This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structu...This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structure,a double-layer Ag-Au metal film is combined with a blue phosphorene/transition metal dichalcogenide(BlueP/TMDC)hybrid structure and graphene.In the optimization function of the IABC method,the reflectivity at resonance angle is incorporated as a constraint to achieve high phase sensitivity.The performance of the Ag-Au-BlueP/TMDC-graphene heterostructure as optimized by the IABC method is compared with that of a similar structure optimized using the traditional ABC algorithm.The results indicate that optimization using the IABC method gives significantly more phase sensitivity,together with lower reflectivity,than can be achieved with the traditional ABC method.The highest phase sensitivity of 3.662×10^(6) °/RIU is achieved with a bilayer of BlueP/WS2 and three layers of graphene.Moreover,analysis of the electric field distribution demonstrates that the optimal arrangement can be utilized for enhanced detection of small biomolecules.Thus,given the exceptional sensitivity achieved,the proposed method based on the IABC algorithm has great promise for use in the design of high-performance SPR biosensors with a variety of multilayer structures.展开更多
In this study,we propose Space-to-Depth and You Only Look Once Version 7(SPD-YOLOv7),an accurate and efficient method for detecting pests inmaize crops,addressing challenges such as small pest sizes,blurred images,low...In this study,we propose Space-to-Depth and You Only Look Once Version 7(SPD-YOLOv7),an accurate and efficient method for detecting pests inmaize crops,addressing challenges such as small pest sizes,blurred images,low resolution,and significant species variation across different growth stages.To improve the model’s ability to generalize and its robustness,we incorporate target background analysis,data augmentation,and processing techniques like Gaussian noise and brightness adjustment.In target detection,increasing the depth of the neural network can lead to the loss of small target information.To overcome this,we introduce the Space-to-Depth Convolution(SPD-Conv)module into the SPD-YOLOv7 framework,replacing certain convolutional layers in the traditional system backbone and head network.This modification helps retain small target features and location information.Additionally,the Efficient Layer Aggregation Network-Wide(ELAN-W)module is combined with the Convolutional Block Attention Module(CBAM)attention mechanism to extract more efficient features.Experimental results show that the enhanced YOLOv7 model achieves an accuracy of 98.38%,with an average accuracy of 99.4%,outperforming the original YOLOv7 model.These improvements represent an increase of 2.46%in accuracy and 3.19%in average accuracy.The results indicate that the enhanced YOLOv7 model is more efficient and real-time,offering valuable insights for maize pest control.展开更多
Aiming at the problem of music noise introduced by classical spectral subtraction,a shorttime modulation domain(STM)spectral subtraction method has been successfully applied for singlechannel speech enhancement.Howeve...Aiming at the problem of music noise introduced by classical spectral subtraction,a shorttime modulation domain(STM)spectral subtraction method has been successfully applied for singlechannel speech enhancement.However,due to the inaccurate voice activity detection(VAD),the residual music noise and enhanced performance still need to be further improved,especially in the low signal to noise ratio(SNR)scenarios.To address this issue,an improved frame iterative spectral subtraction in the STM domain(IMModSSub)is proposed.More specifically,with the inter-frame correlation,the noise subtraction is directly applied to handle the noisy signal for each frame in the STM domain.Then,the noisy signal is classified into speech or silence frames based on a predefined threshold of segmented SNR.With these classification results,a corresponding mask function is developed for noisy speech after noise subtraction.Finally,exploiting the increased sparsity of speech signal in the modulation domain,the orthogonal matching pursuit(OMP)technique is employed to the speech frames for improving the speech quality and intelligibility.The effectiveness of the proposed method is evaluated with three types of noise,including white noise,pink noise,and hfchannel noise.The obtained results show that the proposed method outperforms some established baselines at lower SNRs(-5 to +5 dB).展开更多
An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal ...An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.展开更多
针对菌落图像中小菌落易漏检的问题,提出了一种基于INC4-YOLO(you only look once)的计数方法,实现精准的菌落计数。采用带残差结构的Inception模块(Inception module with residual connection,IncRes)替换YOLOv5骨干网络中的Bottlenec...针对菌落图像中小菌落易漏检的问题,提出了一种基于INC4-YOLO(you only look once)的计数方法,实现精准的菌落计数。采用带残差结构的Inception模块(Inception module with residual connection,IncRes)替换YOLOv5骨干网络中的Bottleneck模块,以增强图像特征提取能力。从网络的浅层特征中引出一个小目标检测头,以增强算法在训练过程中对小菌落的注意力。分别在标注微生物自动识别数据集(annotated germs for automated recognition,AGAR)和真实菌落计数场景下对INC4-YOLO进行计数性能测试。实验结果表明,在AGAR测试集中,提出的算法在小菌落的平均百分比绝对值计数误差(mean absolute percentage error,MAPE)比其他先进目标检测算法降低了2%;真实菌落计数场景下,INC4-YOLO的MAPE相比YOLOv5降低了7%,表明该算法可帮助菌落计数设备实现精准计数。展开更多
为有效降低退役动力电池梯次利用寿命损耗,提高电池模组运行过程中功能状态的一致性,本工作提出了考虑电池能量状态(state of energy,SOE)、健康状态(state of health,SOH)及功率状态(state of power,SOP)的多状态耦合的电池模组分选方...为有效降低退役动力电池梯次利用寿命损耗,提高电池模组运行过程中功能状态的一致性,本工作提出了考虑电池能量状态(state of energy,SOE)、健康状态(state of health,SOH)及功率状态(state of power,SOP)的多状态耦合的电池模组分选方法。首先,提取退役动力电池基本电性能参数(如容量、电压、内阻等),建立了电池模组SOE、SOH及SOP多状态耦合表征模型;其次,估计电池模组SOE特性,预测梯次利用电池模组SOH一致性,采用改进K-means聚类算法对电池模组进行了第一阶段的动态分选。最后,建立了电池模组耦合多参量的SOP表征模型,估计了电池模组中电池之间SOP偏差,对电池模组进行了第二阶段的动态分选。通过仿真算例分析验证该方法有效提高了退役动力电池梯次利用过程中模组的一致性,降低了系统运行的寿命损耗,为梯次利用储能大规模应用提供理论基础。展开更多
针对设备背景噪声影响机械故障检测的问题,提出一种融合自适应噪声完全集成局部均值分解(Complete Ensemble Local Mean Decomposition with Adaptive Noise,CELMDAN)与改进的多点最优最小熵去卷积调整(Improved Multipoint Optimal Min...针对设备背景噪声影响机械故障检测的问题,提出一种融合自适应噪声完全集成局部均值分解(Complete Ensemble Local Mean Decomposition with Adaptive Noise,CELMDAN)与改进的多点最优最小熵去卷积调整(Improved Multipoint Optimal Minimum Entropy Deconvolution Adjusted,IMOMEDA)的微弱机械特征增强方法。该方法首先利用CELMDAN方法把复杂振动信号分解为多个单模态的乘积函数(Product Functions,PFs),解决了集成局部均值分解(Ensemble Local Mean Decomposition,ELMD)对信号施加噪声幅值和试错次数难以确定的问题。其次,提出一种具有鲁棒性较强、物理意义明确以及尺度不变性的周期调制强度(Periodic Modulation Intensity,PMI),以筛选出有效的PFs。接着,针对所选PFs中的噪声,提出IMOMEDA方法进行消除,该方法通过迭代估计最优模型参数,自适应地提取振动信号中的周期性故障瞬态特征,能够在频域中定位瞬态的谱峭度,从而抽取被背景噪声淹没的微弱故障特征。最后,以煤矿提升机为研究对象,设计了多种振动信号特征增强方法对比实验、机械运行状态诊断性能实验以及信号特征增强算法性能对比实验,多角度验证了本文方法的有效性。展开更多
基金funded by the National Natural Science Foundation of China(Grant No.52375547)the Natural Science Foundation of Chongqing,China(Grant Nos.CSTB2022NSCQ-BHX0736 and CSTB2022NSCQ-MSX1523)the Chongqing Scientific Institution Incentive Performance Guiding Special Projects(Grant No.CSTB2024JXJL-YFX0034).
文摘This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structure,a double-layer Ag-Au metal film is combined with a blue phosphorene/transition metal dichalcogenide(BlueP/TMDC)hybrid structure and graphene.In the optimization function of the IABC method,the reflectivity at resonance angle is incorporated as a constraint to achieve high phase sensitivity.The performance of the Ag-Au-BlueP/TMDC-graphene heterostructure as optimized by the IABC method is compared with that of a similar structure optimized using the traditional ABC algorithm.The results indicate that optimization using the IABC method gives significantly more phase sensitivity,together with lower reflectivity,than can be achieved with the traditional ABC method.The highest phase sensitivity of 3.662×10^(6) °/RIU is achieved with a bilayer of BlueP/WS2 and three layers of graphene.Moreover,analysis of the electric field distribution demonstrates that the optimal arrangement can be utilized for enhanced detection of small biomolecules.Thus,given the exceptional sensitivity achieved,the proposed method based on the IABC algorithm has great promise for use in the design of high-performance SPR biosensors with a variety of multilayer structures.
文摘In this study,we propose Space-to-Depth and You Only Look Once Version 7(SPD-YOLOv7),an accurate and efficient method for detecting pests inmaize crops,addressing challenges such as small pest sizes,blurred images,low resolution,and significant species variation across different growth stages.To improve the model’s ability to generalize and its robustness,we incorporate target background analysis,data augmentation,and processing techniques like Gaussian noise and brightness adjustment.In target detection,increasing the depth of the neural network can lead to the loss of small target information.To overcome this,we introduce the Space-to-Depth Convolution(SPD-Conv)module into the SPD-YOLOv7 framework,replacing certain convolutional layers in the traditional system backbone and head network.This modification helps retain small target features and location information.Additionally,the Efficient Layer Aggregation Network-Wide(ELAN-W)module is combined with the Convolutional Block Attention Module(CBAM)attention mechanism to extract more efficient features.Experimental results show that the enhanced YOLOv7 model achieves an accuracy of 98.38%,with an average accuracy of 99.4%,outperforming the original YOLOv7 model.These improvements represent an increase of 2.46%in accuracy and 3.19%in average accuracy.The results indicate that the enhanced YOLOv7 model is more efficient and real-time,offering valuable insights for maize pest control.
基金National Natural Science Foundation of China(NSFC)(No.61671075)Major Program of National Natural Science Foundation of China(No.61631003)。
文摘Aiming at the problem of music noise introduced by classical spectral subtraction,a shorttime modulation domain(STM)spectral subtraction method has been successfully applied for singlechannel speech enhancement.However,due to the inaccurate voice activity detection(VAD),the residual music noise and enhanced performance still need to be further improved,especially in the low signal to noise ratio(SNR)scenarios.To address this issue,an improved frame iterative spectral subtraction in the STM domain(IMModSSub)is proposed.More specifically,with the inter-frame correlation,the noise subtraction is directly applied to handle the noisy signal for each frame in the STM domain.Then,the noisy signal is classified into speech or silence frames based on a predefined threshold of segmented SNR.With these classification results,a corresponding mask function is developed for noisy speech after noise subtraction.Finally,exploiting the increased sparsity of speech signal in the modulation domain,the orthogonal matching pursuit(OMP)technique is employed to the speech frames for improving the speech quality and intelligibility.The effectiveness of the proposed method is evaluated with three types of noise,including white noise,pink noise,and hfchannel noise.The obtained results show that the proposed method outperforms some established baselines at lower SNRs(-5 to +5 dB).
基金Supported by the National Natural Science Foundation of China(51175262)the Research Fund for Doctoral Program of Higher Education of China(20093218110020)+2 种基金the Jiangsu Province Science Foundation for Excellent Youths(BK201210111)the Jiangsu Province Industry-Academy-Research Grant(BY201220116)the Innovative and Excellent Foundation for Doctoral Dissertation of Nanjing University of Aeronautics and Astronautics(BCXJ10-09)
文摘An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.
文摘针对菌落图像中小菌落易漏检的问题,提出了一种基于INC4-YOLO(you only look once)的计数方法,实现精准的菌落计数。采用带残差结构的Inception模块(Inception module with residual connection,IncRes)替换YOLOv5骨干网络中的Bottleneck模块,以增强图像特征提取能力。从网络的浅层特征中引出一个小目标检测头,以增强算法在训练过程中对小菌落的注意力。分别在标注微生物自动识别数据集(annotated germs for automated recognition,AGAR)和真实菌落计数场景下对INC4-YOLO进行计数性能测试。实验结果表明,在AGAR测试集中,提出的算法在小菌落的平均百分比绝对值计数误差(mean absolute percentage error,MAPE)比其他先进目标检测算法降低了2%;真实菌落计数场景下,INC4-YOLO的MAPE相比YOLOv5降低了7%,表明该算法可帮助菌落计数设备实现精准计数。
文摘为有效降低退役动力电池梯次利用寿命损耗,提高电池模组运行过程中功能状态的一致性,本工作提出了考虑电池能量状态(state of energy,SOE)、健康状态(state of health,SOH)及功率状态(state of power,SOP)的多状态耦合的电池模组分选方法。首先,提取退役动力电池基本电性能参数(如容量、电压、内阻等),建立了电池模组SOE、SOH及SOP多状态耦合表征模型;其次,估计电池模组SOE特性,预测梯次利用电池模组SOH一致性,采用改进K-means聚类算法对电池模组进行了第一阶段的动态分选。最后,建立了电池模组耦合多参量的SOP表征模型,估计了电池模组中电池之间SOP偏差,对电池模组进行了第二阶段的动态分选。通过仿真算例分析验证该方法有效提高了退役动力电池梯次利用过程中模组的一致性,降低了系统运行的寿命损耗,为梯次利用储能大规模应用提供理论基础。
文摘针对设备背景噪声影响机械故障检测的问题,提出一种融合自适应噪声完全集成局部均值分解(Complete Ensemble Local Mean Decomposition with Adaptive Noise,CELMDAN)与改进的多点最优最小熵去卷积调整(Improved Multipoint Optimal Minimum Entropy Deconvolution Adjusted,IMOMEDA)的微弱机械特征增强方法。该方法首先利用CELMDAN方法把复杂振动信号分解为多个单模态的乘积函数(Product Functions,PFs),解决了集成局部均值分解(Ensemble Local Mean Decomposition,ELMD)对信号施加噪声幅值和试错次数难以确定的问题。其次,提出一种具有鲁棒性较强、物理意义明确以及尺度不变性的周期调制强度(Periodic Modulation Intensity,PMI),以筛选出有效的PFs。接着,针对所选PFs中的噪声,提出IMOMEDA方法进行消除,该方法通过迭代估计最优模型参数,自适应地提取振动信号中的周期性故障瞬态特征,能够在频域中定位瞬态的谱峭度,从而抽取被背景噪声淹没的微弱故障特征。最后,以煤矿提升机为研究对象,设计了多种振动信号特征增强方法对比实验、机械运行状态诊断性能实验以及信号特征增强算法性能对比实验,多角度验证了本文方法的有效性。
