The feasibility of the formation of a liquid plasma catalysis system through micro arc oxidation（MAO） under AC power with titanium-aluminum alloy electrodes was investigated.In the decolorization of organic dyeing w...The feasibility of the formation of a liquid plasma catalysis system through micro arc oxidation（MAO） under AC power with titanium-aluminum alloy electrodes was investigated.In the decolorization of organic dyeing wastewater simulated with Rhodamine B,Ti-Al alloy electrodes were superior over Ti electrodes and Al electrodes.The optimal molar percentage of Ti in alloy electrodes was 70%and the optimal decolorization rate was up to 88.9%if the additive suitable for Al was added into the solution to be treated.The decolorization rates were the same in the case of the alloy-alloy electrodes and alloy-Al electrodes.The proportion of the effects of plasma,TiO2 catalyzer during MAO and H2O2 after MAO in decolorization has been obtained.With the catalysis of TiO2 formed on the electrodes,the reaction rate was improved by a maximum of 95%and the decolorization rate was improved by a maximum of 71.6%.Based on the spectral analysis,the plasma catalysis mechanism has been studied.展开更多
Wireless sensor networks (WSNs) and wireless mesh networks (WMNs) are popular research subjects. The interconnection of both network types enables next-generation applications and creates new optimization opportunitie...Wireless sensor networks (WSNs) and wireless mesh networks (WMNs) are popular research subjects. The interconnection of both network types enables next-generation applications and creates new optimization opportunities. Currently, plenty of protocols are available on the security of either wireless sensor networks or wireless mesh networks, an investigation in peer work underpins the fact that neither of these protocols is adapt to the interconnection of these network types. The internal cause relies on the fact that they differ in terms of complexity, scalability and network abstraction level. Therefore, in this article, we propose a unified security framework with three key management protocols, MPKM, MGKM, and TKM which are able to provide basic functionalities on the simplest devices and advanced functionalities on high performance nodes. We perform a detailed performance evaluation on our protocols against some important metrics such as scalability, key connectivity and compromise resilience, and we also compare our solution to the current keying protocols for WSNs and WMNs.展开更多
With the popularity of smart meters and the growing availability of high-resolution load data, the research on the dynamics of electricity consumption at finely resolved timescales has become increasingly popular. Man...With the popularity of smart meters and the growing availability of high-resolution load data, the research on the dynamics of electricity consumption at finely resolved timescales has become increasingly popular. Many existing algorithms underperform when clustering load profiles contain a large number of feature points. In addition, it is difficult to accurately describe the similarity of profile shapes when load sequences have large fluctuations, leading to inaccurate clustering results. To this end, this paper proposes a high-resolution load profile clustering approach based on dynamic largest triangle three buckets(LTTBs) and multiscale dynamic time warping under limited warping path length(LDTW). Dynamic LTTB is a novel dimensionality reduction algorithm based on LTTB. New sequences are constructed by dynamically dividing the intervals of significant feature points. The extraction of fluctuation characteristics is optimized. New curves with more concentrated features will be applied to the subsequent clustering. The proposed multiscale LDTW is used to generate a similarity matrix for spectral clustering, providing a more comprehensive and flexible matching method to characterize the similarity of load profiles. Thus, the clustering effect of a high-resolution load profile is improved. The proposed approach has been applied to multiple datasets. Experiment results demonstrate that the proposed approach significantly improves the Davies-Bouldin indicator(DBI) and validity index(VI). Therefore, better similarity and accuracy can be achieved using high-resolution load profile clustering.展开更多
PV panel modules are the core component of solar power technology and it is vital that defects are quickly detected and repaired to ensure their safe and stable operation.Traditional computer vision detection model ha...PV panel modules are the core component of solar power technology and it is vital that defects are quickly detected and repaired to ensure their safe and stable operation.Traditional computer vision detection model have problems such as low detection efficiency,many missed detections and poor robustness.To address these problems,we propose a single-stage target detection model PDTNet,which can better extract defect features and can be better deployed with a small number of parameters.Firstly,we propose a multi-scale feature extraction module and then increase the network receptive field by increasing the convolutional kernel size.Secondly,an ECA attention mechanism is added between the backbone network and the FPN layer to increase the attention of the model for the channels.Finally,by switching the network to DW convolution,the model has a smaller number of parameters,and the inference speed of the detection device is accelerated.The experimental results show that,compared to the YOLOX-Tiny model,our model achieves a 2.46%performance improvement on the PV-Multi-Defect dataset,with a detection speed of 56.7 frames per second,enabling real-time object detection with fewer parameters.In addition,the model has better robustness and shows better results on the Pascal VOC dataset.展开更多
In autonomous driving,detecting and segmenting out-of-distribution(OOD)samples is crucial.However,this becomes challenging in adverse weather.Existing OOD research mostly focuses on clear weather,leaving a gap for adv...In autonomous driving,detecting and segmenting out-of-distribution(OOD)samples is crucial.However,this becomes challenging in adverse weather.Existing OOD research mostly focuses on clear weather,leaving a gap for adverse weather OOD data.This paper presents an innovative approach:an adaptive fog parameter predictor and differentiable image dehazing module for foggy anomaly segmentation.The lightweight end-to-end network combines dehazing and anomaly segmentation,leveraging instance normalization,spatial activation functions,and dynamic convolution in dehazing.We enhance Deeplabv3+for anomaly segmentation and employ Mahalanobis distance-based Standardized Max Logits(SML)for OOD detection and segmentation.Our proposed network undergoes comprehensive comparison with other anomaly segmentation models using the OOD dataset,showcasing its advanced performance in anomaly segmentation of OOD data under foggy scenarios.展开更多
Electric load forecasting holds a pivotal role in reaching energy conservation,emission reductions,and global carbon neutrality.The urgency of accurate forecasting is escalating in light of intensifying global climate...Electric load forecasting holds a pivotal role in reaching energy conservation,emission reductions,and global carbon neutrality.The urgency of accurate forecasting is escalating in light of intensifying global climate change,acting as a linchpin for optimizing urban energy systems,minimizing energy consumption,and achieving low-carbon development.Addressing the prevalent challenges,especially the inability of current methods to effectively unearth latent load volume information resulting in diminished predictive accuracy,has become a focal point of contemporary research.This paper aims to tackle these issues by introducing a novel method that deconstructs electric load into seasonal and trend components,each forecasted through distinct models.Notably,for the seasonal components,a method incorporating both local and global information is utilized,and an innovative Expand intra-layerConvolution is introduced,facilitating effective forecasting through the use of residual blocks.When benchmarked against existing methodologies,this model demonstrates better performance in key metrics such as MAE and MSE.展开更多
基金National Natural Science Foundation of China(No.11675031) for their support of this research
文摘The feasibility of the formation of a liquid plasma catalysis system through micro arc oxidation（MAO） under AC power with titanium-aluminum alloy electrodes was investigated.In the decolorization of organic dyeing wastewater simulated with Rhodamine B,Ti-Al alloy electrodes were superior over Ti electrodes and Al electrodes.The optimal molar percentage of Ti in alloy electrodes was 70%and the optimal decolorization rate was up to 88.9%if the additive suitable for Al was added into the solution to be treated.The decolorization rates were the same in the case of the alloy-alloy electrodes and alloy-Al electrodes.The proportion of the effects of plasma,TiO2 catalyzer during MAO and H2O2 after MAO in decolorization has been obtained.With the catalysis of TiO2 formed on the electrodes,the reaction rate was improved by a maximum of 95%and the decolorization rate was improved by a maximum of 71.6%.Based on the spectral analysis,the plasma catalysis mechanism has been studied.
文摘Wireless sensor networks (WSNs) and wireless mesh networks (WMNs) are popular research subjects. The interconnection of both network types enables next-generation applications and creates new optimization opportunities. Currently, plenty of protocols are available on the security of either wireless sensor networks or wireless mesh networks, an investigation in peer work underpins the fact that neither of these protocols is adapt to the interconnection of these network types. The internal cause relies on the fact that they differ in terms of complexity, scalability and network abstraction level. Therefore, in this article, we propose a unified security framework with three key management protocols, MPKM, MGKM, and TKM which are able to provide basic functionalities on the simplest devices and advanced functionalities on high performance nodes. We perform a detailed performance evaluation on our protocols against some important metrics such as scalability, key connectivity and compromise resilience, and we also compare our solution to the current keying protocols for WSNs and WMNs.
基金supported by the Joint Fund of National Natural Science Foundation of China (No. U1936213)National Natural Science Foundation of China (No. 61872230)+1 种基金Program of Shanghai Academic Research Leader (No. 21XD1421500)Shanghai Science and Technology Commission Project (No. 20020500600)。
文摘With the popularity of smart meters and the growing availability of high-resolution load data, the research on the dynamics of electricity consumption at finely resolved timescales has become increasingly popular. Many existing algorithms underperform when clustering load profiles contain a large number of feature points. In addition, it is difficult to accurately describe the similarity of profile shapes when load sequences have large fluctuations, leading to inaccurate clustering results. To this end, this paper proposes a high-resolution load profile clustering approach based on dynamic largest triangle three buckets(LTTBs) and multiscale dynamic time warping under limited warping path length(LDTW). Dynamic LTTB is a novel dimensionality reduction algorithm based on LTTB. New sequences are constructed by dynamically dividing the intervals of significant feature points. The extraction of fluctuation characteristics is optimized. New curves with more concentrated features will be applied to the subsequent clustering. The proposed multiscale LDTW is used to generate a similarity matrix for spectral clustering, providing a more comprehensive and flexible matching method to characterize the similarity of load profiles. Thus, the clustering effect of a high-resolution load profile is improved. The proposed approach has been applied to multiple datasets. Experiment results demonstrate that the proposed approach significantly improves the Davies-Bouldin indicator(DBI) and validity index(VI). Therefore, better similarity and accuracy can be achieved using high-resolution load profile clustering.
基金supported by the National Natural Science Foundation of China under Grant No.U1936213Program of Shanghai Academic Research Leader No.21XD1421500Shanghai Science and Technology Commission Project No.20020500600.
文摘PV panel modules are the core component of solar power technology and it is vital that defects are quickly detected and repaired to ensure their safe and stable operation.Traditional computer vision detection model have problems such as low detection efficiency,many missed detections and poor robustness.To address these problems,we propose a single-stage target detection model PDTNet,which can better extract defect features and can be better deployed with a small number of parameters.Firstly,we propose a multi-scale feature extraction module and then increase the network receptive field by increasing the convolutional kernel size.Secondly,an ECA attention mechanism is added between the backbone network and the FPN layer to increase the attention of the model for the channels.Finally,by switching the network to DW convolution,the model has a smaller number of parameters,and the inference speed of the detection device is accelerated.The experimental results show that,compared to the YOLOX-Tiny model,our model achieves a 2.46%performance improvement on the PV-Multi-Defect dataset,with a detection speed of 56.7 frames per second,enabling real-time object detection with fewer parameters.In addition,the model has better robustness and shows better results on the Pascal VOC dataset.
基金supported by the National Natural Science Foundation of China under Grant No.U1936213Program of Shanghai Academic Research Leader No.21XD1421500Shanghai Science and Technology Commission Project No.20020500600.
文摘In autonomous driving,detecting and segmenting out-of-distribution(OOD)samples is crucial.However,this becomes challenging in adverse weather.Existing OOD research mostly focuses on clear weather,leaving a gap for adverse weather OOD data.This paper presents an innovative approach:an adaptive fog parameter predictor and differentiable image dehazing module for foggy anomaly segmentation.The lightweight end-to-end network combines dehazing and anomaly segmentation,leveraging instance normalization,spatial activation functions,and dynamic convolution in dehazing.We enhance Deeplabv3+for anomaly segmentation and employ Mahalanobis distance-based Standardized Max Logits(SML)for OOD detection and segmentation.Our proposed network undergoes comprehensive comparison with other anomaly segmentation models using the OOD dataset,showcasing its advanced performance in anomaly segmentation of OOD data under foggy scenarios.
文摘Electric load forecasting holds a pivotal role in reaching energy conservation,emission reductions,and global carbon neutrality.The urgency of accurate forecasting is escalating in light of intensifying global climate change,acting as a linchpin for optimizing urban energy systems,minimizing energy consumption,and achieving low-carbon development.Addressing the prevalent challenges,especially the inability of current methods to effectively unearth latent load volume information resulting in diminished predictive accuracy,has become a focal point of contemporary research.This paper aims to tackle these issues by introducing a novel method that deconstructs electric load into seasonal and trend components,each forecasted through distinct models.Notably,for the seasonal components,a method incorporating both local and global information is utilized,and an innovative Expand intra-layerConvolution is introduced,facilitating effective forecasting through the use of residual blocks.When benchmarked against existing methodologies,this model demonstrates better performance in key metrics such as MAE and MSE.
