The Co_(3)O_(4)nanoparticles,dominated by a catalytically active(110)lattice plane,were synthesized as a low-temperature NO_(x) adsorbent to control the cold start emissions from vehicles.These nanoparticles boast a s...The Co_(3)O_(4)nanoparticles,dominated by a catalytically active(110)lattice plane,were synthesized as a low-temperature NO_(x) adsorbent to control the cold start emissions from vehicles.These nanoparticles boast a substantial quantity of active chemisorbed oxygen and lattice oxygen,which exhibited a NO_(x) uptake capacity commensurate with Pd/SSZ-13 at 100℃.The primary NO_(x) release temperature falls within a temperature range of 200-350℃,making it perfectly suitable for diesel engines.The characterization results demonstrate that chemisorbed oxygen facilitate nitro/nitrites intermediates formation,contributing to the NO_(x) storage at 100℃,while the nitrites begin to decompose within the 150-200℃range.Fortunately,lattice oxygen likely becomes involved in the activation of nitrites into more stable nitrate within this particular temperature range.The concurrent processes of nitrites decomposition and its conversion to nitrates results in a minimal NO_(x) release between the temperatures of 150-200℃.The nitrate formed via lattice oxygen mainly induces the NO_(x) to be released as NO_(2) within a temperature range of 200-350℃,which is advantageous in enhancing the NO_(x) activity of downstream NH_(3)-SCR catalysts,by boosting the fast SCR reaction pathway.Thanks to its low cost,considerable NO_(x) absorption capacity,and optimal release temperature,Co_(3)O_(4)demonstrates potential as an effective material for passive NO_(x) adsorber applications.展开更多
Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in futu...Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks.Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connectedgraph. However, the complexity of the real world makes the complex networksabstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start linkprediction is favored as one of the most valuable subproblems of traditional linkprediction. However, due to the loss of many links in the observation network, thetopological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topologicalinformation from observed network becomes the key point to solve the problemof cold-start link prediction. In this paper, we propose a framework for solving thecold-start link prediction problem, a joint-weighted symmetric nonnegative matrixfactorization model fusing graph regularization information, based on low-rankapproximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designedgraph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain eachother. Finally, a unified framework for implementing cold-start link prediction isconstructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validationon five real networks with attributes shows that the proposed model has very goodpredictive performance when predicting missing edges of isolated nodes.展开更多
In order to improve the cold start performance of heavy duty diesel engine, electronically controlling the preheating of intake air by flame was researched. According to simulation and thermodynamic analysis about th...In order to improve the cold start performance of heavy duty diesel engine, electronically controlling the preheating of intake air by flame was researched. According to simulation and thermodynamic analysis about the partial working processes of the diesel engine, the amount of heat energy, enough to make the fuel self ignite at the end of compression process at different temperatures of coolant and intake air, was calculated. Several HY20 preheating plugs were used to heat up the intake air. Meanwhile, an electronic control system based on 8 bit micro controller unit (MCS 8031) was designed to automatically control the process of heating intake air. According to the various temperatures of coolant and ambient air, one plug or two plugs can automatically be selected to heat intake air. The demo experiment validated that the total system could operate successfully and achieve the scheduled function.展开更多
近年来,社会化推荐成为了推荐领域的研究热点。在基于用户历史行为的推荐算法中引入用户的社交关系,能够缓解推荐系统面临的数据稀疏性和冷启动的问题。本文提出了一种基于相对信任增强的推荐算法(relative trust enhancement recommend...近年来,社会化推荐成为了推荐领域的研究热点。在基于用户历史行为的推荐算法中引入用户的社交关系,能够缓解推荐系统面临的数据稀疏性和冷启动的问题。本文提出了一种基于相对信任增强的推荐算法(relative trust enhancement recommendation algorithm based on the CosRA,RTECosRA)。该算法在“用户-物品”的二部图网络中,基于CosRA相似性指标进行资源分配,在资源分配过程中引入用户的信任关系,调整受信任用户获得的资源值,从而提高受信任用户所选物品的推荐率。在FriendFeed和Epinions数据集上的实验结果显示,相比于基准算法,RTECosRA算法在准确性和多样性上均有提高,且加入信任关系后,扩大了用户的可推荐范围,一定程度上缓解了冷启动问题。展开更多
在容器技术和微服务框架的普及背景下,无服务器计算为开发者提供了一种无需关注服务器操作以及硬件资源管理的云计算范式.与此同时,无服务器计算通过弹性扩缩容实时地适应动态负载变化,能够有效降低请求响应延时并且减少服务成本,满足...在容器技术和微服务框架的普及背景下,无服务器计算为开发者提供了一种无需关注服务器操作以及硬件资源管理的云计算范式.与此同时,无服务器计算通过弹性扩缩容实时地适应动态负载变化,能够有效降低请求响应延时并且减少服务成本,满足了客户对于云服务成本按需付费的需求.然而,无服务器计算中面临着弹性扩缩容需求导致的冷启动延迟问题.提前预热函数实例能够有效地降低冷启动发生频率和延时.然而,在云环境中流量突发问题极大地增加了预测预热函数实例数的难度.针对上述挑战,提出了一种基于概率分布的弹性伸缩算法(probability distribution based auto-scaling algorithm,PDBAA),利用监控指标历史数据预测未来请求的概率分布,以最小化请求响应延时为目的计算预热函数实例的最佳数量,并且PDBAA能够有效地结合深度学习技术的强大预测功能进一步提升性能.在Knative框架中,通过NASA和WSAL数据集对算法进行了验证,仿真实验表明,相比于Knative弹性伸缩算法以及其他预测算法,所提出的算法弹性性能提升了31%以上,平均响应时间降低了16%以上,能够更好地解决流量突发问题,有效地降低了无服务器计算请求的响应延时.展开更多
The problem of recommending new items to users(often referred to as item cold-start recommendation)remains a challenge due to the absence of users’past preferences for these items.Item features from side information ...The problem of recommending new items to users(often referred to as item cold-start recommendation)remains a challenge due to the absence of users’past preferences for these items.Item features from side information are typically leveraged to tackle the problem.Existing methods formulate regression methods,taking item features as input and user ratings as output.These methods are confronted with the issue of overfitting when item features are high-dimensional,which greatly impedes the recommendation experience.Availing of high-dimensional item features,in this work,we opt for feature selection to solve the problem of recommending top-N new items.Existing feature selection methods find a common set of features for all users,which fails to differentiate users1 preferences over item features.To personalize feature selection,we propose to select item features discriminately for different users.We study the personalization of feature selection at the level of the user or user group.We fulfill the task by proposing two embedded feature selection models.The process of personalized feature selection filters out the dimensions that are irrelevant to recommendations or unappealing to users.Experimental results on real-life datasets with high-dimensional side information reveal that the proposed method is effective in singling out features that are crucial to top-N recommendation and hence improving performance.展开更多
The Self-adaptive control of the temperature can achieve the start of fuel cell at different operating temperatures, which is very important for the successful cold-start of the air-cooled PEMFC. The temperature distr...The Self-adaptive control of the temperature can achieve the start of fuel cell at different operating temperatures, which is very important for the successful cold-start of the air-cooled PEMFC. The temperature distribution characteristics during the cold-start process were analyzed based on adaptive temperature recognition control in this paper. Preheating model and cold-start model were established and the optimal balance between the hot air flow rate and the temperature required to promote a uniform temperature distribution in the stack was explored in the preheating stage. Finally, the non-equilibrium mass transfer, as well as the temperature rise in the catalyst layer and gas diffusion layer with different current densities, were analyzed in the start-up stage. The results indicate that the air-cooled PEMFC stack can be successfully started up at -40 ◦C within 10 min by means of external gas heating. The current density and air velocity have significant impacts on the temperature of aircooled PEMFC stack. Dynamic analysis of air-cooled PEMFCs and real-time monitoring are suitable for machine learning and self-adaptive control to set the operation parameters to achieve successful cold start. Optimize the matching of load current and cathode inlet speed to achieve thermal management in low temperature environment.展开更多
Carbon monoxide(CO) is a poisonous gas particularly to all leaving being present in the atmosphere.An estimate has shown that the vehicular exhaust contributes the largest source of CO pollution in developed countries...Carbon monoxide(CO) is a poisonous gas particularly to all leaving being present in the atmosphere.An estimate has shown that the vehicular exhaust contributes the largest source of CO pollution in developed countries.Due to the exponentially increasing number of automobile vehicles on roads,CO concentrations have reached an alarming level in urban areas.To control this vehicular exhaust pollution,the end-of-pipe-technology using catalytic converters is recommended.The catalysts operating efficiently in a catalytic converter are a challenging class of materials for applications in cold start of engines to maintain indoor air quality.In the cold start period,the catalytic converter was entirely inactive,because the catalytic converter had not been warmed up.The cold start phase is also depending upon the characteristics of vehicles and property of catalysts.The increasing cost of noble metals with the increasing number of vehicles motivates the investigation of material concepts to reduce the precious metal content in automotive catalysts or to find a substitute for noble metals.Hopcalite(CuMnOx) catalyst could work very well at the low temperature;thus,it can overcome the problem of cold-start emissions if used in a catalytic converter.Further,low cost,easy availability and advanced synthesis methods with stabilizer,promoter,etc.,advocates for the use of hopcalite as an auto exhaust purification catalyst.Although there are numerous research articles present on this topic until now,no review has been presented for demanding this issue.So there is a space in this area,and it has been made an attempt to seal this hole and progress the future scope for hopcalite catalyst for purification of exhaust gases by this review.展开更多
基金supported by the National Natural Science Foundation of China(22006044,22006043)External Cooperation Program of Science and Technology Planning of Fujian Province(2023I0018)+2 种基金the Fujian Province Science and Technology Program Funds(2020H6013)the National Engineering Laboratory for Mobile Source Emission Control Technology(NELMS2020A03)the Scientific Research Funds of Huaqiao University(605-50Y200270001)。
文摘The Co_(3)O_(4)nanoparticles,dominated by a catalytically active(110)lattice plane,were synthesized as a low-temperature NO_(x) adsorbent to control the cold start emissions from vehicles.These nanoparticles boast a substantial quantity of active chemisorbed oxygen and lattice oxygen,which exhibited a NO_(x) uptake capacity commensurate with Pd/SSZ-13 at 100℃.The primary NO_(x) release temperature falls within a temperature range of 200-350℃,making it perfectly suitable for diesel engines.The characterization results demonstrate that chemisorbed oxygen facilitate nitro/nitrites intermediates formation,contributing to the NO_(x) storage at 100℃,while the nitrites begin to decompose within the 150-200℃range.Fortunately,lattice oxygen likely becomes involved in the activation of nitrites into more stable nitrate within this particular temperature range.The concurrent processes of nitrites decomposition and its conversion to nitrates results in a minimal NO_(x) release between the temperatures of 150-200℃.The nitrate formed via lattice oxygen mainly induces the NO_(x) to be released as NO_(2) within a temperature range of 200-350℃,which is advantageous in enhancing the NO_(x) activity of downstream NH_(3)-SCR catalysts,by boosting the fast SCR reaction pathway.Thanks to its low cost,considerable NO_(x) absorption capacity,and optimal release temperature,Co_(3)O_(4)demonstrates potential as an effective material for passive NO_(x) adsorber applications.
基金supported by the Teaching Reform Research Project of Qinghai Minzu University,China(2021-JYYB-009)the“Chunhui Plan”Cooperative Scientific Research Project of the Ministry of Education of China(2018).
文摘Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks.Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connectedgraph. However, the complexity of the real world makes the complex networksabstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start linkprediction is favored as one of the most valuable subproblems of traditional linkprediction. However, due to the loss of many links in the observation network, thetopological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topologicalinformation from observed network becomes the key point to solve the problemof cold-start link prediction. In this paper, we propose a framework for solving thecold-start link prediction problem, a joint-weighted symmetric nonnegative matrixfactorization model fusing graph regularization information, based on low-rankapproximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designedgraph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain eachother. Finally, a unified framework for implementing cold-start link prediction isconstructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validationon five real networks with attributes shows that the proposed model has very goodpredictive performance when predicting missing edges of isolated nodes.
文摘In order to improve the cold start performance of heavy duty diesel engine, electronically controlling the preheating of intake air by flame was researched. According to simulation and thermodynamic analysis about the partial working processes of the diesel engine, the amount of heat energy, enough to make the fuel self ignite at the end of compression process at different temperatures of coolant and intake air, was calculated. Several HY20 preheating plugs were used to heat up the intake air. Meanwhile, an electronic control system based on 8 bit micro controller unit (MCS 8031) was designed to automatically control the process of heating intake air. According to the various temperatures of coolant and ambient air, one plug or two plugs can automatically be selected to heat intake air. The demo experiment validated that the total system could operate successfully and achieve the scheduled function.
文摘近年来,社会化推荐成为了推荐领域的研究热点。在基于用户历史行为的推荐算法中引入用户的社交关系,能够缓解推荐系统面临的数据稀疏性和冷启动的问题。本文提出了一种基于相对信任增强的推荐算法(relative trust enhancement recommendation algorithm based on the CosRA,RTECosRA)。该算法在“用户-物品”的二部图网络中,基于CosRA相似性指标进行资源分配,在资源分配过程中引入用户的信任关系,调整受信任用户获得的资源值,从而提高受信任用户所选物品的推荐率。在FriendFeed和Epinions数据集上的实验结果显示,相比于基准算法,RTECosRA算法在准确性和多样性上均有提高,且加入信任关系后,扩大了用户的可推荐范围,一定程度上缓解了冷启动问题。
文摘在容器技术和微服务框架的普及背景下,无服务器计算为开发者提供了一种无需关注服务器操作以及硬件资源管理的云计算范式.与此同时,无服务器计算通过弹性扩缩容实时地适应动态负载变化,能够有效降低请求响应延时并且减少服务成本,满足了客户对于云服务成本按需付费的需求.然而,无服务器计算中面临着弹性扩缩容需求导致的冷启动延迟问题.提前预热函数实例能够有效地降低冷启动发生频率和延时.然而,在云环境中流量突发问题极大地增加了预测预热函数实例数的难度.针对上述挑战,提出了一种基于概率分布的弹性伸缩算法(probability distribution based auto-scaling algorithm,PDBAA),利用监控指标历史数据预测未来请求的概率分布,以最小化请求响应延时为目的计算预热函数实例的最佳数量,并且PDBAA能够有效地结合深度学习技术的强大预测功能进一步提升性能.在Knative框架中,通过NASA和WSAL数据集对算法进行了验证,仿真实验表明,相比于Knative弹性伸缩算法以及其他预测算法,所提出的算法弹性性能提升了31%以上,平均响应时间降低了16%以上,能够更好地解决流量突发问题,有效地降低了无服务器计算请求的响应延时.
基金supported by the National Natural Science Foundation of China under Grant Nos.61872446,61902417,71690233,and 71971212the Natural Science Foundation of Hunan Province of China under Grant No.2019JJ20024.
文摘The problem of recommending new items to users(often referred to as item cold-start recommendation)remains a challenge due to the absence of users’past preferences for these items.Item features from side information are typically leveraged to tackle the problem.Existing methods formulate regression methods,taking item features as input and user ratings as output.These methods are confronted with the issue of overfitting when item features are high-dimensional,which greatly impedes the recommendation experience.Availing of high-dimensional item features,in this work,we opt for feature selection to solve the problem of recommending top-N new items.Existing feature selection methods find a common set of features for all users,which fails to differentiate users1 preferences over item features.To personalize feature selection,we propose to select item features discriminately for different users.We study the personalization of feature selection at the level of the user or user group.We fulfill the task by proposing two embedded feature selection models.The process of personalized feature selection filters out the dimensions that are irrelevant to recommendations or unappealing to users.Experimental results on real-life datasets with high-dimensional side information reveal that the proposed method is effective in singling out features that are crucial to top-N recommendation and hence improving performance.
基金supported by the National Key Research and Development Program of China(No.2020YFB1506300)the National Natural Science Foundation of China(No.51806071)+1 种基金the Natural Science Foundation of Hubei Province(No.2020CFA040)Wuhan Applied Foundational Frontier Project(No.2020010601012205).
文摘The Self-adaptive control of the temperature can achieve the start of fuel cell at different operating temperatures, which is very important for the successful cold-start of the air-cooled PEMFC. The temperature distribution characteristics during the cold-start process were analyzed based on adaptive temperature recognition control in this paper. Preheating model and cold-start model were established and the optimal balance between the hot air flow rate and the temperature required to promote a uniform temperature distribution in the stack was explored in the preheating stage. Finally, the non-equilibrium mass transfer, as well as the temperature rise in the catalyst layer and gas diffusion layer with different current densities, were analyzed in the start-up stage. The results indicate that the air-cooled PEMFC stack can be successfully started up at -40 ◦C within 10 min by means of external gas heating. The current density and air velocity have significant impacts on the temperature of aircooled PEMFC stack. Dynamic analysis of air-cooled PEMFCs and real-time monitoring are suitable for machine learning and self-adaptive control to set the operation parameters to achieve successful cold start. Optimize the matching of load current and cathode inlet speed to achieve thermal management in low temperature environment.
基金the Departments of Civil Engineering and Chemical Engineering and Technology,Indian Institute of Technology (Banaras Hindu University),Varanasi,India,for their guidance and support
文摘Carbon monoxide(CO) is a poisonous gas particularly to all leaving being present in the atmosphere.An estimate has shown that the vehicular exhaust contributes the largest source of CO pollution in developed countries.Due to the exponentially increasing number of automobile vehicles on roads,CO concentrations have reached an alarming level in urban areas.To control this vehicular exhaust pollution,the end-of-pipe-technology using catalytic converters is recommended.The catalysts operating efficiently in a catalytic converter are a challenging class of materials for applications in cold start of engines to maintain indoor air quality.In the cold start period,the catalytic converter was entirely inactive,because the catalytic converter had not been warmed up.The cold start phase is also depending upon the characteristics of vehicles and property of catalysts.The increasing cost of noble metals with the increasing number of vehicles motivates the investigation of material concepts to reduce the precious metal content in automotive catalysts or to find a substitute for noble metals.Hopcalite(CuMnOx) catalyst could work very well at the low temperature;thus,it can overcome the problem of cold-start emissions if used in a catalytic converter.Further,low cost,easy availability and advanced synthesis methods with stabilizer,promoter,etc.,advocates for the use of hopcalite as an auto exhaust purification catalyst.Although there are numerous research articles present on this topic until now,no review has been presented for demanding this issue.So there is a space in this area,and it has been made an attempt to seal this hole and progress the future scope for hopcalite catalyst for purification of exhaust gases by this review.
