Reviewerly/exHarmony huggingface dataset:
Research on the Denoising Effect of Filters Based on Matlab

Digital filters have been widely used in the field of digital signal processing. This article focuses on the study of IIR filters designed using bilinear variation method and FIR filters designed using window function method based on Matlab simulation. For the same signal with random noise added, the signal is filtered through two types of filters to eliminate the added noise. The waveform and spectrum of the signal before and after filtering are compared and analyzed. The simulation experiment results show that both FIR filter and IIR filter can effectively overcome the shortcomings of traditional filters. Through intuitive comparison of waveform and spectrum, it is found that the elliptical filter designed based on bilinear transformation method can achieve better denoising effect than the FIR filter designed with Blackman window.
Interpretable Deep Knowledge Tracing with Graph Relationship Information

Knowledge tracing tasks involve analyzing students' historical question-answering sequences to assess their knowledge status, enabling the prediction of their future performance. Most existing knowledge tracing models only explore the direct impact of questions or knowledge concepts themselves on students' answers but do not effectively explore the deep relationships between questions and knowledge concepts, which weakens the predictive performance of the models. In addition, the nature of the deep learning black box leads to a lack of interpretability of the knowledge tracing model applying deep learning algorithms. Therefore, we introduce an interpretable deep knowledge tracing model with graph relationship information. It leverages graph attention neural networks to explore fine-grained relationships between questions and knowledge concepts in the original dataset, thus enhancing its predictive performance. Furthermore, DKVMN is employed to capture students' knowledge state for each knowledge concept, and IRT is incorporated to enhance interpretability. Our methodology beats popular knowledge tracing methods in terms of prediction performance, according to experimental results on three genuine public online education datasets, more accurately capturing students' knowledge states for better prediction of their future performance, and enhancing model interpretability.
Class-aware Learning for Imbalanced Multi-Label Classification

Imbalanced multi-label image classification has gained increasing attention recently, in which each sample has multiple class labels, but the number of each category is unevenly distributed. It’s common in practical applications but traditional multi-label learning methods can hardly deal with imbalance problems. In this paper, we propose an effective method to tackle imbalanced multi-label learning. The class-aware embedding network is proposed to learn robust class-based representation. Additionally, by using the distribution-balanced loss to weigh different samples, our model can improve the feature learning ability of minority classes. Extensive experiments on widely used long-tailed manual multi-label datasets like VOC-LT and COCO-LT explicitly validate the proposed good method.
Pedestrian Detection Algorithm Based on Improved YOLOv4

Pedestrian detection aims to automatically identify and locate pedestrian objects in images or videos, enabling applications such as intelligent surveillance, traffic safety, and crowd counting. To address the issue of low accuracy in pedestrian detection using the YOLOv4 object detection algorithm, an improved YOLOv4 algorithm is proposed to enhance pedestrian detection performance. By incorporating the DenseNet model and ECANet attention mechanism, experiments are conducted on the INRIA dataset. The improved YOLOv4 algorithm achieves an average precision (AP) of 93.97%, which is a 4.85% improvement compared to the original YOLOv4 algorithm.
Research on global path planning for driverless vehicles based on improved A* algorithm

The global path planning of driverless vehicles is one of the key technologies in the research of driverless vehicles. This article studies the disadvantages of the A* algorithm in the application of global path planning. The slow search and judgment of the final path node by the A* algorithm is due to the algorithm's exploration and judgment of all eight nodes around each exploration node, resulting in a large amount of unnecessary calculations. Therefore, it is proposed to combine the JPS algorithm with the A* algorithm, Improve the problem of slow searching for final path nodes and long running time in the A* algorithm. The article replaces the exploration node part of the A* algorithm with the skip search method of the JPS algorithm, which greatly reduces unnecessary calculations. Through Matlab simulation experiments, the running time before and after the algorithm improvement is compared. The results show that the A * algorithm combined with the skip search method of the JPS algorithm significantly reduces search nodes, reduces algorithm running time, and optimizes algorithm efficiency.
Time-sensitive-target Threat Assessment for UAV Ground Attack Based on Incremental ELM

To get a scientific threat assessment of Time-Sensitive-Target (TST) on the ground for speed and accuracy improvement of UAV ground attack decision, a threat assessment method for TST on the ground is proposed based on Incremental Extreme Learning Machine (IELM). On the basis of threat assessment indicator system for TST on the ground, the connection degree and set pair concepts in SPA theory are first introduced to construct the training samples and test samples, and then the obtained samples are used to train and test IELM online to get the threat assessment model of TST on the ground, completing the assessment and ordering of targets. Simulation shows that, the proposed method is fast and accurate in evaluating the TST threat on the ground, and can accurately sort the threat of targets.
Cross-Scale Object Detection for Large-Scale Images in Real-Time

The remote digital tower offers advantages over traditional physical towers, while accurately locating objects in panoramic images presents challenges due to large variations in object scale and potential image quality issues. This paper proposes a simple yet effective cross-scale object detection method for panoramic images in the context of remote tower systems for air traffic control. Our approach focuses on adaptive feature adjustments to enhance small object representation and explores higher computational efficiency in convolutional layers for improved network inference. Additionally, we utilize TensorRT for inference, optimizing the network structure and employing GPU hardware acceleration. Results show that the proposed method can significantly enhance cross-scale object detection performance, contributing to the advancement of remote tower systems.
Research on PUE Prediction Model of Data Center Based on Artificial Intelligence

With the development of artificial intelligence technology, the power consumption of data centers is increasing rapidly. How to reduce the energy consumption level of data centers and improve their green level is the current research hotspot. Since data center refrigeration system power consumption accounts for more than 40%, some scholars proposed to use artificial intelligence technology to model and analyze the refrigeration system to improve its operation efficiency and reduce the overall energy consumption of the data center. At present, Google uses neural network to build data center PUE Analysis Model and applied it in daily lives. The overall PUE of the data center is reduced by 0.5% to 2%. The basis of PUE optimization is PUE accurate prediction, so it is of great significance to study PUE prediction model. Given the large amount of operating data and the uneven level of data quality, it is difficult to model and analyze the refrigeration system in the data center. In this paper, deep neural networks are used to predict the PUE of data centers, and a PUE prediction model is constructed. Through field experimental analysis in data centers, the results show that the scheme proposed in this paper can accurately predict the PUE, which lays a foundation for further research on the influencing factors of PUE of refrigeration systems and then proposes the PUE optimization scheme.
Interpreter-Based Secure Centralized Interoperability Method for IoT Devices

In order to enable rapid integration of IoT devices with different communication protocols, this article proposes an interpreter-based centralized interoperability method. The method utilizes a unified interpreter deployed on a central server for scheduling and achieving interoperability among diverse devices. Specifically, the method employs a Lua interpreter embedded within the central server, where device interoperability logic is described using Lua scripts. This facilitates communication and data exchange between different devices. By leveraging interpreters, IoT devices can adhere to a standardized communication protocol and data format, thereby achieving cross-device interoperability and addressing a specific class of communication challenges in IoT devices.
Improved Algorithm for GAN Super-Resolution Face Image Reconstruction

Aimed at solving the unstable training of the existing GAN-based single-image super-resolution reconstruction model and the unsatisfactory visual effect of the reconstructed face image details, a face super-resolution reconstruction algorithm based on EM distance is proposed and named as SRWGAN. The main improvements include removing the BN layer in the model generator and increasing the network depth by adding residual blocks, introducing a shortcut connection optimization network in the discriminator, using EM distance instead of JS divergence as the adversarial loss of the network, and selecting RMSProp to replace the momentum-based gradient optimization algorithm. The experimental results show that, when the improved model algorithm is compared with the BiLinear, SRCNN and SRGAN methods for image reconstruction, the reconstructed face image is improved in terms of texture details and visual effects. Compared with the SRGAN model that has a better visual effect, the PSNR value is improved by an average of 1.84dB, and the SSIM value is enhanced by an average of 0.036, which proves the effectiveness of the proposed algorithm.
Research on style transfer model based on improved CycleGAN algorithm

Style transfer is a heated computer vision topic that has been broadly mentioned in both academic research and practical applications, for example, the style transfer technique is behind the various filters in daily used image processing tools. However, it had been a major drawback in this field that its training set required strictly paired images, until the introduction of CycleGAN, a combination of style transfer technique and Generating Adversarial Networks (GAN), which has also been one of the most popular technologies in machine learning since its advent. CycleGAN is an image style transfer technique proposed by Jun-Yan Zhu et al. at the University of California, Berkeley. The paper proposed a way that can be performed without the strict pairs of the source image and style image so that a wider range of dataset options can be incorporated into this field. The generator in the CycleGAN network consists of an encoder, converter, and decoder, which can play the role of preserving the original image features and converting images. In our research, this study found that there is still certain scope for improvement in the original model, so this study conducted a further study on the basis of rebuilding the original CycleGAN. This study studied the CycleGAN framework, which is an unsupervised learning style transfer model based on generative adversarial networks, and proposed an improved nerual network model by modifying the adversarial loss, and compared it with the original CycleGAN network. The results showed that the modified CycleGAN can obtain more realistic images with better visual effects than original CycleGAN after training in fewer rounds.
Improving the Feature Saliency by Eigenvalue Analysis

Human visual system can improve the feature saliency automatically from insufficient contrast or when there are occlusions . This paper focuses on corner/junction detection by simulating the biological vision mechanisms to increase the feature saliency. An algorithm is designed to simulate the recognization process of biological vision system. This approach depends on the orientations of edges. The feature saliency measure are increased from the image. The algorithm has been applied on several classical contour test images and the experiments demonstrate that the algorithm is more simple and can handle junctions robustly compared with previous works .
Research on personnel positioning accuracy optimization technology for complex electric power environment

Personnel location information is the core of production safety control, where existing methods such as BeiDou positioning, UWB positioning and other methods are difficult to meet the positioning needs of complex power production environment. This paper proposes a visual localization method based on the optimization of personnel recognition accuracy. For the accurate identification of personnel location in the complex electric power environment, this paper proposes a pedestrian detection method based on the fusion of multiple target detection algorithms by extending the Kalman filtering method, fusion of target detection and key point detection model, to improve the personnel identification and localization ability. It is also validated based on the data of the substation safety demonstration base environment. The conclusion of the study shows that the range and stability of target detection can be significantly improved based on the method proposed in this paper.
Design and Recognition of Cigarette Anti-counterfeiting Code Based on OpenCV

How to effectively implement the tobacco monopoly industry supervision system, and prevent fake cigarettes and private cigarettes from entering the market, is a complex problem involving many factors. This paper proposes a method of printing a unique anti-counterfeiting code on the plastic film of the cigarette outer packaging, and judging the authenticity of the cigarette by image recognition of the anti-counterfeiting code. Using OpenCV technology, the image is processed by median filtering, edge detection and corner detection, and the region of interest of the image is determined. Through affine transformation, contour detection, block processing and other operations on the region of interest, the mark is located and identified. Finally, by decoding the anti-counterfeiting code, the relevant data of the cigarette are obtained, and the authenticity of the cigarette is judged. This method can realize automatic recognition and decoding of anti-counterfeiting codes, which is simple and effective, and provides a new technical means for anti-counterfeiting in the tobacco industry.
Multimodal Sentiment Recognition Based on Sentiment Fusion Transformer

Multimodal emotion recognition (MER), which relies on its role in processing and analysing comments posted on social media and identifying the corresponding target emotion states, has a very important position in education, social media and especially in the field of HCI. In order to solve the problem of emotion recognition for video, text and other forms of data in social media, we propose a Transformer with feature. The Transformer is able to take into account the combination of different feature relations and extract global keys, so it is introduced for long-range sequential context extraction, and the residual connections are placed after the Transformer to prevent information collapse. In addition, this paper uses a multi-headed attention mechanism to combine the extracted different feature laws, and finally the fused feature vectors are fed into the classifier to classify the sentiment and output it to achieve multimodal sentiment recognition in a multimodal context.
Target recognition algorithm for UAV aerial images based on improved YOLO-X

Target detection in UAV aerial images has become one of the hot topics in recent years. Traditional detection methods have difficulty meeting both light weighting and accuracy requirements. This paper proposes a new target detection algorithm to address the issue of insufficient light weighting: using EficientNet as the main backbone network, replacing the backbone network of YOLOX, continuing to use YOLOX’s detection head and neck, and using this algorithm on the drone aerial dataset VisDrone. It was found that the average accuracy and performance are basically the same as other models, greatly improving the computational speed and efficiency of the model. Compared with other backbone networks added to YOLOX, the mAP value is improved by at least 3.4%. Compared with other advanced target detection algorithms, mAP value increased by at least 1.2%. When processing UAV target identification tasks, the YOLOX model based on EfficientNet-B0as the main trunk network has a strong recognition ability for the image targets whose color is similar to the background color, relatively fuzzy and small scale.
Person Re-Identification Based on Improved Transformer and CNN

Person re-identification is a cross-view pedestrian tracking and retrieval technology, which is of great significance in the field of security monitoring. Due to the different conditions of the shooting scene, there will be a series of problems such as low resolution, perspective occlusion, pose changes, and lighting, which bring many challenges to the application of person re-identification technology. In order to solve the problem of low model recognition accuracy due to the loss of image block information and insufficient expression of pedestrian local features in person re-identification, this paper proposes a person re-identification method based on improved Transformer and CNN. Using the backbone network combined with ResNet and Transformer enhances the ability of pedestrian feature extraction. Through a large number of experiments on the mainstream data sets Market1501 and DukeMTMC-reID, the experimental results show that the person re-identification algorithm based on the improved Transformer and CNN can effectively improve the accuracy of person re-identification
Short-time Prediction of Image Motion Trajectories Based on Convolutional Long and Short-term Memory Neural Networks

Traditional image motion trajectory prediction is based on the analysis of the observation data to make numerical predictions. This prediction method has the problems of low prediction efficiency and low accuracy when the data is large. In this paper, a prediction method based on Convolutional long and short-term memory networks model (ConvLSTM) is proposed for the short-term prediction of the movement trajectory of meteorological cloud images. And the Structural Similarity (SSIM) loss function is introduced in the model training, which effectively improves the accuracy of prediction and reduces MAE and MSE, where MAE is as low as 0.017, and MSE is as low as 0.00009. The experimental results show that the ConvLSTM-SSIM network model designed in this study can effectively extract the temporal and spatial characteristics of image sequence and realize the short-term prediction of image motion trajectory, it also provides some reference for other fields that needs short-time image sequence prediction.
Research on Enhanced AES Algorithm Based on Key Operations

AES is recognized as one of the most secure symmetric encryption algorithms and has been widely used in the field of information security. It has the advantages of simplicity, efficiency, symmetry, modularity, etc., but there are many attacks against AES, such as differential attack, linear attack, side channel attack, etc., which requires us to further study the methods to enhance the security of AES algorithm. This paper presents an enhanced AES algorithm based on key operation. The traditional SubBytes operation in AES replacing the current state matrix with values obtained from the S-box. and the ShiftRows is that every row of the state matrix is rotated to the left according to the fixed bit, while the improved method mentioned in this paper is to associate the SubBytes and ShiftRows with the key in the calculation process. After the improvement of SubBytes, the current state matrix and the KV value calculated by the wheel key matrix are XOR operation to get a new state matrix, and the new state matrix is used to replace the S-box. The ShiftRows improvement is to dynamically shift the state matrix by comparing the KV values obtained from the calculation of the wheel key. The efficiency of the algorithm will be reduced slightly due to the introduction of key-related operations in the process of this improvement scheme, but it is worthwhile to improve the security of the algorithm on the basis of the small decrease in efficiency. Experiments show that the improved AES algorithm has better avalanche effect than the traditional AES algorithm.
Object Detection Algorithm Based on YOLOv7 Traffic Infrared Image

Due to the low resolution, high noise, and strong spatial correlation of thermal infrared images, an improved YOLOv7 algorithm is proposed to solve the problem of low accuracy in road traffic infrared target detection algorithms. Introducing CBAM attention mechanism in the backbone network and Neck network to enhance the ability to locate small targets; Replace the original SPPCSPC module with an ASPP module in the Neck network to improve the detection accuracy of images with lower resolutions; Replace the original CIoU loss function with the WIoU loss function to enhance the detection accuracy of weak and small targets. Experiments on the Chinese thermal infrared data set CTIR show that compared with the YOLOv7 algorithm, the detection accuracy mAP value of the proposed algorithm is increased by 3.4%, and the detection performance is improved.
Application of Intelligent Ground Object Recognition and Classification Method Based on AI Recognition to Foreign Source Risk Assessment in Transmission Line Channels

This paper designs a foreign source classification detection method for transmission line channels based on AI recognition. Firstly, the image is collected, and then the remote sensing image data is preprocessed. The sample set of typical ground feature elements required for training is segmented on remote sensing image, and all the training samples are enhanced. It provides sufficient training data for the subsequent ground object training recognition of network model. Thirdly, different training parameters are selected according to the characteristics of different ground objects. In this algorithm, train_crop_size is adjusted, and a convolutional neural network based on shallow neural network is designed. The parameters are adjusted in terms of cavity convolution rate, down sampling, up sampling, decoding and encoding fusion image. Experiments on real environment samples show that the model can obtain high recognition accuracy and fast recognition rate in multiple spatial scales. It has strong generalization ability of natural scene and practical value of engineering.