization Algorithm.

Piezoelectric inkjet printing technology, known for its high precision and cost-effectiveness, has found extensive applications in various fields. However, the issue of residual vibration significantly limits its printing quality and efficiency. This paper presents a method for suppressing residual vibration based on the particle swarm optimization (PSO) algorithm.

An Agent-Based Method for Feature Recognition and Path Optimization of Computer Numerical Control Machining Trajectories.

In recent years, artificial intelligence technology has seen increasingly widespread application in the field of intelligent manufacturing, particularly with deep learning offering novel methods for recognizing geometric shapes with specific features. In traditional CNC machining, computer-aided manufacturing (CAM) typically generates G-code for specific machine tools based on existing models. However, the tool paths for most CNC machines consist of a series of collinear motion commands (G01), which often result in discontinuities in the curvature of adjacent tool paths, leading to machining defects.

Determination of the rat estrous cycle vased on EfficientNet.

In the field of biomedical research, rats are widely used as experimental animals due to their short gestation period and strong reproductive ability. Accurate monitoring of the estrous cycle is crucial for the success of experiments. Traditional methods are time-consuming and rely on the subjective judgment of professionals, which limits the efficiency and accuracy of experiments.

A Multi-Task Joint Learning Model Based on Transformer and Customized Gate Control for Predicting Remaining Useful Life and Health Status of Tools.

Previous studies have primarily focused on predicting the remaining useful life (RUL) of tools as an independent process. However, the RUL of a tool is closely related to its wear stage. In light of this, a multi-task joint learning model based on a transformer encoder and customized gate control (TECGC) is proposed for simultaneous prediction of tool RUL and tool wear stages.

Correction: Gao et al. A Modeling Method for Thermal Error Prediction of CNC Machine Equipment Based on Sparrow Search Algorithm and Long Short-Term Memory Neural Network. 2023, , 3600.

In the original publication [...

A novel method for detecting credit card fraud problems.

Credit card fraud is a significant problem that costs billions of dollars annually. Detecting fraudulent transactions is challenging due to the imbalance in class distribution, where the majority of transactions are legitimate. While pre-processing techniques such as oversampling of minority classes are commonly used to address this issue, they often generate unrealistic or overgeneralized samples.

Edge Detection via Fusion Difference Convolution.

Edge detection is a crucial step in many computer vision tasks, and in recent years, models based on deep convolutional neural networks (CNNs) have achieved human-level performance in edge detection. However, we have observed that CNN-based methods rely on pre-trained backbone networks and generate edge images with unwanted background details. We propose four new fusion difference convolution (FDC) structures that integrate traditional gradient operators into modern CNNs.

Application of hybrid improved temporal convolution network model in time series prediction of river water quality.

Time series prediction of river water quality is an important method to grasp the changes of river water quality and protect the river water environment. However, due to the time series data of river water quality have strong periodicity, seasonality and nonlinearity, which seriously affects the accuracy of river water quality prediction. In this paper, a new hybrid deep neural network model is proposed for river water quality prediction, which is integrated with Savitaky-Golay (SG) filter, STL time series decomposition method, Self-attention mechanism, and Temporal Convolutional Network (TCN).

MAB-DrNet: Bearing Fault Diagnosis Method Based on an Improved Dilated Convolutional Neural Network.

Rolling bearing fault diagnosis is of great significance to the safe and reliable operation of manufacturing equipment. In the actual complex environment, the collected bearing signals usually contain a large amount of noises from the resonances of the environment and other components, resulting in the nonlinear characteristics of the collected data. Existing deep-learning-based solutions for bearing fault diagnosis perform poorly in classification performance under noises.

Mining belt foreign body detection method based on YOLOv4_GECA model.

In the process of mining belt transportation, various foreign objects may appear, which will have a great impact on the crusher and belt, thus affecting production progress and causing serious safety accidents. Therefore, it is important to detect foreign objects in the early stages of intrusion in mining belt conveyor systems. To solve this problem, the YOLOv4_GECA method is proposed in this paper.

A Modeling Method for Thermal Error Prediction of CNC Machine Equipment Based on Sparrow Search Algorithm and Long Short-Term Memory Neural Network.

To better solve the problem of thermal error of computerized numerical control machining equipment (CNCME), a thermal error prediction model based on the sparrow search algorithm and long short-term memory neural network (SSA-LSTMNN) is proposed. Firstly, the Fuzzy C-means clustering algorithm (FCMCA) is used to screen the key temperature-sensitive points of the CNCME. Secondly, by taking the temperature rise data of key temperature-sensitive points as input and the corresponding time thermal error data as output, we established the SSA-LSTMNN thermal error prediction model.

Loop Closure Detection Based on Residual Network and Capsule Network for Mobile Robot.

Loop closure detection based on a residual network (ResNet) and a capsule network (CapsNet) is proposed to address the problems of low accuracy and poor robustness for mobile robot simultaneous localization and mapping (SLAM) in complex scenes. First, the residual network of a feature coding strategy is introduced to extract the shallow geometric features and deep semantic features of images, reduce the amount of image noise information, accelerate the convergence speed of the model, and solve the problems of gradient disappearance and network degradation of deep neural networks. Then, the dynamic routing mechanism of the capsule network is optimized through the entropy peak density, and a vector is used to represent the spatial position relationship between features, which can improve the ability of image feature extraction and expression to optimize the overall performance of networks.

TAFM: A Recommendation Algorithm Based on Text-Attention Factorization Mechanism.

The click-through rate (CTR) prediction task is used to estimate the probabilities of users clicking on recommended items, which are extremely important in recommender systems. Recently, the deep factorization machine (DeepFM) algorithm was proposed. The DeepFM algorithm incorporates a factorization machine (FM) to learn not only low-order features but also the interactions of higher-order features.

Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories.

Over recent years, traditional manufacturing factories have been accelerating their transformation and upgrade toward smart factories, which are an important concept within Industry 4.0. As a key communication technology in the industrial internet architecture, time-sensitive networks (TSNs) can break through communication barriers between subsystems within smart factories and form a common network for various network flows.

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing.

With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority.

Joint Soft-Hard Attention for Self-Supervised Monocular Depth Estimation.

In recent years, self-supervised monocular depth estimation has gained popularity among researchers because it uses only a single camera at a much lower cost than the direct use of laser sensors to acquire depth. Although monocular self-supervised methods can obtain dense depths, the estimation accuracy needs to be further improved for better applications in scenarios such as autonomous driving and robot perception. In this paper, we innovatively combine soft attention and hard attention with two new ideas to improve self-supervised monocular depth estimation: (1) a soft attention module and (2) a hard attention strategy.

[Application research on carotid arterial shunting during simultaneous coronary artery bypass grafting and carotid endarterectomy].

The optimal operative strategy in patients with asymptomatic severe carotid artery stenosis undergoing coronary artery bypass grafting (CABG) is unknown. We sought to investigate the safety of carotid arterial shunting during simultaneous CABG and carotid endarterectomy (CEA). The clinical data of patients undergoing synchronous combined CEA and CABG in the First Hospital of China Medical University between March 2017 and July 2019 was retrospectively studied.