Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet,this paper proposes a novel lightweight neural network model called ResghostNet.This model constr...Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet,this paper proposes a novel lightweight neural network model called ResghostNet.This model constructs the Resghost Module by combining residual connections and Adaptive-SE Blocks,which enhances the quality of generated feature maps through direct propagation of original input information and selection of important channels before cheap operations.Specifically,ResghostNet introduces residual connections on the basis of the Ghost Module to optimize the information flow,and designs a weight self-attention mechanism combined with SE blocks to enhance feature expression capabilities in cheap operations.Experimental results on the ImageNet dataset show that,compared to GhostNet,ResghostNet achieves higher accuracy while reducing the number of parameters by 52%.Although the computational complexity increases,by optimizing the usage strategy of GPU cachememory,themodel’s inference speed becomes faster.The ResghostNet is optimized in terms of classification accuracy and the number of model parameters,and shows great potential in edge computing devices.展开更多
The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automaticallysegment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancingtumor cor...The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automaticallysegment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancingtumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, itis very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantagesof DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks.We used dense blocks in the encoder part and residual blocks in the decoder part. The number of output featuremaps increases with the network layers in contracting path of encoder, which is consistent with the characteristicsof dense blocks. Using dense blocks can decrease the number of network parameters, deepen network layers,strengthen feature propagation, alleviate vanishing-gradient and enlarge receptive fields. The residual blockswere used in the decoder to replace the convolution neural block of original U-Net, which made the networkperformance better. Our proposed approach was trained and validated on the BraTS2019 training and validationdata set. We obtained dice scores of 0.901, 0.815 and 0.766 for whole tumor, tumor core and enhancing tumorcore respectively on the BraTS2019 validation data set. Our method has the better performance than the original3D U-Net. The results of our experiment demonstrate that compared with some state-of-the-art methods, ourapproach is a competitive automatic brain tumor segmentation method.展开更多
Residual neural network (ResNet) is a powerful neural network architecture that has proven to be excellent in extracting spatial and channel-wise information of images. ResNet employs a residual learning strategy that...Residual neural network (ResNet) is a powerful neural network architecture that has proven to be excellent in extracting spatial and channel-wise information of images. ResNet employs a residual learning strategy that maps inputs directly to outputs, making it less difficult to optimize. In this paper, we incorporate differential information into the original residual block to improve the representative ability of the ResNet, allowing the modified network to capture more complex and metaphysical features. The proposed DFNet preserves the features after each convolutional operation in the residual block, and combines the feature maps of different levels of abstraction through the differential information. To verify the effectiveness of DFNet on image recognition, we select six distinct classification datasets. The experimental results show that our proposed DFNet has better performance and generalization ability than other state-of-the-art variants of ResNet in terms of classification accuracy and other statistical analysis.展开更多
Generative adversarial networks(GANs)are paid more attention to dealing with the end-to-end speech enhancement in recent years.Various GANbased enhancement methods are presented to improve the quality of reconstructed...Generative adversarial networks(GANs)are paid more attention to dealing with the end-to-end speech enhancement in recent years.Various GANbased enhancement methods are presented to improve the quality of reconstructed speech.However,the performance of these GAN-based methods is worse than those of masking-based methods.To tackle this problem,we propose speech enhancement method with a residual dense generative adversarial network(RDGAN)contributing to map the log-power spectrum(LPS)of degraded speech to the clean one.In detail,a residual dense block(RDB)architecture is designed to better estimate the LPS of clean speech,which can extract rich local features of LPS through densely connected convolution layers.Meanwhile,sequential RDB connections are incorporated on various scales of LPS.It significantly increases the feature learning flexibility and robustness in the time-frequency domain.Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments.Specifically,in the untrained acoustic test with limited priors,e.g.,unmatched signal-to-noise ratio(SNR)and unmatched noise category,RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes.It indicates that our method is more generalized in untrained conditions.展开更多
In the field of image denoising, deep learning technology holds a dominance. However, the current network model tends to lose fine-grained information with the depth of the network. To address this issue, this paper p...In the field of image denoising, deep learning technology holds a dominance. However, the current network model tends to lose fine-grained information with the depth of the network. To address this issue, this paper proposes a Multi-scale Attention Dilated Residual Image Denoising Network(MADRNet) based on skip connection, which consists of Dense Interval Transmission Block(DTB), Sparse Residual Block(SRB), Dilated Residual Attention Reconstruction Block(DRAB) and Noise Extraction Block(NEB). The DTB enhances the classical dense layer by reducing information redundancy and extracting more accurate feature information. Meanwhile, SRB improves feature information exchange and model generalization through the use of sparse mechanism and skip connection strategy with different expansion factors. The NEB is primarily responsible for extracting and estimating noise. Its output, together with that of the sparse residual module, acts on the DRAB to effectively prevent loss of shallow feature information and improve denoising effect. Furthermore, the DRAB integrates an dilated residual block into an attention mechanism to extract hidden noise information while using residual learning technology to reconstruct clear images. We respectively examined the performance of MADRNet in gray image denoising, color image denoising and real image denoising. The experiment results demonstrate that proposed network outperforms some excellent image denoising network in terms of peak signal-to-noise ratio, structural similarity index measurement and denoising time. The proposed network effectively addresses issues associated with the loss of detail information.展开更多
The efficient use of building materials is one of the responses to increasing urbanization and building energy consumption. Soil as a building material has been used for several thousand years due to its availability ...The efficient use of building materials is one of the responses to increasing urbanization and building energy consumption. Soil as a building material has been used for several thousand years due to its availability and its usual properties improving and stabilization techniques used. Thus, fonio straws and shea butter residues are incorporated into tow soil matrix. The objective of this study is to develop a construction eco-material by recycling agricultural and biopolymer by-products in compressed earth blocks (CEB) stabilization and analyze these by-products’ influence on CEB usual properties. To do this, compressed stabilized earth blocks (CSEB) composed of clay and varying proportion (3% to 10%) of fonio straw and shea butter residue incorporated were subjected to thermophysical, flexural, compressive, and durability tests. The results obtained show that the addition of fonio straw and shea butter residues as stabilizers improves compressed stabilized earth blocks thermophysical and mechanical performance and durability. Two different clay materials were studied. Indeed, for these CEB incorporating 3% fonio straw and 3% - 10% shea butter residue, the average compressive strength and three-point bending strength values after 28 days old are respectively 3.478 MPa and 1.062 MPa. In terms of CSEB thermal properties, the average thermal conductivity is 0.549 W/m·K with 3% fonio straw and from 0.667 to 0.798 W/m. K is with 3% - 10% shea butter residue and the average thermal diffusivity is 1.665.10-7 m2/s with 3% FF and 2.24.10-7 m2/s with 3.055.10-7 m2/s with 3% - 10% shea butter residue, while the average specific heat mass is between 1.508 and 1.584 kJ/kg·K. In addition, the shea butter residue incorporated at 3% - 10% improves CSEB water repellency, with capillary coefficient values between 31 and 68 [g/m2·s]1/2 and a contact angle between 43.63°C and 86.4°C. Analysis of the results shows that, it is possible to use these CSEB for single-storey housing construction.展开更多
Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network(DNN).But the mapping-based methods only utilizes the phase of noisy speech,which limits the u...Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network(DNN).But the mapping-based methods only utilizes the phase of noisy speech,which limits the upper bound of speech enhancement performance.Maskingbased methods need to accurately estimate the masking which is still the key problem.Combining the advantages of above two types of methods,this paper proposes the speech enhancement algorithm MM-RDN(maskingmapping residual dense network)based on masking-mapping(MM)and residual dense network(RDN).Using the logarithmic power spectrogram(LPS)of consecutive frames,MM estimates the ideal ratio masking(IRM)matrix of consecutive frames.RDN can make full use of feature maps of all layers.Meanwhile,using the global residual learning to combine the shallow features and deep features,RDN obtains the global dense features from the LPS,thereby improves estimated accuracy of the IRM matrix.Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments.Specifically,in the untrained acoustic test with limited priors,e.g.,unmatched signal-to-noise ratio(SNR)and unmatched noise category,MM-RDN can still outperform the existing convolutional recurrent network(CRN)method in themeasures of perceptual evaluation of speech quality(PESQ)and other evaluation indexes.It indicates that the proposed algorithm is more generalized in untrained conditions.展开更多
Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational h...Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.展开更多
Objective:To observe the incidence of residual neuromuscular blockade at the end of operation and during tracheal extubation, and analyze the risk factors causing residual neuromuscular blockade by judging the degree ...Objective:To observe the incidence of residual neuromuscular blockade at the end of operation and during tracheal extubation, and analyze the risk factors causing residual neuromuscular blockade by judging the degree of muscle relaxation according to clinical signs when after using rocuronium or cis-atracurium in general anesthesia.Methods: 500 adults were implemented with propofol-remifentanil intravenous anesthesia or sevoflurane inhalation anesthesia. Rocuronium and cis-atracurium were given, respectively. The TOFr was observed with blind method by TOF Watch SX monitor during anesthesia.Results: The mean TOFr=0.53±0.38 at the end of operation,including 275 cases of 0<TOFr<0.9 and 112 cases of TOFr=0. The mean TOFr=0.97±0.12 at extubation, including 60 cases of TOFr<0.9. The incidence of residual neuromuscular blockade at extubation showed an increasing trend with the increase of age or body mass index. The average TOFr value at extubation, which interval time over 10 min after neostigmine administration to extubation was significant higher than that of interval time less than 10 min.Conclusions:There has 12% patients with TOFr<0.9 when extubation by estimating rocuronium and cis-atracurium effect with clinical signs and experience, it has a hidden danger of residual neuromuscular blockade. The main risk factors to increasing the incidence of residual neuromuscular blockade are growing old and the short time of administrating muscle relaxants or neostigmine to extubation.展开更多
Agricultural wastes and sawdust combined with cement matrix in the manufacture of building elements has been practiced with success in developed countries. In this study, sawdust from wood species (Pinus caribaea and ...Agricultural wastes and sawdust combined with cement matrix in the manufacture of building elements has been practiced with success in developed countries. In this study, sawdust from wood species (Pinus caribaea and Eucalyptus grandis) and an agricultural waste—rice husk (Oriza sativa) were combined with Portland cement type V (high initial strength), modified by polymer styrene-butadiene (SBR) addition. Hollow blocks produced with Eucalyptus grandis and rice husk residues showed better compressive strength;however, those produced with residues derived from Pinus caribaea presented non-satisfactory results, due to the particle size that was used.展开更多
The use of soil as a construction material is limited due to climatic conditions such as rain and wind effects. The valorization of industrial and agricultural by-products in soil-material-based composites for constru...The use of soil as a construction material is limited due to climatic conditions such as rain and wind effects. The valorization of industrial and agricultural by-products in soil-material-based composites for construction materials is an alternative to producing eco-materials for building construction. This study evaluates the effect of Shea Butter residue (SBr) and hydrated lime (HL) as stabilizers on the performance of Compressed Earth Blocks (CEB). For the production of CEB specimens, firstly the dry mixtures were prepared using soil material and 5 wt% HL, 5% - 25% wt% SBr and secondly, the appropriate amount of water was thoroughly mixed with the dry mixtures using the result of the proctor compaction test. All the moistened mixtures were mechanically pressed into CEBs on mold size (29.5 cm × 14 cm × 9.5 cm), cured at ambient temperature in the lab for 0 - 45 days, and dried at 60˚C for 7 days before being tested. The results give for the accessible porosity, bulk density, maximum dry and wet compressive strength, the respective value 31.58%;1580 kg/cm2;3.26 MPa and 0.75 MPa for CEB stabilized with 5 wt% lime without SBr. Moreover, the abrasion coefficient (14.49 cm2/g), the mass lost (0.08%), the surface depth (3.25 mm/h), the eroded surface (9.12 cm2), the sorptivity (0.046 g/cm2·min1/2 the absorption by total immersion at 2 h and 24 h (4.06 and 11.94%) are best for the CEBs stabilized with 5/5 wt% HL/SSBr. However, the lower thermal properties were obtained with CEB stabilized with 25 wt% SSBr. We therefore observe the significant reaction between these industrial and agricultural by-products with the earth material, with effects particularly on the hydric, thermal and durability properties. The use of industrial and agricultural by-products such as lime and SBr at an appropriate rate of 5 wt% are suitable to improve CEBs performances.展开更多
针对自动驾驶场景下,近处干扰点云误检率高、远处稀疏点云漏检率高的问题,提出了一种基于改进PointPillars的自动驾驶障碍物点云检测算法.首先,通过聚合模块和共享多层感知机(shared multi-layer perceptron,MLP)对柱体内点云进行特征编...针对自动驾驶场景下,近处干扰点云误检率高、远处稀疏点云漏检率高的问题,提出了一种基于改进PointPillars的自动驾驶障碍物点云检测算法.首先,通过聚合模块和共享多层感知机(shared multi-layer perceptron,MLP)对柱体内点云进行特征编码,采用最大池化与平均池化叠加的方法将点云的显著特征与细节特征映射为柱体特征;其次,针对算法对伪图特征关注与利用不充分的问题,引入坐标注意力(coordinate attention,CA)机制和残差连接的伪图特征提取模块(attention and residual second block,ARSB),将深层与浅层特征图进行融合,优化算法梯度,增强算法对有效目标的关注度.试验结果表明:改进算法对全局点云检测精度较高,平均精度优于PointPillars、稀疏到稠密3D目标检测器(STD)等点云目标检测算法,在汽车类别上的检测精度优势明显,检测速度较快,符合实时性要求.展开更多
随着我国智能养殖的快速发展,利用目标检测技术实现对蜜蜂的实时动态监测,对提升养蜂业的数字化与智能化水平具有重要意义。针对复杂背景下蜜蜂检测难度大、准确率不高的问题,提出一种基于YOLOv8(you only look once version 8)改进的...随着我国智能养殖的快速发展,利用目标检测技术实现对蜜蜂的实时动态监测,对提升养蜂业的数字化与智能化水平具有重要意义。针对复杂背景下蜜蜂检测难度大、准确率不高的问题,提出一种基于YOLOv8(you only look once version 8)改进的目标检测算法YOLO-iTN。该算法在主干网络使用反向残差移动块(inverted residual mobile block,iRMB)改进C2f,提出全新的iC2f(iRMB-C2f),增强对小目标的检测能力。在颈部网络提出新的跨域多尺度特征融合网络TX-BiFPN改进PANet(path aggregation network),利用细节特征和跳跃连接,提升多尺度特征融合能力。在头部网络增加极小目标检测头,去掉大目标检测头,强化对浅层特征信息的利用。此外,引入了归一化高斯Wasserstein距离(normalized Wasserstein distance,NWD)损失函数削弱模型对小目标位置偏差的敏感性,提高对小目标的识别检测能力。结果表明,YOLO-iTN的平均检测精度AP50较原始YOLOv8提升1.6百分点,AP50:95提升2.0百分点,综合性能优于原始YOLOv8及其他模型。展开更多
基金funded by Science and Technology Innovation Project grant No.ZZKY20222304.
文摘Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet,this paper proposes a novel lightweight neural network model called ResghostNet.This model constructs the Resghost Module by combining residual connections and Adaptive-SE Blocks,which enhances the quality of generated feature maps through direct propagation of original input information and selection of important channels before cheap operations.Specifically,ResghostNet introduces residual connections on the basis of the Ghost Module to optimize the information flow,and designs a weight self-attention mechanism combined with SE blocks to enhance feature expression capabilities in cheap operations.Experimental results on the ImageNet dataset show that,compared to GhostNet,ResghostNet achieves higher accuracy while reducing the number of parameters by 52%.Although the computational complexity increases,by optimizing the usage strategy of GPU cachememory,themodel’s inference speed becomes faster.The ResghostNet is optimized in terms of classification accuracy and the number of model parameters,and shows great potential in edge computing devices.
基金This was supported partially by Sichuan Science and Technology Program under Grants 2019YJ0356,21ZDYF2484,21GJHZ0061Scientific Research Foundation of Education Department of Sichuan Province under Grant 18ZB0117.
文摘The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automaticallysegment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancingtumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, itis very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantagesof DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks.We used dense blocks in the encoder part and residual blocks in the decoder part. The number of output featuremaps increases with the network layers in contracting path of encoder, which is consistent with the characteristicsof dense blocks. Using dense blocks can decrease the number of network parameters, deepen network layers,strengthen feature propagation, alleviate vanishing-gradient and enlarge receptive fields. The residual blockswere used in the decoder to replace the convolution neural block of original U-Net, which made the networkperformance better. Our proposed approach was trained and validated on the BraTS2019 training and validationdata set. We obtained dice scores of 0.901, 0.815 and 0.766 for whole tumor, tumor core and enhancing tumorcore respectively on the BraTS2019 validation data set. Our method has the better performance than the original3D U-Net. The results of our experiment demonstrate that compared with some state-of-the-art methods, ourapproach is a competitive automatic brain tumor segmentation method.
基金supported by the Japan Society for the Promotion of Science(JSPS)KAKENHI under Grant JP22H03643Japan Science and Technology Agency(JST)Support for Pioneering Research Initiated by the Next Generation(SPRING)under Grant JPMJSP2145JST through the Establishment of University Fellowships towards the Creation of Science Technology Innovation under Grant JPMJFS2115.
文摘Residual neural network (ResNet) is a powerful neural network architecture that has proven to be excellent in extracting spatial and channel-wise information of images. ResNet employs a residual learning strategy that maps inputs directly to outputs, making it less difficult to optimize. In this paper, we incorporate differential information into the original residual block to improve the representative ability of the ResNet, allowing the modified network to capture more complex and metaphysical features. The proposed DFNet preserves the features after each convolutional operation in the residual block, and combines the feature maps of different levels of abstraction through the differential information. To verify the effectiveness of DFNet on image recognition, we select six distinct classification datasets. The experimental results show that our proposed DFNet has better performance and generalization ability than other state-of-the-art variants of ResNet in terms of classification accuracy and other statistical analysis.
基金This work is supported by the National Key Research and Development Program of China under Grant 2020YFC2004003 and Grant 2020YFC2004002the National Nature Science Foundation of China(NSFC)under Grant No.61571106。
文摘Generative adversarial networks(GANs)are paid more attention to dealing with the end-to-end speech enhancement in recent years.Various GANbased enhancement methods are presented to improve the quality of reconstructed speech.However,the performance of these GAN-based methods is worse than those of masking-based methods.To tackle this problem,we propose speech enhancement method with a residual dense generative adversarial network(RDGAN)contributing to map the log-power spectrum(LPS)of degraded speech to the clean one.In detail,a residual dense block(RDB)architecture is designed to better estimate the LPS of clean speech,which can extract rich local features of LPS through densely connected convolution layers.Meanwhile,sequential RDB connections are incorporated on various scales of LPS.It significantly increases the feature learning flexibility and robustness in the time-frequency domain.Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments.Specifically,in the untrained acoustic test with limited priors,e.g.,unmatched signal-to-noise ratio(SNR)and unmatched noise category,RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes.It indicates that our method is more generalized in untrained conditions.
基金funded by National Nature Science Foundation of China,grant number 61302188。
文摘In the field of image denoising, deep learning technology holds a dominance. However, the current network model tends to lose fine-grained information with the depth of the network. To address this issue, this paper proposes a Multi-scale Attention Dilated Residual Image Denoising Network(MADRNet) based on skip connection, which consists of Dense Interval Transmission Block(DTB), Sparse Residual Block(SRB), Dilated Residual Attention Reconstruction Block(DRAB) and Noise Extraction Block(NEB). The DTB enhances the classical dense layer by reducing information redundancy and extracting more accurate feature information. Meanwhile, SRB improves feature information exchange and model generalization through the use of sparse mechanism and skip connection strategy with different expansion factors. The NEB is primarily responsible for extracting and estimating noise. Its output, together with that of the sparse residual module, acts on the DRAB to effectively prevent loss of shallow feature information and improve denoising effect. Furthermore, the DRAB integrates an dilated residual block into an attention mechanism to extract hidden noise information while using residual learning technology to reconstruct clear images. We respectively examined the performance of MADRNet in gray image denoising, color image denoising and real image denoising. The experiment results demonstrate that proposed network outperforms some excellent image denoising network in terms of peak signal-to-noise ratio, structural similarity index measurement and denoising time. The proposed network effectively addresses issues associated with the loss of detail information.
文摘The efficient use of building materials is one of the responses to increasing urbanization and building energy consumption. Soil as a building material has been used for several thousand years due to its availability and its usual properties improving and stabilization techniques used. Thus, fonio straws and shea butter residues are incorporated into tow soil matrix. The objective of this study is to develop a construction eco-material by recycling agricultural and biopolymer by-products in compressed earth blocks (CEB) stabilization and analyze these by-products’ influence on CEB usual properties. To do this, compressed stabilized earth blocks (CSEB) composed of clay and varying proportion (3% to 10%) of fonio straw and shea butter residue incorporated were subjected to thermophysical, flexural, compressive, and durability tests. The results obtained show that the addition of fonio straw and shea butter residues as stabilizers improves compressed stabilized earth blocks thermophysical and mechanical performance and durability. Two different clay materials were studied. Indeed, for these CEB incorporating 3% fonio straw and 3% - 10% shea butter residue, the average compressive strength and three-point bending strength values after 28 days old are respectively 3.478 MPa and 1.062 MPa. In terms of CSEB thermal properties, the average thermal conductivity is 0.549 W/m·K with 3% fonio straw and from 0.667 to 0.798 W/m. K is with 3% - 10% shea butter residue and the average thermal diffusivity is 1.665.10-7 m2/s with 3% FF and 2.24.10-7 m2/s with 3.055.10-7 m2/s with 3% - 10% shea butter residue, while the average specific heat mass is between 1.508 and 1.584 kJ/kg·K. In addition, the shea butter residue incorporated at 3% - 10% improves CSEB water repellency, with capillary coefficient values between 31 and 68 [g/m2·s]1/2 and a contact angle between 43.63°C and 86.4°C. Analysis of the results shows that, it is possible to use these CSEB for single-storey housing construction.
基金supported by the National Key Research and Development Program of China under Grant 2020YFC2004003 and Grant 2020YFC2004002the National Nature Science Foundation of China(NSFC)under Grant No.61571106.
文摘Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network(DNN).But the mapping-based methods only utilizes the phase of noisy speech,which limits the upper bound of speech enhancement performance.Maskingbased methods need to accurately estimate the masking which is still the key problem.Combining the advantages of above two types of methods,this paper proposes the speech enhancement algorithm MM-RDN(maskingmapping residual dense network)based on masking-mapping(MM)and residual dense network(RDN).Using the logarithmic power spectrogram(LPS)of consecutive frames,MM estimates the ideal ratio masking(IRM)matrix of consecutive frames.RDN can make full use of feature maps of all layers.Meanwhile,using the global residual learning to combine the shallow features and deep features,RDN obtains the global dense features from the LPS,thereby improves estimated accuracy of the IRM matrix.Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments.Specifically,in the untrained acoustic test with limited priors,e.g.,unmatched signal-to-noise ratio(SNR)and unmatched noise category,MM-RDN can still outperform the existing convolutional recurrent network(CRN)method in themeasures of perceptual evaluation of speech quality(PESQ)and other evaluation indexes.It indicates that the proposed algorithm is more generalized in untrained conditions.
基金National Natural Science Foundation of China(Grant No.62073227)Liaoning Provincial Science and Technology Department Foundation(Grant No.2023JH2/101300212).
文摘Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.
基金Guangdong science and technology plan project (2013B31800248).
文摘Objective:To observe the incidence of residual neuromuscular blockade at the end of operation and during tracheal extubation, and analyze the risk factors causing residual neuromuscular blockade by judging the degree of muscle relaxation according to clinical signs when after using rocuronium or cis-atracurium in general anesthesia.Methods: 500 adults were implemented with propofol-remifentanil intravenous anesthesia or sevoflurane inhalation anesthesia. Rocuronium and cis-atracurium were given, respectively. The TOFr was observed with blind method by TOF Watch SX monitor during anesthesia.Results: The mean TOFr=0.53±0.38 at the end of operation,including 275 cases of 0<TOFr<0.9 and 112 cases of TOFr=0. The mean TOFr=0.97±0.12 at extubation, including 60 cases of TOFr<0.9. The incidence of residual neuromuscular blockade at extubation showed an increasing trend with the increase of age or body mass index. The average TOFr value at extubation, which interval time over 10 min after neostigmine administration to extubation was significant higher than that of interval time less than 10 min.Conclusions:There has 12% patients with TOFr<0.9 when extubation by estimating rocuronium and cis-atracurium effect with clinical signs and experience, it has a hidden danger of residual neuromuscular blockade. The main risk factors to increasing the incidence of residual neuromuscular blockade are growing old and the short time of administrating muscle relaxants or neostigmine to extubation.
文摘Agricultural wastes and sawdust combined with cement matrix in the manufacture of building elements has been practiced with success in developed countries. In this study, sawdust from wood species (Pinus caribaea and Eucalyptus grandis) and an agricultural waste—rice husk (Oriza sativa) were combined with Portland cement type V (high initial strength), modified by polymer styrene-butadiene (SBR) addition. Hollow blocks produced with Eucalyptus grandis and rice husk residues showed better compressive strength;however, those produced with residues derived from Pinus caribaea presented non-satisfactory results, due to the particle size that was used.
文摘The use of soil as a construction material is limited due to climatic conditions such as rain and wind effects. The valorization of industrial and agricultural by-products in soil-material-based composites for construction materials is an alternative to producing eco-materials for building construction. This study evaluates the effect of Shea Butter residue (SBr) and hydrated lime (HL) as stabilizers on the performance of Compressed Earth Blocks (CEB). For the production of CEB specimens, firstly the dry mixtures were prepared using soil material and 5 wt% HL, 5% - 25% wt% SBr and secondly, the appropriate amount of water was thoroughly mixed with the dry mixtures using the result of the proctor compaction test. All the moistened mixtures were mechanically pressed into CEBs on mold size (29.5 cm × 14 cm × 9.5 cm), cured at ambient temperature in the lab for 0 - 45 days, and dried at 60˚C for 7 days before being tested. The results give for the accessible porosity, bulk density, maximum dry and wet compressive strength, the respective value 31.58%;1580 kg/cm2;3.26 MPa and 0.75 MPa for CEB stabilized with 5 wt% lime without SBr. Moreover, the abrasion coefficient (14.49 cm2/g), the mass lost (0.08%), the surface depth (3.25 mm/h), the eroded surface (9.12 cm2), the sorptivity (0.046 g/cm2·min1/2 the absorption by total immersion at 2 h and 24 h (4.06 and 11.94%) are best for the CEBs stabilized with 5/5 wt% HL/SSBr. However, the lower thermal properties were obtained with CEB stabilized with 25 wt% SSBr. We therefore observe the significant reaction between these industrial and agricultural by-products with the earth material, with effects particularly on the hydric, thermal and durability properties. The use of industrial and agricultural by-products such as lime and SBr at an appropriate rate of 5 wt% are suitable to improve CEBs performances.
文摘针对自动驾驶场景下,近处干扰点云误检率高、远处稀疏点云漏检率高的问题,提出了一种基于改进PointPillars的自动驾驶障碍物点云检测算法.首先,通过聚合模块和共享多层感知机(shared multi-layer perceptron,MLP)对柱体内点云进行特征编码,采用最大池化与平均池化叠加的方法将点云的显著特征与细节特征映射为柱体特征;其次,针对算法对伪图特征关注与利用不充分的问题,引入坐标注意力(coordinate attention,CA)机制和残差连接的伪图特征提取模块(attention and residual second block,ARSB),将深层与浅层特征图进行融合,优化算法梯度,增强算法对有效目标的关注度.试验结果表明:改进算法对全局点云检测精度较高,平均精度优于PointPillars、稀疏到稠密3D目标检测器(STD)等点云目标检测算法,在汽车类别上的检测精度优势明显,检测速度较快,符合实时性要求.
文摘随着我国智能养殖的快速发展,利用目标检测技术实现对蜜蜂的实时动态监测,对提升养蜂业的数字化与智能化水平具有重要意义。针对复杂背景下蜜蜂检测难度大、准确率不高的问题,提出一种基于YOLOv8(you only look once version 8)改进的目标检测算法YOLO-iTN。该算法在主干网络使用反向残差移动块(inverted residual mobile block,iRMB)改进C2f,提出全新的iC2f(iRMB-C2f),增强对小目标的检测能力。在颈部网络提出新的跨域多尺度特征融合网络TX-BiFPN改进PANet(path aggregation network),利用细节特征和跳跃连接,提升多尺度特征融合能力。在头部网络增加极小目标检测头,去掉大目标检测头,强化对浅层特征信息的利用。此外,引入了归一化高斯Wasserstein距离(normalized Wasserstein distance,NWD)损失函数削弱模型对小目标位置偏差的敏感性,提高对小目标的识别检测能力。结果表明,YOLO-iTN的平均检测精度AP50较原始YOLOv8提升1.6百分点,AP50:95提升2.0百分点,综合性能优于原始YOLOv8及其他模型。
