bluuebunny/crossref_metadata_2025_split huggingface dataset:
Support vector regression based on improved Harris Hawk optimization algorithm for power load forecasting
Authors: Liu Zilang , Chen Hongwei 
Published: August 2023

Abstract
Electricity load forecasting is an important part of power system operation, planning and management. Support vector regression (SVR) is a commonly used and efficient method for medium and long-term load forecasting. The hyperparameters of SVR have a very high influence on its performance and are not easily determined. In this paper, an improved Harris Hawk optimization algorithm (TAHHO) is proposed for optimizing the hyperparameters of SVR, and a TAHHO-SVR medium- and long-term prediction model is developed. TAHHHO enhances the convergence and stability of the original algorithm by introducing the survival of the fittest principle and the crossover operator of the artificial tree (AT) algorithm, which is validated in 13 benchmark test functions. The proposed TAHHO-SVR model is used for forecasting the UK National Grid dataset and demonstrates the feasibility and competitiveness of the model, which effectively improves the forecasting accuracy compared to HHO-SVR.
Image Recognition of Peach Pests Based on Improved Convnext
Authors: Lei Cheng Min , Shi Ai Jv , Cui En Quan , Wei Lv Hong , Min Mu Shao 
Published: August 2023

Abstract
A ConvNeXt-based image recognition model for peach tree pests is proposed to address the problem of low accuracy of pest image recognition due to the large intra-class morphological variation of peach tree pests in natural scenes. First, ConvNeXt is used as the backbone network and the model structure is optimized by introducing the visual perceptual field module to extract discriminative features of peach tree pests. Secondly, a joint loss function is used to make the model continuously learn the features that minimize the intra-class distance to accelerate the model convergence and improve the recognition accuracy. Finally, two tandem fully connected layers are used to replace the original fully connected layer, and the feature vectors input to the fully connected layer are visualized to show the pest classification effect. The experimental results show that compared with 10 networks such as RegNet, the model in this paper effectively improves the recognition accuracy of peach tree pest images, and the accuracy is improved by 4.8% compared with the standard ConvNeXt, which verifies the effectiveness of the model in this paper for accurate recognition of peach tree pest images in natural scenes.
Improved MobileViT: A More Efficient Light-weight Convolution and Vision Transformer Hybrid Model
Authors: Liu Jing , Chen Huiyang , Zhou Weimin 
Published: August 2023

Abstract
Convolutional neural networks (CNNs) utilize spatial induction bias to learn local features of images, while vision transformers (ViTs) can learn global representations of visual information through self-attention. However, due to ViT’s large number of parameters, it is challenging to apply them to mobile vision tasks. Based on Conv-ViT light-weight hybrid model MobileViT, this paper proposed the improved backbone network and transformer block to improve the applicability. Firstly, the MV2 block in MobileViT is substituted with an improved depth separable convolution and linear activation function. Secondly, we proposed a novel self-attention to replace the original self-attention of the transformer block in MobileViT, which reduces computational load and enhances global representation by enabling information interaction between different windows. Experimental results demonstrate that the improved model’s accuracy in image classification is 2.2% higher than that of MobileViT on the CIFAR-100 dataset.
Natural scene text detection algorithm based on the regional proposal
Authors: Wu Guangyue , Zeng Qingtao , Zhao Jiefeng , Yang Zibo 
Published: August 2023

Abstract
Text detection in natural scenes is widely used in various fields. The combination of deep learning models and text detection improves the accuracy and efficiency of detection. Its algorithm is also endless, with continuous optimization and improvement. On this basis, some algorithms for text detection in natural scenes are summarized from the two aspects of the Faster R-CNN method and SSD method based on regional suggestion. These algorithms and experimental data are analyzed and compared to further summarize the challenges faced by text detection in natural scenes and the direction of improvement.
Crankshaft defect detection based on machine vision
Authors: Guo Kunxiang , Sun Yuan 
Published: August 2023

Abstract
As a core component of the Marine engine, the crankshaft crank is required to have a high surface quality to ensure that the ship does not fail during the voyage. After analysing the defect characteristics, aiming at the problems such as the inaccurate detection of single defect feature in the crankshaft bend defect, which leads to poor detection effect, a crankshaft bend defect detection classification method based on machine vision was proposed. The recognition accuracy can reach 97.8%. Compared with the defect identification by texture and geometric features alone, the classification and recognition accuracy can be improved, and the requirements of industrial detection can be better met.
Numerical Detection of Concrete Slump by Fusion of Target Segmentation and Image Classification Network
Authors: Gao Xiao , Yan Hairong 
Published: August 2023

Abstract
In construction, concrete compatibility is an important comprehensive index to ensure the construction, and the concrete slump is an important criterion to judge concrete compatibility in the actual construction process. In this study, we propose to extract new data sets from concrete mixing video sequences and correlate the image features characterized in the concrete mixing and transportation process with the concrete performance features, starting from the concrete transportation process [1]. First, the UNet network model for semantic segmentation is used to identify and locate the concrete regions, and the localized concrete regions are zoomed in using interpolation; then the ResNet image classification network is used to determine the slump category of the processed concrete regions; finally, the results of the semantic segmentation network and the image classification network are fused to obtain the final concrete slump detection results. The experimental results demonstrate that the proposed method can guarantee real-time concrete slump detection with improved accuracy.
SSFN: a novel Spatial-Spectral FusionNet for hyperspectral image classification
Authors: Yan Chenyang , Jia Hong 
Published: August 2023

Abstract
Hyperspectral image classification is the process of identifying ground objects within hyperspectral images at the pixel level. While many CNN based methods have been successfully used in this field, they often struggle to effectively extract inter-spectral information due to the high spectral dimensions of hyperspectral images. Recently, the transformer model has been introduced to capture the interdependence between spectral data, but it may lose the ability to capture local context features. In order to address the limitations of CNN and Transformer models, we propose a novel approach that integrates the advantages of both models. Specifically, we use CNN to extract spatial information and Transformer to extract spectral information, which is then fused before being fed into the MLP framework for classification. Additionally, we introduce a sparse strategy to eliminate the impact of redundant frequency bands on the transformer’s performance. Our method fully utilizes the spatial and spectral information in hyperspectral image data and has achieved excellent performance on hyperspectral datasets. By leveraging both CNN and transformer models, we can effectively capture both spatial and spectral features, providing a powerful tool for hyperspectral image classification.
Multi-feature Fusion Based on RV Correlation Coefficient for Facial Expression Recognition
Authors: Wang Yan , Lu Yuming , Wan Xing 
Published: August 2023

Abstract
Extracting effective features of expressions becomes a hot research topic, and a single feature pattern cannot reflect the diversity of expressions. Therefore, to obtain rich information feature data and raise the expression recognition performance, we propose a feature fusion model of multiple feature selection by the measure of the RV correlation coefficient. In the proposed feature fusion model, the feature patterns are firstly selected by RV correlation coefficient from various expression texture features. And then according to rank the values of the RV correlation coefficient, we build a CCA subspace and PCA subspace respectively to fuse selected features. Finally, a new facial expression feature presentation is constructed through weighting and combining the two fusion features from the subspaces. The new features are fed to SVM classifier for expression recognition. Experimental verification shows that our proposed model has a superior performance than the existing algorithms.
Design of an Intelligent High-Temperature Infrared Temperature Measurement System
Authors: Jiang Han , Chen Cai , Wu Conglin , Ren Zhi , Yang Jintang 
Published: August 2023

Abstract
The system is designed to address the problem of untimely transmission of temperature measurement signals in the sampling process at the LF refinery location, as well as the subjective involvement of the operator in the traditional temperature measurement method, i.e. the technology and experience of the operation, which results in a certain systematic error in the temperature measurement results. In this paper, a steel plant is used as an experimental object to test the performance of the device and the practicality of the system in combination with the on-site operating environment; and the system differs from the traditional contact temperature measurement by visualizing the steel image and generating an intelligent temperature profile while measuring the temperature with the help of an infrared camera. The experimental results show that the system can be remotely controlled to achieve intelligent temperature measurement, and the percentage of temperature measurement error is controlled within 1.5%, which is of practical significance to the automation and intelligence of the LF refining furnace temperature measurement in the iron and steel metallurgical industry.
Target detection based on multi-scale feature fusion and cross-channel interactive attention mechanism
Authors: Zhao Chenyang , Song Yong , Yang Xin , Zhou Ya , Yang Jinqi 
Published: August 2023

Abstract
Aiming at the problems of complex background, target scale change and small target in aerial image detection, we propose a YOLOv5 target detection algorithm based on multi-scale feature fusion and cross-channel interactive attention mechanism. Including: M-PPM (Multi-scale pyramid pooling module) is designed as a replacement for the SPP (Spatial Pyramid Pooling) structure in YOLOv5, so as to make full use of different scale features to fuse global feature information; CCA (Cross-channel interactive attention mechanism) is designed to realize cross-channel information interaction and utilization, and enhance the network’s capability to generalize and fusion efficiency of small target features. Bi-directional Feature Pyramid Network (BiFPN) is utilized to solve scale difference problem in multi-target detection. The proposed algorithm’s experimental results is 2.3 % and 1.8 % higher than YOLOv5 on the VisDrone and UAVDT aerial data sets, respectively.
Fabric Defect Detection Using a One-class Classification Based on Depthwise Separable Convolution Autoencoder
Authors: Chen Chun , Deng Xiaoyan , Yu Zhuliang , Wu Zhengtao 
Published: August 2023

Abstract
Fabric defect detection is anomaly detection, which is widely studied in the textile industry. Like most anomaly detection tasks, there are some problems hindering detection results, such as class imbalance, defective sample scarcity, and feature selection. This paper proposes a method applying depthwise separable convolution autoencoder on dimensionality reduction and one-class classifier support vector data description (SVDD) to detect fabric defects. A depthwise separable convolution autoencoder can effectively extract sample features with less computation and fewer parameters than the regular convolution, which will be easily used in industrial production. SVDD can only use non-defective samples to train the classifier and solve the difficulty and heavy cost of collecting negative samples (defective samples). In this paper, we will demonstrate the effectiveness of the method on polyester fibers by using accuracy and AUC as evaluation criteria.
Research on Eye Movement Classification Based on the Deep Learning Model
Authors: Xie Shuangqiang , Guo Chunyong , Qiao Xiang 
Published: August 2023

Abstract
To improve the recognition accuracy of eye movement direction based on EOGM, the Emotiv Epoc, and EEG acquisition headset was used to collect the EEG signals of the subjects in real time. After the 14-channel signals were filtered by wavelet, the short-time energy calculation method was used to select the two channels most sensitive to eye movement. The long and short-term memory network (LSTM) was then used to classify the eye movement signals. On the same data set, the experimental results show that the classification rates in the upper, lower, left, and right directions are up to 91%, 90%, 83%, and 75%, respectively, and the average classification rate is up to 84%. Compared with the existing classification methods, the classification accuracy of the proposed method is higher and the implementation process of the classification algorithm is simpler, which further verifies the feasibility and effectiveness of using EEG to identify the direction of eye movement.
Parallel transient response topology optimization based on PETSc
Authors: Wang Sen , Lou Nengchuang , Kang Zhan 
Published: August 2023

Abstract
Improving the dynamic properties and reducing the vibration levels is extremely important for the design of many engineering structures. In this paper, we develop a dynamic topology optimization program based on parallel computing with the help of the PETSc Portable Extension Toolkit for Scientific Computing and the MPI communication protocol. The SIMP model is used as the topology optimization model, and the sensitivity analysis schemes of the dynamic compliance and displacement response as opposed to the density design variables are given in the paper. Using this optimization formulation, we developed a parallel topology optimization program for minimizing the transient response. The convergence performance of the program is shown with a relatively large-scale numerical example.
A Rotation-Invariance Face Detector Based on RetinaNet
Authors: Xiong Yuyang , Meng Wei , Yan Junwei , Yang Jun 
Published: August 2023

Abstract
The use of deep convolutional neural networks has greatly improved the performance of general face detection. For detecting rotated faces, the mainstream approach is to use multi-stage detectors to gradually adjust the rotated face to a vertical orientation for detection, which increases the complexity of training as multiple networks are involved. In this study, we propose a new method for rotation-invariant face detection, which abandons the previously used cascaded architecture with multiple stages and instead uses a single-stage detector to achieve end-to-end detection of face classification, face box regression, and facial landmark regression. Extensive experiments on FDDB in multiple orientations have shown the effectiveness of our method. The results demonstrate that our method achieves good detection performance and the detection accuracy of our method even exceeds that of other rotated face detectors on the front-facing FDDB dataset.
3D Human Pose Estimation Based on Transformer
Authors: Yin He , Lv Chang , Shao Yeqin 
Published: August 2023

Abstract
Currently, 3D human pose estimation has gradually been a well-liked subject. Although various models based on the deep neural network have produced an excellent performance, they still suffer from the ignorance of multiple feasible pose solutions and the problem of the relatively-fixed input length. To solve these issues, a coordinate transformer encoder based on a 2D pose is constructed to generate multiple feasible pose solutions, and multi-to-one pose mapping is employed to generate a reliable pose. A temporal transformer encoder is used to exploit the temporal dependencies of consecutive pose sequences, which avoids the issue of relatively-fixed input length caused by temporal dilated convolution. Adequate experiments indicate that our model achieves a promising performance.
Inversion studies of CO2 emission sources in industrial parks
Authors: Chen Yuan , Wang Yingxiang , Zhou Sixuan , Lei He , Li Siwu , Deng Guangyu 
Published: August 2023

Abstract
Industrial parks are a significant contributor to global CO2 emissions, making low-carbon development essential in mitigating climate change. In this study, we focus on the Hubei province industrial park as a case study and investigate the inversion of critical information on CO2 emission sources. Firstly, we establish a forward model of CO2 diffusion within the industrial park by employing atmospheric monitoring data and the AERMOD model system. Subsequently, we use a single back propagation (BP) neural network with the particle swarm optimization (PSO), whale optimization algorithm (WOA), and pelican optimization algorithm (POA) for inverse calculation of CO2 emission source locations and emission intensity. Our results show that the POA-BP inversion model has high inversion accuracy and stability, enabling us to locate the CO2 emission sources within the industrial park. This information can provide crucial decision support for the industrial park to tackle climate change and promote green transformation.
Prediction Model of Horizontal Drilling Trajectory of Anti-collision Drilling Robot
Authors: Kang Mingxia , Wu Honglin , Hua Dezheng 
Published: August 2023

Abstract
The horizontal drilling trajectory of the anti-collision drilling robot is taken as the research object and its drilling trajectory prediction model is established in this paper. The drilling trajectory is affected by many factors, such as the structural parameters of the auger, the drill bit, the drilling process parameters, and the geological characteristics of the coal seam, etc., which results in the existence of multi-level time-scale structure and localization characteristics of the drilling trajectory data in the time domain. The wavelet analysis method can decompose the relatively complex non-stationary time series problem into low-frequency decomposition signals representing trend items and high-frequency decomposition signals representing periodicity and randomness. It can not only predict the main trend of drilling trajectory changes but also effectively predict sudden load changes and short-term changes. Based on this, a new prediction method is proposed in this paper. Firstly, the data is decomposed into relatively simple component signals using wavelet decomposition technology, then the prediction models are established according to the characteristics of each component signal. Finally, the drilling trajectory prediction model is established by synthesizing the prediction results. The anti-impact drilling robot drilling test bed is set up and the drilling experiments are carried out to verify the effectiveness of the model, which can lay the foundation for the anti-impact drilling robot drilling deflection control.
A hybrid multi-objective optimization method for nuclear essential service water system design
Authors: Zhou Kangning , Cheng Wei , Liu Yilong , Chen Xuefeng , RongyongZhang  , Mao Yujia 
Published: August 2023

Abstract
Evolutionary algorithms have proven to be very successful in solving multi-objective optimization problems (MOPs). However, their performance often deteriorates when constraints are introduced. This paper proposes a hybrid heuristic intelligence (HHI) approach to remedy this issue. First, a classification operator is designed to sort individuals by their relative constraint values, which enlarges the size of feasible solutions in the early process. Then, by combining constraints with the non-dominated sorting method, we define a new selection operator to increase the probability of selecting individuals who have low constraint values but high objective values. Finally, we use hybrid optimized operations to simultaneously enhance the convergence and diversity performance by performing different operators in a certain domain. The proposed method achieves state-of-the-art performance on test functions ZDT1, Binh2, and OSY, with a remarkable decrease of 24% in the inverted generational distance (IGD) on OSY. For further applications, our method is also tested with an engineering model established for the design of the nuclear essential service water system (SEC). The final result shows that without taking hundreds of hours, the final solution of HHI has met all constraints and reduced the total cost by 8.9%© YEAR The Authors. Published by Elsevier Ltd.
Co-occurrence of climate-change induced and anthropogenic pressures in Central American key biodiversity areas
Authors: Baumbach Lukas , Hickler Thomas , Yousefpour Rasoul , Hanewinkel Marc 
Published: August 2023

Abstract
Central America hosts many key biodiversity areas (KBAs), areas which represent unique and irreplaceable ecosystems of global importance for species conservation. However, large extents of these areas are not under legal protection and could be threatened by pressures from land use change (e.g. deforestation and agricultural expansion), high human population density (e.g. population growth and urban sprawl) and climate-driven biome shifts. Here, we simulated future biome stability under the influence of climate change across KBAs in the Mesoamerican biodiversity hot spot and combined the results with projections of land use and population density up to the end of the 21st century. We applied four forcing scenarios based on two global climate models (GFDL-ESM4 and IPSL-CM6A-LR) and two shared socio-economic pathways (SSP1-2.6 and SSP3-7.0), which represent a range from low to high emission pathways. Our model projected decreased biome stability in 39%–46% of protected areas in KBAs, whereas this number even increased to 59%–60% for unprotected areas in KBAs (depending on the climate scenario). While human interferences in protected parts of KBAs are expected to be limited, large parts of unprotected areas in KBAs were projected to be pressured by multiple factors at once and are reason for concern. In particular, high human population pressures (>10 people km−2) emerged as a main threat over 30%–44% of the unprotected area in KBAs. These were largely accompanied by pressures from land use and sporadically reinforced by pressures from climate-driven biome shifts. Among the hot spots facing multiple high pressures are some of the last tropical dry and montane forest ecosystems in Central America, which stresses the need for urgent conservation action.