Object detection is a crucial branch of computer vision that aims to locate and classify objects in images. Using deep convolutional neural networks (CNNs) as the primary framework for object ...detection can efficiently extract features, which is closer to real-time performance than the traditional model that extracts features manually. In recent years, the rise of Transformer with powerful self-attention mechanisms has further enhanced performance to a new level. However, when it comes to specific vision tasks in the real world, it is necessary to obtain 3D information about the spatial coordinates, orientation, and velocity of objects, which makes research on object detection in 3D scenes more active. Although LiDAR-based 3D object detection algorithms have excellent performance, they are difficult to popularize in practical applications due to their high price. Hence, we summarize the development process, different frameworks, contributions, advantages, disadvantages, and development trends of image-based 2D and 3D object detection algorithms in recent years to help more researchers better understand this field. Besides, representative datasets，evaluation metrics，related techniques and applications are introduced, and some valuable research directions are discussed.
Massive energy consumption data of buildings was generated with the development of information technology, and the real-time energy consumption data was transmitted to energy consumption monitoring ...system by the distributed wireless sensor network (WSN). Accurately predicting the energy consumption is of importance for energy manager to make advisable decision and achieve the energy conservation. In recent years, considerable attention has been gained on predicting energy use of buildings in China. More and more predictive models appeared in recent years, but it is still a hard work to construct an accurate model to predict the energy consumption due to the complexity of the influencing factors. In this paper, 40 weather factors were considered into the research as input variables, and the electricity of supermarket which was acquired by the energy monitoring system was taken as the target variable. With the aim to seek the optimal subset, three feature selection (FS) algorithms were involved in the study, respectively: stepwise, least angle regression (Lars), and Boruta algorithms. In addition, three machine learning methods that include random forest (RF) regression, gradient boosting regression (GBR), and support vector regression (SVR) algorithms were utilized in this paper and combined with three feature selection (FS) algorithms, totally are nine hybrid models aimed to explore an improved model to get a higher prediction performance. The results indicate that the FS algorithm Boruta has relatively better performance because it could work well both on RF and SVR algorithms, the machine learning method SVR could get higher accuracy on small dataset compared with the RF and GBR algorithms, and the hybrid model called SVR-Boruta was chosen to be the proposed model in this paper. What is more, four evaluate indicators were selected to verify the model performance respectively are the mean absolute error (MAE), the mean squared error(MSE), the root mean squared error (RMSE), and the
R
-squared (
R
2
), and the experiment results further verified the superiority of the recommended methodology.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In recent years, as one of the important tasks of computer vision, 3D reconstruction has received extensive attention. This paper focuses on the research progress of using deep learning to ...reconstruct the 3D shape of general objects in recent years. Taking the steps of 3D reconstruction by deep learning as the context, according to the data feature representation in the process of 3D reconstruction, it is divided into voxel, point cloud, surface mesh and implicit surface. Then, according to the number of inputting 2D images, it can be divided into single view 3D reconstruction and multi-view 3D reconstruction, which are subdivided according to the network architecture and the training mechanism they use. While the research progress of each category is discussed, the development prospects, advantages and disadvantages of each training method are analyzed. This paper studies the new hotspots in specific 3D reconstruction fields in recent years, such as 3D reconstruction of dynamic human bodies and 3D completion
GB/T 3836.1-2021 Explosive atmospheres-Part 1: Equipment-General requirements stipulates that the threshold power of RF equipment in explosive environments shall not exceed 6 W. This regulation ...limits the application of high-power RF equipment in coal mines. However, existing research on electromagnetic safety in explosive environments lacks comprehensive theoretical analysis and experimental verification. In order to solve the above problems, the electromagnetic wave thermal effect equation is derived. It is analyzed that the controllable parameters affecting the generation of thermal energy from the mixture of gas and coal dust coupled by electromagnetic waves are the electromagnetic wave coupling time, the electric field strength and the electromagnetic wave frequency. Based on the regulation in GB/T 3836.1-2021 that the maximum surface temperature of electrical equipment that may accumulate coal dust cannot exceed 150 ℃, simulation experiments are conducted using the multi physics field simulation softwar
Bladder cancer (BLCA) typically has a poor prognosis due to high relapse and metastasis rates. A growing body of evidence indicates that N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) ...play crucial roles in the progression of BLCA and the treatment response of patients with BLCA. Therefore, we conducted a comprehensive RNA-seq analysis of BLCA using data from The Cancer Genome Atlas (TCGA) to establish an m6A-related lncRNA prognostic signature (m6A-RLPS) for BLCA.
Consensus clustering analysis was used to investigate clusters of BLCA patients with varying prognoses. The least absolute shrinkage and selection operator Cox regression were used to develop the m6A-RLPS. The ESTIMATE and CIBERSORT algorithms were used to evaluate the immune composition.
A total of 745 m6A-related lncRNAs were identified using Pearson correlation analysis (|R| > 0.4, p < 0.001). Fifty-one prognostic m6A-related lncRNAs were screened using univariate Cox regression analysis. Through consensus clustering analysis, patients were divided into two clusters (clusters 1 and 2) with different overall survival rates and tumor stages based on the differential expression of the lncRNAs. Enrichment analysis demonstrated that terms related to tumor biological processes and immune-related activities were increased in patient cluster 2, which was more likely to exhibit low survival rates. Nine m6A-related prognostic lncRNAs were finally determined and subsequently used to construct the m6A-RLPS, which was verified to be an independent predictor of prognosis using univariate and multivariate Cox regression analyses. Further, a nomogram based on age, tumor stage, and the m6A-RLPS was generated and showed high accuracy and reliability with respect to predicting the survival outcomes of BLCA patients. The prognostic signature was found to be strongly correlated to tumor-infiltrating immune cells and immune checkpoint expression.
We established a novel m6A-RLPS with a favorable prognostic value for patients with BLCA. We believe that this prognostic signature can provide new insights into the tumorigenesis of BLCA and predict the treatment response in patients with BLCA.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Achieving three-dimensional (3D) omnidirectional wireless power transfer (OWPT) without any active control is challenging. This paper proposes a square polyhedron transmitter (SPTx) based on the ...reverse current of parallel coil, which can create a 3D omnidirectional magnetic field. To minimize the counteracting of the reverse magnetic field, each coil is equipped with magnetic shielding material. The coils of SPTx are connected in series and excited by a single power supply without any current amplitude or phase control circuits. The performance of SPTx system is validated with different receivers (Rx) by experiments. In the experiment with dual Rxs, the maximum transmission efficiency is 79.6% with an efficiency stability rate of 49%. In addition, the output power does not affect the efficiency, and more Rxs will increase transmission efficiency.
There are flammable and explosive gases such as gas underground in coal mines. The electromagnetic waves radiated by the 5G wireless communication system base station antenna are absorbed by the ...underground metal structure, generating discharge sparks at the metal structure breakpoint. When the energy of the electric spark reaches the minimum ignition energy of gas, an explosion may occur, which limits the application of 5G technology in coal mines. In order to evaluate the safety of the RF power of 5G wireless communication base stations, the relationship between RF power, maximum radiation field strength, and distance is obtained by analyzing the coupling of electromagnetic waves with metal structures. Using the minimum ignition energy as the safety criterion, it can be concluded that when the receiving power of the antenna load is less than 2.625 W, it can ensure that it will not cause gas explosions. The analysis shows that 700 MHz should be given priority as the 5G working frequency band in coal mines un
GB 3836.1-2021 Explosive atmospheres - Part 1: Equipment-General requirements stipulates that the RF threshold power of RF equipment in explosive environments shall not exceed 6 W. This regulation is ...derived from EU standards and lacks experimental verification, seriously restricting the application of 5G technology in mines. In order to reassess the safety of electromagnetic wave energy radiated by 5G communication equipment in mines, it is analyzed that the form of discharge generated by metal structures coupling electromagnetic waves should be low voltage breaking circuit arc discharge. The discharge energy generated by metal structures coupling electromagnetic waves is analyzed. The half wave oscillator structure that is most easily coupling with electromagnetic waves is selected as the research object. Through comparison, it is found that the discharge energy generated by the equivalent DC discharge circuit equivalent half wave oscillator is greater than that generated by the equivalent high-frequency di
Blasthole detection is crucial but challenging in tedious underground mining processes, given the diversity of surrounding rock backgrounds and uneven light intensity. However, existing algorithms ...have limitations in extracting image features and identifying differently sized objects. This study proposes a cascade-network-based blasthole detection method. The proposed method includes a blasthole feature extract transformation (BFET) module and a blasthole detection (BD) module. Firstly, we constructed the BFET module on the improved Cycle Generative Adversarial Network (CycleGAN) by multi-scale feature fusion. Then, we fused the convolution features of the generators in CycleGAN to obtain the enhanced feature map of the blasthole images. Secondly, the BD module was cascaded with the BFET module to accomplish the task of detecting blastholes. Results indicated that the detection accuracy of the blasthole image was significantly improved by strengthening the contrast of the image and suppressing over-exposure. The experimental results also showed that the proposed method enhanced the contrast of the image and could improve the accuracy of blasthole detection in real time. Compared with the YOLOv7 and CycleGAN+YOLOv7 methods, the detection accuracy of our method was improved by 5.34% and 2.38%, respectively.
PDF
