Problem Of Accuracy Research Articles

Inference of gene regulatory networks based on directed graph convolutional networks.

Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.

Small object intelligent Detection method based on Adaptive Cascading Context

With the technology advances, deep learning-based object detection has made unprecedented progress. However, the small spatial ratio of object pixels affects the effective extraction of deep details features, resulting in poor detection results in small object detection. To improve the accuracy of small object detection, an adaptive Cascading Context small (ACC) object detection method is proposed based on YOLOv5. Firstly, a separate shallow layer feature was proposed to obtain more detailed information beneficial to small object detection. Secondly, an adaptive cascade method is proposed to fuse the output features of the three layers of the pyramid to adaptively filter negative semantic information, while fusing with shallow features to solve the problem of low classification accuracy caused by insufficient semantic information of shallow features. Finally, an adaptive context model is proposed to use a deformable convolution to obtain spatial context features of shallow small objects, associating the targets with the background, thereby improving the accuracy of small object detection. The experimental results show that the detection accuracy of the proposed method has been improved by 6.12%, 3.35%, 3.33%, and 5.2%, respectively, compared with the source code on the PASCAL VOC, NWPU VHR-10, KITTI, and RSOD datasets, which fully demonstrate the effectiveness of our method in small object detection.

A dissolved oxygen prediction model based on GRU–N-Beats

Dissolved oxygen is one of the most important water quality parameters in aquaculture, and the level determines whether fish can grow healthily. Since there is a delay in equipment control in the aquaculture environment, dissolved oxygen prediction is needed to reduce the loss due to low dissolved oxygen. To solve the problem of insufficient accuracy and poor interpretability of traditional methods in predicting dissolved oxygen from multivariate water quality parameters, this paper proposes an improved N-Beats-based prediction network. First, the maximum expectation algorithm [expectation–maximization (EM)] was used to fill in the original data by fitting the missing values. Second, the discrete wavelet transform (DWT) was used to reduce the overall noise of the sample, then the gated recurrent unit (GRU) feature extraction network was employed to extract the water quality information from the temporal dimension, the N-Beats was utilized to predict the preprocessed data, and the residual operation through Stack was performed to obtain the prediction results. The improved algorithm overcomes the challenge of insufficient prediction accuracy of the traditional algorithm. The GRU–N-Beats network proposed in this paper can extract features from multivariate time dimensions for prediction. The values of root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 for the proposed algorithm were 0.171, 0.120, 0.015, and 0.97, respectively. In particular, they were 28.5%, 32.1%, 51.6%, 24.3%, 14.9%, 36.4%, and 19.3% higher than those of long short-term memory (LSTM), GRU, temporal convolutional network (TCN), LSTM–TCN, PatchTST, back-propagation neural network (BPNN), and N-Beats on RMSE, respectively.

FFD-SLAM: A Real-Time Visual SLAM Toward Dynamic Scenes with Semantic and Optical Flow Information

To solve the problem of poor localization accuracy and robustness of visual simultaneous localization and mapping (SLAM) systems in highly dynamic environments, this paper proposes a dynamic visual SLAM algorithm called FFD-SLAM that fuses the target detection network with the optical flow method. The algorithm considers ORB-SLAM2 as the basic framework, joins the semantic thread in parallel with its tracking thread, initially obtains the set of feature points through the real-time detection of dynamic objects in the environment through YOLOv5 in the semantic thread, then filters the set of feature points obtained in the semantic thread through the optical flow module, and finally utilizes the remaining static feature points for the matching calculation. Experiments showed that the proposed algorithm showed an improvement of approximately 97% in the localization accuracy compared with the ORB-SLAM2 algorithm in a highly dynamic environment, which effectively improves the localization accuracy and robustness of the system. The proposed algorithm also showed a higher real-time performance compared with some excellent dynamic SLAM algorithms.

Prediction and Optimization Analysis of the Performance of an Office Building in an Extremely Hot and Cold Region

The White Paper on Peak Carbon and Carbon Neutral Action 2022 states that China is to achieve peak carbon by 2030 and carbon neutrality by 2060. Based on the “3060 dual-carbon” goal, how to improve the efficiency of energy performance is an important prerequisite for building a low-carbon, energy-saving, green, and beautiful China. The office performance building studied in this paper is located in the urban area of Turpan, where the climate is characterized by an extremely hot summer environment and a cold winter environment. At the same time, the building is oriented east–west, with the main façade facing west, and the main façade consists of a large area of single-layer glass curtain wall, which is affected by western sunlight. As a result, there are serious problems with the building’s energy consumption, which in turn leads to excessive carbon emissions and high life cycle costs for the building. To address the above problems, this paper analyzes and optimizes the following four dimensions. First, the article creates a Convolutional Neural Network (CNN) prediction model with Total Energy Use in Buildings (TEUI), Global Warming Potential (GWP), and Life Cycle Costs (LCC) as the performance objectives. After optimization, the R2 of the three are 0.9908, 0.9869, and 0.9969, respectively, thus solving the problem of low accuracy of traditional prediction models. Next, the NSGA-II algorithm is used to optimize the three performance objectives, which are reduced by 41.94%, 40.61%, and 31.29%, respectively. Then, in the program decision stage, this paper uses two empowered Topsis methods to optimize this building performance problem. Finally, the article analyzes the variables using two sensitivity analysis methods. Through the above research, this paper provides a framework of optimization ideas for office buildings in extremely hot and cold regions while focusing on the four major aspects of machine learning, multi-objective optimization, decision analysis, and sensitivity analysis systematically and completely. For the development of office buildings in the region, whether in the early program design or in the later stages, energy-saving measures to optimize the design have laid the foundation of important guidelines.

Trajectory tracking control of the pushing ore truck manipulator based on adaptive sliding mode control with interference observer

An improved sliding mode controller was designed to solve the problem of low tracking accuracy caused by various disturbances and errors existing in the hydraulic-driven manipulator for pushing ore truck. The state space equation of the hydraulic system of the pushing ore truck manipulator was modeled and derived, and a sliding mode function was designed. For interference signals, use interference observers for observation. For the unobserved parts, adaptive laws are used for compensation to reduce system chattering, and system’s stability is verified using Lyapunov functions. The simulation results show that, compared with the traditional Sliding mode control and adaptive sliding mode control, the adaptive sliding mode control strategy based on interference observer proposed in this paper can effectively weaken the system chattering, reduce the uncertainty caused by external interference and modeling error, and improve the trajectory tracking accuracy of the pushing ore truck manipulator.

A State-of-arts Review of Deep Learning Techniques for Speech Emotion Recognition

In sophisticated Human-Computer Interfaces (HCI), the emotional state of the user is becoming a crucial component that is closely linked to emotional speech recognition. Spoken expressions, which can be a part of human-machine interaction, are an important source of emotional information. Speech emotion recognition (SER) in deep learning (DL) continues to be a hot topic, especially in the field of emotional computing. Current deep learning (DL) and neural network methods are applied in this highly active field of research. This is as a result of its expanding potential, advancements in algorithms, and practical uses. Quantitative factors such as pitch, intensity, accent and Mel-Frequency Cepstral Coefficients (MFCC) can be employed to model the paralinguistic data contained in human speech. To achieve SER, three key procedures are usually followed: data processing, feature selection/extraction, and classification based on the underlying emotional qualities. The nature of these processes and the peculiarities of human speech lend support to the employment of DL techniques for SER implementation. A variety of DL methods have been used for SER tasks in recent affective computing research works; however, only a small number of them capture the underlying ideas and methodologies that can be used to facilitate the three main steps of SER implementation. With a focus on the three SER implementation processes, we provide a state-of-the-art assessment of research conducted over the last ten years that tackled SER tasks from DL perspectives in this work. Various issues are covered in detail, including the problem of low classification accuracy of Speaker-Independent experiments and the related remedies. The review offers principles for SER evaluation as well, emphasizing indicators that can be experimented with and common baselines.

HGR Correlation Pooling Fusion Framework for Recognition and Classification in Multimodal Remote Sensing Data

This paper investigates remote sensing data recognition and classification with multimodal data fusion. Aiming at the problems of low recognition and classification accuracy and the difficulty in integrating multimodal features in existing methods, a multimodal remote sensing data recognition and classification model based on a heatmap and Hirschfeld–Gebelein–Rényi (HGR) correlation pooling fusion operation is proposed. A novel HGR correlation pooling fusion algorithm is developed by combining a feature fusion method and an HGR maximum correlation algorithm. This method enables the restoration of the original signal without changing the value of transmitted information by performing reverse operations on the sample data. This enhances feature learning for images and improves performance in specific tasks of interpretation by efficiently using multi-modal information with varying degrees of relevance. Ship recognition experiments conducted on the QXS-SROPT dataset demonstrate that the proposed method surpasses existing remote sensing data recognition methods. Furthermore, land cover classification experiments conducted on the Houston 2013 and MUUFL datasets confirm the generalizability of the proposed method. The experimental results fully validate the effectiveness and significant superiority of the proposed method in the recognition and classification of multimodal remote sensing data.

Time dimension feature extraction and classification of high-dimensional large data streams based on unsupervised learning

In order to solve the problem of low accuracy of time dimension feature extraction and classification of high-dimensional large data streams, this paper proposes a time dimension feature extraction and classification algorithm of high-dimensional large data streams based on unsupervised learning. Analyze the trend of high-dimensional data flow changes under machine learning, and achieve dimensionality reduction of high-dimensional large traffic time dimensional data through local save projection. Analyze the spatial relationship between feature attributes and feature space, segment and fit high-dimensional big data streams and time dimensional feature data streams, further segment time dimensional sequences using sliding windows, and complete feature extraction through discrete dyadic wavelet transform. According to the clustering algorithm, cluster the time dimension feature data stream, calculate the cosine similarity of the feature data, model the time dimension feature stream of training samples, use the feature classification function to minimize the classification loss, and use unsupervised learning to achieve the final classification task. The test results show that this method can improve the temporal feature extraction and classification accuracy streams.

Capsule Attention Based Detection of Contraband in X-Ray Images

Aiming at the problem of low detection accuracy caused by the different postures, different sizes, complex backgrounds and overlapping occlusions of contraband in the security checking process, a Matrix capsule network based on attention mechanism (MCAM) is designed by introducing attention mechanism into the capsule network. Firstly, a multi-feature-extraction (MFE) module is designed to solve the difficulty of detecting contraband with different sizes and complex backgrounds; then the Conv2d with Attention for ConvCap (CACC) is constructed in the convolutional layer of the capsule, and the weight information is computed in the channel dimensions of the feature map to give the contraband regions higher coefficients to enhance the contraband detection ability when the poses are different and the occlusion is severe; finally, a new capsule detection layer is designed by utilizing the pose matrix of the capsule to give full play to the detection ability of the matrix capsule network. The mAPs of the proposed model on SIXray, SIXray10, and SIXray100 are 85.10%, 64.05%, and 53.67%, respectively, which are 42.79%, 19.73%, and 9.41% higher than those of the original network, higher than those of the current mainstream detection networks, and the number of parameters of the network and the amount of computation are also lower.

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI.

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.

A new ECT image reconstruction algorithm based on Vision transformer (ViT)

Aiming at the problem of low accuracy of ECT image reconstruction, this paper proposes an ECT image reconstruction algorithm based on Vision Transformer (ViT). ViT is a method that applies the Transformer to the field of image classification, which is characterized by strong long-distance dependency learning ability and strong multi-modal fusion ability compared to CNN. This paper fully utilizes the characteristics of ViT to transform the image reconstruction process of ECT into the classification process of ViT. Here, a method is proposed to use the object field distribution as the image classification label. This method converts a two-dimensional image of the object field distribution into a one-dimensional vector, which is the label vector of the image classification. These vectors and their corresponding reconstructed images obtained by the Landweber algorithm form the sample set of ViT. Extracting a large number of flow pattern samples with various shapes and distributions through COMSOL is used as a training set. After training ViT, this network model is used to infer the labels of the predicted flow patterns. After post-processing this label, the corresponding reconstructed image can be obtained. Finally, simulation experiments are conducted, and the experiments results show that the image errors and correlation coefficients of the reconstructed images obtained through the algorithm in this paper are better than those of Tikhonov algorithm, Landweber algorithm and Long Short-Term Memory Network(LSTM). And this algorithm has better resistance to noise interference than Tikhonov algorithm, Landweber algorithm, and LSTM. This also provides a new approach and means for ECT image reconstruction.

On-line quantitative analysis of major elements in phosphate slurry using LIBS assisted by plasma information from orthogonal directions imaging

To solve the lag of chemical element information feedback and realize the on-line analysis of phosphorite flotation, LIBS was used to measure major elements in the phosphate slurry. Plasma images in two orthogonal directions were captured synchronously to describe the irregular plasma. The plasma image projections in different directions were calculated as the features. A spectral correction method with these features was proposed to solve the problems of large signal fluctuations and poor measurement accuracy. The proposed method was first applied in the filed application of phosphate slurry on-line analysis. The RSDs of the characteristic spectral intensity decreased from more than 20 % to less than 3 %, improving the stability of spectra significantly. The method was further combined with the calibration of the concentrations of P2O5, MgO and SiO2. The R2 and MAE of P2O5 were 0.99 and 0.5 %, respectively, meeting the accuracy requirements of online measurements.

A High-Precision Monitoring Method Based on SVM Regression for Multivariate Quantitative Analysis of PID Response to VOC Signals

In the moist environment of soil-water-air, there is a problem of low accuracy in monitoring volatile organic compounds (VOCs) using a photoionization detector (PID). This study is based on the PID water-soil-gas VOC online monitor developed by this group, online monitoring of the concentration of different constituents of VOCs in different production enterprises of the petroleum and chemical industries in Shandong Province, with the concentration of the laboratory test, to build a relevant model. The correlation coefficient about the PID test concentration and the actual concentration correlation coefficient was obtained through the collection of a large number of data trainings. Based on the application of PID in VOC monitoring, the establishment of a PID high-precision calibration model is important for the precise monitoring of VOCs. In this paper, multiple quantitative analyses were conducted, based on SVM regression of PID response to VOC signals, to study the high-precision VOC monitoring method. To select the response signals of PID under different concentrations of environmental VOCs measured by the research group, first, the PID response to VOC signals was modeled using the support vector machine principle to verify the effect of traditional SVM regression. For the problem of raw data redundancy, calculate the time-domain and frequency-domain characteristics of the PID signal, and conduct the principal component analysis of the time-domain of the PID signal. In order to make the SVM regression more generalized and robust, the selection of kernel function parameters and penalty factor of SVM is optimized by genetic algorithm. By comparing the accuracy of PID calibration models such as PID signal feature extraction, SVM regression, and principal component analysis SVM regression, the superiority of photoionization detector using the signal feature extraction PCA-GA-SVM method to monitor VOCs is verified.

Detection of Small Targets in Photovoltaic Cell Defect Polarization Imaging Based on Improved YOLOv7

A photovoltaic cell defect polarization imaging small target detection method based on improved YOLOv7 is proposed to address the problem of low detection accuracy caused by insufficient feature extraction ability in the process of small target defect detection. Firstly, polarization imaging technology is introduced, using polarization degree images as inputs to enhance the edge contour information of YOLOv7 for detecting small targets; then, the COT self-attention mechanism is added to reconstruct the SPPCSPC module to improve YOLOv7’s ability to capture and fuse small target features in complex backgrounds; next, the normalized Wasserstein distance (NWD) is used to replace the traditional loss function based on intersection over union (IoU) metric, reducing the boundary offset between the prior box and the closest real target box in the prediction process of the object detection model and reducing the sensitivity of the YOLOv7 network to small object position deviations; finally, by constructing a shortwave infrared polarization imaging system to obtain polarization images of photovoltaic cells and detect small targets with scratch defects in photovoltaic cells, the applicability and effectiveness of the proposed method are verified. The results show that the proposed method has good recognition ability for small target defects in photovoltaic cells. By applying the constructed dataset, the detection accuracy reaches 98.08%, the recall rate reaches 95.06% and the mAP reaches 98.83%.

Application Big Data and Intelligent Optimization Algorithms on Teaching Evaluation Method for Higher Vocational Institutions

INTRODUCTION: The optimization of the teaching evaluation system, as an essential part of teaching reform in higher vocational colleges and universities, is conducive to the development of higher vocational colleges and universities' disciplines, making the existing teaching more standardized.OBJECTIVES: Aiming at the problems of inefficiency, incomplete index system, and low assessment accuracy in evaluation methods of higher vocational colleges and universities.METHODS: Proposes a teaching evaluation method for higher vocational colleges and universities with a big data mining algorithm and an intelligent optimization algorithm. Firstly, the teaching evaluation index system of higher vocational colleges and universities is downgraded and analyzed by using principal component analysis; then, the random forest hyperparameters are optimized by the grey wolf optimization algorithm, and the teaching evaluation model of higher vocational colleges and universities is constructed; finally, the validity and stability of the proposed method is verified by simulation experimental analysis.RESULTS: The results show that the proposed method improves the accuracy of the evaluation model.CONCLUSION: Solves the problems of low evaluation accuracy, incomplete system, and low efficiency of teaching evaluation methods in higher vocational colleges.

Research on Fault Diagnosis Method of CNC Machine Tools Based on Integrated MPA Optimised Random Forests

INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the production line of normal production.OBJECTIVES: For the current research on CNC machine tool fault diagnosis, there are problems such as poorly considered feature selection and insufficiently precise methods.METHODS: This paper proposes a CNC machine tool fault diagnosis method based on improving random forest by intelligent optimisation algorithm with integrated learning as the framework. Firstly, the CNC machine tool fault diagnosis process is analysed to extract the CNC machine tool fault features and construct the time domain, frequency domain and time-frequency domain feature system; then, the random forest is improved by the marine predator optimization algorithm with integrated learning as the framework to construct the CNC machine tool fault diagnosis model; finally, the validity and superiority of the proposed method is verified by simulation experiment analysis.RESULTS: The results show that the proposed method meets the real-time requirements while improving the diagnosis accuracy.CONCLUSION: Solve the problem of poor accuracy of fault diagnosis of CNC machine tools and unsound feature system.

Numerical simulation and experimental analysis of foreign object impact on aero-engine fan rotor blade

The detection and identification of fan rotor foreign object impact events during the working process of aero-engines are crucial to the safety and security of aircraft flight. Through the numerical simulation method based on finite element and the foreign object impact test platform to simulate the process of real engine impact by foreign objects, the rule of fan rotor blade foreign object impact and the monitoring and identification method were studied. Meanwhile, aiming at the problems of high difficulty and low accuracy in identifying the impact of foreign objects on the fan by the airborne parameters and added vibration parameters, the non-contact vibration measurement method based on blade tip timing was used to obtain the blade vibration displacement under different fan speed, foreign matter mass and impact velocity. The numerical simulation and experimental results show that the change trend of the vibration displacement of the rotor tip of the fan is consistent with that of the numerical simulation. And with the increase of the foreign object projectile mass, the vibration displacement of the blade tip caused by the impact of the foreign object on the fan blade increases. In addition, the impact velocity of the foreign object has little effect on the vibration displacement of the blade tips after impact. Furthermore, the vibration displacement of the blade tip caused by the impact of foreign objects first increases and then decreases with increasing speed.

YOLOv5s-CEDB: A robust and efficiency Camellia oleifera fruit detection algorithm in complex natural scenes

To solve the problem of poor recognition accuracy caused by various colors, uneven distribution, and occlusion by branches and leaves of Camellia oleifera fruits under natural growth conditions, this study proposes an improved deep learning network, YOLOv5s-CEDB, for Camellia oleifera fruit detection based on YOLOv5s. Coordinate Attention Mechanism (CoordAtt), Deformable Convolution (DConv), and Explicit Visual Center (EVC) are introduced to enhance the network’s local and global feature extraction performance. To improve the detection performance for small and dense targets, the feature fusion module of the network was replaced with the designed light-bidirectional feature pyramid (light-BiFPN) structure. GhostConv was used to reduce the parameters and inference speed of the structure. A dataset with different light conditions, colors, and density levels of Camellia oleifera fruits was established, and performance evaluation experiments were conducted. Experimental results showed that the mean Average Precision (mAP) and F1-score of the designed YOLOv5s-CEDB network reached 91.4 % and 89.6 %, respectively, which were 2.6 % and 3.6 % higher than those of the original YOLOv5s model, respectively, and the influencing frame rate arrived at 37.6 FPS. Under different colors, distribution densities, occlusion scenarios, and light intensities, the detection accuracy of the YOLOv5s-CEDB network model was significantly better than those of the YOLOv5s, YOLOv8s and Faster-RCNN networks. It was verified that the proposed YOLOv5s-CEDB network could significantly improve the accuracy and stability of Camellia oleifera fruit detection, satisfying the requirements of yield estimation and intelligent harvesting.

Prediction mechanism of depression tendency among college students under computer intelligent systems

Abstract In response to the existing problems in the prediction accuracy, data collection, real-time monitoring, and consideration of factors leading to depression in the current mechanism for predicting depression among college students, this article used computer intelligent systems to study the prediction mechanism of depression among college students. This article conducted a survey on students at University A using a survey questionnaire to understand the main reasons that affect their tendency to develop depression. It processed and analyzed the data using the Beck Depression Scale and Statistical Product and Service Solution 21.0 (SPSS 21.0). Meanwhile, natural language processing techniques in computer intelligence systems can be utilized. This article combines emotional dictionaries and word frequency-inverse document frequency to construct a prediction mechanism model for depression tendencies among college students, improving the accuracy of predicting student depression tendencies. The experiment shows that the average accuracy of the depression tendency prediction mechanism model constructed based on Natural Language Processing technology after 50 experiments was 97.02%, which was 5.33% higher than the model constructed based on neural network calculations. Overall, research on the prediction mechanism of depression tendency among college students based on computer intelligence systems can provide more effective mental health support and intervention measures for schools, helping students improve their psychological state, academic achievement, and quality of life.