基于改进Centerfusion的自动驾驶3D目标检测模型

针对自动驾驶路面上目标漏检和错检的问题,提出一种基于改进Centerfusion的自动驾驶3D目标检测模型。该模型通过将相机信息和雷达特征融合,构成多通道特征数据输入,从而增强目标检测网络的鲁棒性,减少漏检问题;为了能够得到更加准确丰富的3D目标检测信息,引入了改进的注意力机制,用于增强视锥网格中的雷达点云和视觉信息融合;使用改进的损失函数优化边框预测的准确度。在Nuscenes数据集上进行模型验证和对比,实验结果表明,相较于传统的Centerfusion模型,提出的模型平均检测精度均值(mean Average Precision,mAP)提高了1.3%,Nuscenes检测分数(Nuscenes Detection Scores,NDS)提高了1.2%。

Hilbert空间中的fusion-Besselian框架与拟fusion-Riesz基

fusion框架作为Hilbert空间中g-框架的特例,与g-框架有许多类似的性质.该文在已有文献的基础上,借助算子理论知识,举反例说明去掉有限维空间的条件下结论不成立,进一步给出fusion-Besselian框架的算子刻画.结合fusion-Besselian框架的算子刻画和反例1,阐明在探讨该类框架性质时,应关注其适用条件和范围.随后讨论拟fusion-Riesz基与拟Riesz基、fusion-Besselian框架之间的关系.最后讨论fusion-Besselian框架和拟fusion-Riesz基的算子扰动,所得结论补充了算子扰动方面的研究.

Influence of layer thickness on formation quality,microstructure,mechanical properties,and corrosion resistance of WE43 magnesium alloy fabricated by laser powder bed fusion

Laser powder bed fusion(L-PBF)of Mg alloys has provided tremendous opportunities for customized production of aeronautical and medical parts.Layer thickness(LT)is of great significance to the L-PBF process but has not been studied for Mg alloys.In this study,WE43 Mg alloy bulk cubes,porous scaffolds,and thin walls with layer thicknesses of 10,20,30,and 40μm were fabricated.The required laser energy input increased with increasing layer thickness and was different for the bulk cubes and porous scaffolds.Porosity tended to occur at the connection joints in porous scaffolds for LT40 and could be eliminated by reducing the laser energy input.For thin wall parts,a large overhang angle or a small wall thickness resulted in porosity when a large layer thicknesses was used,and the porosity disappeared by reducing the layer thickness or laser energy input.A deeper keyhole penetration was found in all occasions with porosity,explaining the influence of layer thickness,geometrical structure,and laser energy input on the porosity.All the samples achieved a high fusion quality with a relative density of over 99.5%using the optimized laser energy input.The increased layer thickness resulted to more precipitation phases,finer grain sizes and decreased grain texture.With the similar high fusion quality,the tensile strength and elongation of bulk samples were significantly improved from 257 MPa and 1.41%with the 10μm layer to 287 MPa and 15.12%with the 40μm layer,in accordance with the microstructural change.The effect of layer thickness on the compressive properties of porous scaffolds was limited.However,the corrosion rate of bulk samples accelerated with increasing the layer thickness,mainly attributed to the increased number of precipitation phases.

Pyrimethamine upregulates BNIP3 to interfere SNARE-mediated autophagosome-lysosomal fusion in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common tumor types and remains a major clinical challenge. Increasing evidence has revealed that mitophagy inhibitors can enhance the effect of chemotherapy on HCC. However, few mitophagy inhibitors have been approved for clinical use in humans. Pyrimethamine (Pyr) is used to treat infections caused by protozoan parasites. Recent studies have reported that Pyr may be beneficial in the treatment of various tumors. However, its mechanism of action is still not clearly defined. Here, we found that blocking mitophagy sensitized cells to Pyr-induced apoptosis. Mechanistically, Pyr potently induced the accumulation of autophagosomes by inhibiting autophagosome-lysosome fusion in human HCC cells. In vitro and in vivo studies revealed that Pyr blocked autophagosome-lysosome fusion by upregulating BNIP3 to inhibit synaptosomal-associated protein 29 (SNAP29)-vesicle-associated membrane protein 8 (VAMP8) interaction. Moreover, Pyr acted synergistically with sorafenib (Sora) to induce apoptosis and inhibit HCC proliferation in vitro and in vivo. Pyr enhances the sensitivity of HCC cells to Sora, a common chemotherapeutic, by inhibiting mitophagy. Thus, these results provide new insights into the mechanism of action of Pyr and imply that Pyr could potentially be further developed as a novel mitophagy inhibitor. Notably, Pyr and Sora combination therapy could be a promising treatment for malignant HCC.

Alloy design for laser powder bed fusion additive manufacturing:a critical review

Metal additive manufacturing(AM)has been extensively studied in recent decades.Despite the significant progress achieved in manufacturing complex shapes and structures,challenges such as severe cracking when using existing alloys for laser powder bed fusion(L-PBF)AM have persisted.These challenges arise because commercial alloys are primarily designed for conventional casting or forging processes,overlooking the fast cooling rates,steep temperature gradients and multiple thermal cycles of L-PBF.To address this,there is an urgent need to develop novel alloys specifically tailored for L-PBF technologies.This review provides a comprehensive summary of the strategies employed in alloy design for L-PBF.It aims to guide future research on designing novel alloys dedicated to L-PBF instead of adapting existing alloys.The review begins by discussing the features of the L-PBF processes,focusing on rapid solidification and intrinsic heat treatment.Next,the printability of the four main existing alloys(Fe-,Ni-,Al-and Ti-based alloys)is critically assessed,with a comparison of their conventional weldability.It was found that the weldability criteria are not always applicable in estimating printability.Furthermore,the review presents recent advances in alloy development and associated strategies,categorizing them into crack mitigation-oriented,microstructure manipulation-oriented and machine learning-assisted approaches.Lastly,an outlook and suggestions are given to highlight the issues that need to be addressed in future work.

Infrared and Visible Image Fusion Based on Res2Net-Transformer Automatic Encoding and Decoding

A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations.

Characterization, preparation, and reuse of metallic powders for laser powder bed fusion: a review

Laser powder bed fusion(L-PBF) has attracted significant attention in both the industry and academic fields since its inception, providing unprecedented advantages to fabricate complex-shaped metallic components. The printing quality and performance of L-PBF alloys are infuenced by numerous variables consisting of feedstock powders, manufacturing process,and post-treatment. As the starting materials, metallic powders play a critical role in infuencing the fabrication cost, printing consistency, and properties. Given their deterministic roles, the present review aims to retrospect the recent progress on metallic powders for L-PBF including characterization, preparation, and reuse. The powder characterization mainly serves for printing consistency while powder preparation and reuse are introduced to reduce the fabrication costs.Various powder characterization and preparation methods are presented in the beginning by analyzing the measurement principles, advantages, and limitations. Subsequently, the effect of powder reuse on the powder characteristics and mechanical performance of L-PBF parts is analyzed, focusing on steels, nickel-based superalloys, titanium and titanium alloys, and aluminum alloys. The evolution trends of powders and L-PBF parts vary depending on specific alloy systems, which makes the proposal of a unified reuse protocol infeasible. Finally,perspectives are presented to cater to the increased applications of L-PBF technologies for future investigations. The present state-of-the-art work can pave the way for the broad industrial applications of L-PBF by enhancing printing consistency and reducing the total costs from the perspective of powders.

CAEFusion: A New Convolutional Autoencoder-Based Infrared and Visible Light Image Fusion Algorithm

To address the issues of incomplete information,blurred details,loss of details,and insufficient contrast in infrared and visible image fusion,an image fusion algorithm based on a convolutional autoencoder is proposed.The region attention module is meant to extract the background feature map based on the distinct properties of the background feature map and the detail feature map.A multi-scale convolution attention module is suggested to enhance the communication of feature information.At the same time,the feature transformation module is introduced to learn more robust feature representations,aiming to preserve the integrity of image information.This study uses three available datasets from TNO,FLIR,and NIR to perform thorough quantitative and qualitative trials with five additional algorithms.The methods are assessed based on four indicators:information entropy(EN),standard deviation(SD),spatial frequency(SF),and average gradient(AG).Object detection experiments were done on the M3FD dataset to further verify the algorithm’s performance in comparison with five other algorithms.The algorithm’s accuracy was evaluated using the mean average precision at a threshold of 0.5(mAP@0.5)index.Comprehensive experimental findings show that CAEFusion performs well in subjective visual and objective evaluation criteria and has promising potential in downstream object detection tasks.

An improved particle filter indoor fusion positioning approach based on Wi-Fi/PDR/geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning(PDR)and geomagnetic technology have the problems of large initial position error,low sensor accuracy,and geomagnetic mismatch.In this study,a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed,where Wi-Fi,PDR,and geomagnetic signals are integrated to improve indoor positioning performances.One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm.During the positioning process,an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively.The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected,which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm.In addition,this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving,which satisfies the real-time requirement of our fusion positioning approach.Through experimental verification,the average positioning accuracy of the proposed approach reaches 1.59 m,which improves 33.2%compared with the existing particle filter fusion positioning algorithms.

Recent research progress in the mechanism and suppression of fusion welding-induced liquation cracking of nickel based superalloys

Nickel-based superalloys are extensively used in the crucial hot-section components of industrial gas turbines,aeronautics,and astronautics because of their excellent mechanical properties and corrosion resistance at high temperatures.Fusion welding serves as an effective means for joining and repairing these alloys;however,fusion welding-induced liquation cracking has been a challenging issue.This paper comprehensively reviewed recent liquation cracking,discussing the formation mechanisms,cracking criteria,and remedies.In recent investigations,regulating material composition,changing the preweld heat treatment of the base metal,optimizing the welding process parameters,and applying auxiliary control methods are effective strategies for mitigating cracks.To promote the application of nickel-based superalloys,further research on the combination impact of multiple elements on cracking prevention and specific quantitative criteria for liquation cracking is necessary.

Microstructure and thermal properties of dissimilar M300–CuCr1Zr alloys by multi-material laser-based powder bed fusion

Multi-material laser-based powder bed fusion (PBF-LB) allows manufacturing of parts with 3-dimensional gradient and additional functionality in a single step. This research focuses on the combination of thermally-conductive CuCr1Zr with hard M300 tool steel.Two interface configurations of M300 on CuCr1Zr and CuCr1Zr on M300 were investigated. Ultra-fine grains form at the interface due to the low mutual solubility of Cu and steel. The material mixing zone size is dependent on the configurations and tunable in the range of0.1–0.3 mm by introducing a separate set of parameters for the interface layers. Microcracks and pores mainly occur in the transition zone.Regardless of these defects, the thermal diffusivity of bimetallic parts with 50vol% of CuCr1Zr significantly increases by 70%–150%compared to pure M300. The thermal diffusivity of CuCr1Zr and the hardness of M300 steel can be enhanced simultaneously by applying the aging heat treatment.

Influence of heat treatment on microstructure,mechanical and corrosion behavior of WE43 alloy fabricated by laser-beam powder bed fusion

Magnesium(Mg)alloys are considered to be a new generation of revolutionary medical metals.Laser-beam powder bed fusion(PBF-LB)is suitable for fabricating metal implants withpersonalized and complicated structures.However,the as-built part usually exhibits undesirable microstructure and unsatisfactory performance.In this work,WE43 parts were firstly fabricated by PBF-LB and then subjected to heat treatment.Although a high densification rate of 99.91%was achieved using suitable processes,the as-built parts exhibited anisotropic and layeredmicrostructure with heterogeneously precipitated Nd-rich intermetallic.After heat treatment,fine and nano-scaled Mg24Y5particles were precipitated.Meanwhile,theα-Mg grainsunderwent recrystallization and turned coarsened slightly,which effectively weakened thetexture intensity and reduced the anisotropy.As a consequence,the yield strength and ultimate tensile strength were significantly improved to(250.2±3.5)MPa and(312±3.7)MPa,respectively,while the elongation was still maintained at a high level of 15.2%.Furthermore,the homogenized microstructure reduced the tendency of localized corrosion and favoredthe development of uniform passivation film.Thus,the degradation rate of WE43 parts was decreased by an order of magnitude.Besides,in-vitro cell experiments proved their favorable biocompatibility.

Disparity estimation for multi-scale multi-sensor fusion

The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer.This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme.A binocular stereo vision sensor composed of two cameras and a light deterction and ranging(LiDAR)sensor is used to jointly perceive the environment,and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map.This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors.Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.

Approximating the Radial Distribution Function of the Electron in a Hydrogen Atom by a Normal Distribution Suggests That Magnetic Confinement Fusion Would Be Less Energy Efficient than Inertial Confinement Fusion

Since the position of the electron in a hydrogen atom cannot be determined, the region in which it resides is said to be determined stochastically and forms an electron cloud. The probability density function of the single electron in 1s orbit is expressed as φ2, a function of distance from the nucleus. However, the probability of existence of the electron is expressed as a radial distribution function at an arbitrary distance from the nucleus, so it is estimated as the probability of the entire spherical shape of that radius. In this study, it has been found that the electron existence probability approximates the radial distribution function by assuming that the probability of existence of the electron being in the vicinity of the nucleus follows a normal distribution for arbitrary x-, y-, and z-axis directions. This implies that the probability of existence of the electron, which has been known only from the distance information, would follow a normal distribution independently in the three directions. When the electrons’ motion is extremely restricted in a certain direction by the magnetic field of both tokamak and helical fusion reactors, the probability of existence of the electron increases with proximity to the nucleus, and as a result, it is less likely to be liberated from the nucleus. Therefore, more and more energy is required to free the nucleus from the electron in order to generate plasma.

Chinese Clinical Named Entity Recognition Using Multi-Feature Fusion and Multi-Scale Local Context Enhancement

Chinese Clinical Named Entity Recognition(CNER)is a crucial step in extracting medical information and is of great significance in promoting medical informatization.However,CNER poses challenges due to the specificity of clinical terminology,the complexity of Chinese text semantics,and the uncertainty of Chinese entity boundaries.To address these issues,we propose an improved CNER model,which is based on multi-feature fusion and multi-scale local context enhancement.The model simultaneously fuses multi-feature representations of pinyin,radical,Part of Speech(POS),word boundary with BERT deep contextual representations to enhance the semantic representation of text for more effective entity recognition.Furthermore,to address the model’s limitation of focusing just on global features,we incorporate Convolutional Neural Networks(CNNs)with various kernel sizes to capture multi-scale local features of the text and enhance the model’s comprehension of the text.Finally,we integrate the obtained global and local features,and employ multi-head attention mechanism(MHA)extraction to enhance the model’s focus on characters associated with medical entities,hence boosting the model’s performance.We obtained 92.74%,and 87.80%F1 scores on the two CNER benchmark datasets,CCKS2017 and CCKS2019,respectively.The results demonstrate that our model outperforms the latest models in CNER,showcasing its outstanding overall performance.It can be seen that the CNER model proposed in this study has an important application value in constructing clinical medical knowledge graph and intelligent Q&A system.

A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.

A Multi-Constraint Path Optimization Scheme Based on Information Fusion in Software Defined Network

The existingmultipath routing in Software Defined Network (SDN) is relatively blind and inefficient, and there is alack of cooperation between the terminal and network sides, making it difficult to achieve dynamic adaptationof service requirements and network resources. To address these issues, we propose a multi-constraint pathoptimization scheme based on information fusion in SDN. The proposed scheme collects network topology andnetwork state information on the network side and computes disjoint paths between end hosts. It uses the FuzzyAnalytic Hierarchy Process (FAHP) to calculate the weight coefficients of multiple constrained parameters andconstructs a composite quality evaluation function for the paths to determine the priority of the disjoint paths. TheSDN controller extracts the service attributes by analyzing the packet header and selects the optimal path for flowrule forwarding. Furthermore, the service attributes are fed back to the path composite quality evaluation function,and the path priority is dynamically adjusted to achieve dynamic adaptation between service requirements andnetwork status. By continuously monitoring and analyzing the service attributes, the scheme can ensure optimalrouting decisions in response to varying network conditions and evolving service demands. The experimentalresults demonstrated that the proposed scheme can effectively improve average throughput and link utilizationwhile meeting the Quality of Service (QoS) requirements of various applications.

A Novel Multi-Stream Fusion Network for Underwater Image Enhancement

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception.Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

Research on Driver’s Fatigue Detection Based on Information Fusion

Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly reduced,which can easily cause traffic accidents.Therefore,studying driver fatigue detectionmethods is significant in ensuring safe driving.However,the fatigue state of actual drivers is easily interfered with by the external environment(glasses and light),which leads to many problems,such as weak reliability of fatigue driving detection.Moreover,fatigue is a slow process,first manifested in physiological signals and then reflected in human face images.To improve the accuracy and stability of fatigue detection,this paper proposed a driver fatigue detection method based on image information and physiological information,designed a fatigue driving detection device,built a simulation driving experiment platform,and collected facial as well as physiological information of drivers during driving.Finally,the effectiveness of the fatigue detection method was evaluated.Eye movement feature parameters and physiological signal features of drivers’fatigue levels were extracted.The driver fatigue detection model was trained to classify fatigue and non-fatigue states based on the extracted features.Accuracy rates of the image,electroencephalogram(EEG),and blood oxygen signals were 86%,82%,and 71%,separately.Information fusion theory was presented to facilitate the fatigue detection effect;the fatigue features were fused using multiple kernel learning and typical correlation analysis methods to increase the detection accuracy to 94%.It can be seen that the fatigue driving detectionmethod based onmulti-source feature fusion effectively detected driver fatigue state,and the accuracy rate was higher than that of a single information source.In summary,fatigue drivingmonitoring has broad development prospects and can be used in traffic accident prevention and wearable driver fatigue recognition.

Research on Multi-Scale Feature Fusion Network Algorithm Based on Brain Tumor Medical Image Classification

Gliomas have the highest mortality rate of all brain tumors.Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’survival rates.This paper proposes a hierarchical multi-scale attention feature fusion medical image classification network(HMAC-Net),which effectively combines global features and local features.The network framework consists of three parallel layers:The global feature extraction layer,the local feature extraction layer,and the multi-scale feature fusion layer.A linear sparse attention mechanism is designed in the global feature extraction layer to reduce information redundancy.In the local feature extraction layer,a bilateral local attention mechanism is introduced to improve the extraction of relevant information between adjacent slices.In the multi-scale feature fusion layer,a channel fusion block combining convolutional attention mechanism and residual inverse multi-layer perceptron is proposed to prevent gradient disappearance and network degradation and improve feature representation capability.The double-branch iterative multi-scale classification block is used to improve the classification performance.On the brain glioma risk grading dataset,the results of the ablation experiment and comparison experiment show that the proposed HMAC-Net has the best performance in both qualitative analysis of heat maps and quantitative analysis of evaluation indicators.On the dataset of skin cancer classification,the generalization experiment results show that the proposed HMAC-Net has a good generalization effect.