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
As a major function of smart transportation in smart cities,vehicle model recognition plays an important role in intelligent transportation.Due to the difference among different vehicle models recognition datasets,the...As a major function of smart transportation in smart cities,vehicle model recognition plays an important role in intelligent transportation.Due to the difference among different vehicle models recognition datasets,the accuracy of network model training in one scene will be greatly reduced in another one.However,if you don’t have a lot of vehicle model datasets for the current scene,you cannot properly train a model.To address this problem,we study the problem of cold start of vehicle model recognition under cross-scenario.Under the condition of small amount of datasets,combined with the method of transfer learning,load the DAN(Deep Adaptation Networks)and JAN(Joint Adaptation Networks)domain adaptation modules into the convolutional neural network AlexNet and ResNet,and get four models:AlexNet-JAN,AlexNet-DAN,ResNet-JAN,and ResNet-DAN which can achieve a higher accuracy at the beginning.Through experiments,transfer the vehicle model recognition from the network image dataset(source domain)to the surveillance-nature dataset(target domain),both Top-1 and Top-5 accuracy have been improved by at least 20%.展开更多
Recommender system (RS) has become a very important factor in many eCommerce sites. In our daily life, we rely on the recommendation from other persons either by word of mouth, recommendation letters, movie, item and ...Recommender system (RS) has become a very important factor in many eCommerce sites. In our daily life, we rely on the recommendation from other persons either by word of mouth, recommendation letters, movie, item and book reviews printed in newspapers, etc. The typical Recommender Systems are software tools and techniques that provide support to people by identifying interesting products and services in online store. It also provides a recommendation for certain users who search for the recommendations. The most important open challenge in Collaborative filtering recommender system is the cold start problem. If the adequate or sufficient information is not available for a new item or users, the recommender system runs into the cold start problem. To increase the usefulness of collaborative recommender systems, it could be desirable to eliminate the challenge such as cold start problem. Revealing the community structures is crucial to understand and more important with the increasing popularity of online social networks. The community detection is a key issue in social network analysis in which nodes of the communities are tightly connected each other and loosely connected between other communities. Many algorithms like Givan-Newman algorithm, modularity maximization, leading eigenvector, walk trap, etc., are used to detect the communities in the networks. To test the community division is meaningful we define a quality function called modularity. Modularity is that the links within a community are higher than the expected links in those communities. In this paper, we try to give a solution to the cold-start problem based on community detection algorithm that extracts the community from the social networks and identifies the similar users on that network. Hence, within the proposed work several intrinsic details are taken as a rule of thumb to boost the results higher. Moreover, the simulation experiment was taken to solve the cold start problem.展开更多
Gasoline compression ignition (GCI) is one of the most promising combustion concepts to maintain low pollutant emissions and high efficiency. However, low load combustion stability and firing in cold-start operations ...Gasoline compression ignition (GCI) is one of the most promising combustion concepts to maintain low pollutant emissions and high efficiency. However, low load combustion stability and firing in cold-start operations are two major challenges for GCI combustion. Strategies including negative valve overlap (NVO), advanced injection strategies, fuel reforming, and intake preheating have been proposed in order to solve these difficulties;however, the cold start is still an obstacle. The objective of this work is to study effective methods to achieve GCI engine cold start-up. This work combines NVO, in-cylinder fuel reforming, and intake preheating to achieve quick firing under cold-start conditions and the subsequent warmup conditions. The results show that start of injection (SOI) during the intake stroke yields the best fuel economy, and injection during the compression stroke has the potential to extend the low load limit. Furthermore, SOI during the NVO period grants the ability to operate under engine conditions with cold intake air and coolant. With highly reactive products made by in-cylinder fuel reforming and fast heat accumulation in the combustion chamber, the NVO injection strategy is highly appropriate for GCI firing. An additional assisted technical method, such as intake preheating, is required to ignite the first firing cycle for a cold-start process. With the combination of NVO, in-cylinder fuel reforming, and intake preheating, the GCI engine successfully started within five combustion cycles in the experiment. After the firing process, the engine could stably operate without further intake preheating;thus, this method is appropriate for engine cold-start and warm-up.展开更多
故障场景下,软开关(soft open points,SOP)的电压支撑和负荷转供作用能有效改善配电系统中的恢复性能。为了提高互联配电系统在极端事件下的运行灵活性和供电可靠性,该文建立含多端软开关的互联配电系统故障恢复的优化决策方法。具体地...故障场景下,软开关(soft open points,SOP)的电压支撑和负荷转供作用能有效改善配电系统中的恢复性能。为了提高互联配电系统在极端事件下的运行灵活性和供电可靠性,该文建立含多端软开关的互联配电系统故障恢复的优化决策方法。具体地,对互联配电网中的多端SOP、分布式电源、储能和冷负荷启动过程进行建模,分析SOP各个端口在恢复过程中的控制模式,并探讨互联配电系统恢复过程中线路拓扑变化和电气装置动作之间的联系,从而实现对系统内可控装置动作顺序的优化决策。之后以负荷恢复量最大、系统总损耗最小为目标,构建互联配电网的故障恢复模型。在原模型基础上进行二阶锥松弛和逻辑约束线性化,并对转化成的混合整数二阶锥规划模型进行求解。最后,以5端SOP连接IEEE33节点和IEEE69节点的系统为算例,验证所述恢复方法的有效性。展开更多
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.
基金This work was supported by CETC Joint Research Program under Grant 6141B08020101,6141B08080101National Key R&D Program of China under Grant 2018ZX09201014the National Natural Science Foundation of China under Grant 61002011.
文摘As a major function of smart transportation in smart cities,vehicle model recognition plays an important role in intelligent transportation.Due to the difference among different vehicle models recognition datasets,the accuracy of network model training in one scene will be greatly reduced in another one.However,if you don’t have a lot of vehicle model datasets for the current scene,you cannot properly train a model.To address this problem,we study the problem of cold start of vehicle model recognition under cross-scenario.Under the condition of small amount of datasets,combined with the method of transfer learning,load the DAN(Deep Adaptation Networks)and JAN(Joint Adaptation Networks)domain adaptation modules into the convolutional neural network AlexNet and ResNet,and get four models:AlexNet-JAN,AlexNet-DAN,ResNet-JAN,and ResNet-DAN which can achieve a higher accuracy at the beginning.Through experiments,transfer the vehicle model recognition from the network image dataset(source domain)to the surveillance-nature dataset(target domain),both Top-1 and Top-5 accuracy have been improved by at least 20%.
文摘Recommender system (RS) has become a very important factor in many eCommerce sites. In our daily life, we rely on the recommendation from other persons either by word of mouth, recommendation letters, movie, item and book reviews printed in newspapers, etc. The typical Recommender Systems are software tools and techniques that provide support to people by identifying interesting products and services in online store. It also provides a recommendation for certain users who search for the recommendations. The most important open challenge in Collaborative filtering recommender system is the cold start problem. If the adequate or sufficient information is not available for a new item or users, the recommender system runs into the cold start problem. To increase the usefulness of collaborative recommender systems, it could be desirable to eliminate the challenge such as cold start problem. Revealing the community structures is crucial to understand and more important with the increasing popularity of online social networks. The community detection is a key issue in social network analysis in which nodes of the communities are tightly connected each other and loosely connected between other communities. Many algorithms like Givan-Newman algorithm, modularity maximization, leading eigenvector, walk trap, etc., are used to detect the communities in the networks. To test the community division is meaningful we define a quality function called modularity. Modularity is that the links within a community are higher than the expected links in those communities. In this paper, we try to give a solution to the cold-start problem based on community detection algorithm that extracts the community from the social networks and identifies the similar users on that network. Hence, within the proposed work several intrinsic details are taken as a rule of thumb to boost the results higher. Moreover, the simulation experiment was taken to solve the cold start problem.
基金the National Natural Science Foundation of China (91641203, 51476114, and 91741119)he National Key Research and Development Program of China (2017YFB0103400).
文摘Gasoline compression ignition (GCI) is one of the most promising combustion concepts to maintain low pollutant emissions and high efficiency. However, low load combustion stability and firing in cold-start operations are two major challenges for GCI combustion. Strategies including negative valve overlap (NVO), advanced injection strategies, fuel reforming, and intake preheating have been proposed in order to solve these difficulties;however, the cold start is still an obstacle. The objective of this work is to study effective methods to achieve GCI engine cold start-up. This work combines NVO, in-cylinder fuel reforming, and intake preheating to achieve quick firing under cold-start conditions and the subsequent warmup conditions. The results show that start of injection (SOI) during the intake stroke yields the best fuel economy, and injection during the compression stroke has the potential to extend the low load limit. Furthermore, SOI during the NVO period grants the ability to operate under engine conditions with cold intake air and coolant. With highly reactive products made by in-cylinder fuel reforming and fast heat accumulation in the combustion chamber, the NVO injection strategy is highly appropriate for GCI firing. An additional assisted technical method, such as intake preheating, is required to ignite the first firing cycle for a cold-start process. With the combination of NVO, in-cylinder fuel reforming, and intake preheating, the GCI engine successfully started within five combustion cycles in the experiment. After the firing process, the engine could stably operate without further intake preheating;thus, this method is appropriate for engine cold-start and warm-up.
文摘故障场景下,软开关(soft open points,SOP)的电压支撑和负荷转供作用能有效改善配电系统中的恢复性能。为了提高互联配电系统在极端事件下的运行灵活性和供电可靠性,该文建立含多端软开关的互联配电系统故障恢复的优化决策方法。具体地,对互联配电网中的多端SOP、分布式电源、储能和冷负荷启动过程进行建模,分析SOP各个端口在恢复过程中的控制模式,并探讨互联配电系统恢复过程中线路拓扑变化和电气装置动作之间的联系,从而实现对系统内可控装置动作顺序的优化决策。之后以负荷恢复量最大、系统总损耗最小为目标,构建互联配电网的故障恢复模型。在原模型基础上进行二阶锥松弛和逻辑约束线性化,并对转化成的混合整数二阶锥规划模型进行求解。最后,以5端SOP连接IEEE33节点和IEEE69节点的系统为算例,验证所述恢复方法的有效性。
