Magnesium alloy is one of the lightest metal structural materials.The weight is further reduced through the hollow structure.However,the hollow structure is easily damaged during processing.In order to maintain the ho...Magnesium alloy is one of the lightest metal structural materials.The weight is further reduced through the hollow structure.However,the hollow structure is easily damaged during processing.In order to maintain the hollow structure and to transfer the stresses during the high temperature deformation,the sand mandrel is proposed.In this paper,the hollow AZ31 magnesium alloy three-channel joint is studied by hot extrusion forming.Sand as one of solid granule medium is used to fill the hollow magnesium alloy.The extrusion temperatures are 230℃ and 300℃,respectively.The process parameters(die angle,temperature,bottom thickness,sidewall thickness,edge-to-middle ratio in bottom,bottom shape)of the hollow magnesium alloy are analyzed based on the results of experiments and the finite element method.The results are shown that the formability of the hollow magnesium alloy will be much better when the ratio of sidewall thickness to the bottom thickness is 1:1.5.Also when edge-to-middle ratio in bottom is about 1:1.5,a better forming product can be received.The best bottom shape in these experiments will be convex based on the forming results.The grain will be refined obviously after the extrusion.Also the microstructures will be shown as streamlines.And these lines will be well agreement with the mold in the corner.展开更多
For time-of-flight(TOF)light detection and ranging(LiDAR),a three-channel high-performance transimpedance amplifier(TIA)with high immunity to input load capacitance is presented.A regulated cascade(RGC)as the input st...For time-of-flight(TOF)light detection and ranging(LiDAR),a three-channel high-performance transimpedance amplifier(TIA)with high immunity to input load capacitance is presented.A regulated cascade(RGC)as the input stage is at the core of the complementary metal oxide semiconductor(CMOS)circuit chip,giving it more immunity to input photodiode detectors.A simple smart output interface acting as a feedback structure,which is rarely found in other designs,reduces the chip size and power consumption simultaneously.The circuit is designed using a 0.5μm CMOS process technology to achieve low cost.The device delivers a 33.87 dB?transimpedance gain at 350 MHz.With a higher input load capacitance,it shows a-3 dB bandwidth of 461 MHz,indicating a better detector tolerance at the front end of the system.Under a 3.3 V supply voltage,the device consumes 5.2 mW,and the total chip area with three channels is 402.8×597.0μm2(including the test pads).展开更多
In the era of the Internet,widely used web applications have become the target of hacker attacks because they contain a large amount of personal information.Among these vulnerabilities,stealing private data through cr...In the era of the Internet,widely used web applications have become the target of hacker attacks because they contain a large amount of personal information.Among these vulnerabilities,stealing private data through crosssite scripting(XSS)attacks is one of the most commonly used attacks by hackers.Currently,deep learning-based XSS attack detection methods have good application prospects;however,they suffer from problems such as being prone to overfitting,a high false alarm rate,and low accuracy.To address these issues,we propose a multi-stage feature extraction and fusion model for XSS detection based on Random Forest feature enhancement.The model utilizes RandomForests to capture the intrinsic structure and patterns of the data by extracting leaf node indices as features,which are subsequentlymergedwith the original data features to forma feature setwith richer information content.Further feature extraction is conducted through three parallel channels.Channel I utilizes parallel onedimensional convolutional layers(1Dconvolutional layers)with different convolutional kernel sizes to extract local features at different scales and performmulti-scale feature fusion;Channel II employsmaximum one-dimensional pooling layers(max 1D pooling layers)of various sizes to extract key features from the data;and Channel III extracts global information bi-directionally using a Bi-Directional Long-Short TermMemory Network(Bi-LSTM)and incorporates a multi-head attention mechanism to enhance global features.Finally,effective classification and prediction of XSS are performed by fusing the features of the three channels.To test the effectiveness of the model,we conduct experiments on six datasets.We achieve an accuracy of 100%on the UNSW-NB15 dataset and 99.99%on the CICIDS2017 dataset,which is higher than that of the existing models.展开更多
为了改善基于卷积编解码架构的单通道语音增强网络对语音声学特征提取不充分、解码特征丢失严重的问题,提出一种基于多路信息聚合协同解码的单通道语音增强网络MIACD,通过双路编码器充分提取融入了语音自监督学习(SSL)表征的幅度谱和复...为了改善基于卷积编解码架构的单通道语音增强网络对语音声学特征提取不充分、解码特征丢失严重的问题,提出一种基于多路信息聚合协同解码的单通道语音增强网络MIACD,通过双路编码器充分提取融入了语音自监督学习(SSL)表征的幅度谱和复数谱特征,由4层Conformer分别从时间和频率维度对提取特征建模,采用残差连接将双路编码器提取的语音幅度、复数特征引入三路信息聚合解码器,并利用所提通道-时频注意力(CTF-Attention)机制根据语音能量分布情况调节解码器中聚合信息,有效缓解解码时可用声学信息缺失严重的问题。在公开数据集Voice Bank DEMAND上的实验结果表明,与用于单通道语音增强的协作学习框架(GaGNet)相比,MIACD在客观评价指标宽带感知评估语音质量(WB-PESQ)上提升了5.1%,短时客观可懂度(STOI)达到96.7%,验证所提方法可充分利用语音信息重构信号,有效抑制噪声并提升语音可理解性。展开更多
基金National Natural Science Foundation of China No.51905068Natural Science Foundation of Liaoning Province No.2020-HYLH-24The open research fund from the State Key Laboratory of Rolling and Automation,Northeastern University No.2020RALKFKT012。
文摘Magnesium alloy is one of the lightest metal structural materials.The weight is further reduced through the hollow structure.However,the hollow structure is easily damaged during processing.In order to maintain the hollow structure and to transfer the stresses during the high temperature deformation,the sand mandrel is proposed.In this paper,the hollow AZ31 magnesium alloy three-channel joint is studied by hot extrusion forming.Sand as one of solid granule medium is used to fill the hollow magnesium alloy.The extrusion temperatures are 230℃ and 300℃,respectively.The process parameters(die angle,temperature,bottom thickness,sidewall thickness,edge-to-middle ratio in bottom,bottom shape)of the hollow magnesium alloy are analyzed based on the results of experiments and the finite element method.The results are shown that the formability of the hollow magnesium alloy will be much better when the ratio of sidewall thickness to the bottom thickness is 1:1.5.Also when edge-to-middle ratio in bottom is about 1:1.5,a better forming product can be received.The best bottom shape in these experiments will be convex based on the forming results.The grain will be refined obviously after the extrusion.Also the microstructures will be shown as streamlines.And these lines will be well agreement with the mold in the corner.
文摘For time-of-flight(TOF)light detection and ranging(LiDAR),a three-channel high-performance transimpedance amplifier(TIA)with high immunity to input load capacitance is presented.A regulated cascade(RGC)as the input stage is at the core of the complementary metal oxide semiconductor(CMOS)circuit chip,giving it more immunity to input photodiode detectors.A simple smart output interface acting as a feedback structure,which is rarely found in other designs,reduces the chip size and power consumption simultaneously.The circuit is designed using a 0.5μm CMOS process technology to achieve low cost.The device delivers a 33.87 dB?transimpedance gain at 350 MHz.With a higher input load capacitance,it shows a-3 dB bandwidth of 461 MHz,indicating a better detector tolerance at the front end of the system.Under a 3.3 V supply voltage,the device consumes 5.2 mW,and the total chip area with three channels is 402.8×597.0μm2(including the test pads).
文摘In the era of the Internet,widely used web applications have become the target of hacker attacks because they contain a large amount of personal information.Among these vulnerabilities,stealing private data through crosssite scripting(XSS)attacks is one of the most commonly used attacks by hackers.Currently,deep learning-based XSS attack detection methods have good application prospects;however,they suffer from problems such as being prone to overfitting,a high false alarm rate,and low accuracy.To address these issues,we propose a multi-stage feature extraction and fusion model for XSS detection based on Random Forest feature enhancement.The model utilizes RandomForests to capture the intrinsic structure and patterns of the data by extracting leaf node indices as features,which are subsequentlymergedwith the original data features to forma feature setwith richer information content.Further feature extraction is conducted through three parallel channels.Channel I utilizes parallel onedimensional convolutional layers(1Dconvolutional layers)with different convolutional kernel sizes to extract local features at different scales and performmulti-scale feature fusion;Channel II employsmaximum one-dimensional pooling layers(max 1D pooling layers)of various sizes to extract key features from the data;and Channel III extracts global information bi-directionally using a Bi-Directional Long-Short TermMemory Network(Bi-LSTM)and incorporates a multi-head attention mechanism to enhance global features.Finally,effective classification and prediction of XSS are performed by fusing the features of the three channels.To test the effectiveness of the model,we conduct experiments on six datasets.We achieve an accuracy of 100%on the UNSW-NB15 dataset and 99.99%on the CICIDS2017 dataset,which is higher than that of the existing models.
文摘为了改善基于卷积编解码架构的单通道语音增强网络对语音声学特征提取不充分、解码特征丢失严重的问题,提出一种基于多路信息聚合协同解码的单通道语音增强网络MIACD,通过双路编码器充分提取融入了语音自监督学习(SSL)表征的幅度谱和复数谱特征,由4层Conformer分别从时间和频率维度对提取特征建模,采用残差连接将双路编码器提取的语音幅度、复数特征引入三路信息聚合解码器,并利用所提通道-时频注意力(CTF-Attention)机制根据语音能量分布情况调节解码器中聚合信息,有效缓解解码时可用声学信息缺失严重的问题。在公开数据集Voice Bank DEMAND上的实验结果表明,与用于单通道语音增强的协作学习框架(GaGNet)相比,MIACD在客观评价指标宽带感知评估语音质量(WB-PESQ)上提升了5.1%,短时客观可懂度(STOI)达到96.7%,验证所提方法可充分利用语音信息重构信号,有效抑制噪声并提升语音可理解性。
