Electronically Controlling the System of Preheating Intake Air by Flame for Diesel Engine Cold-Start

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.

Alleviating the Cold Start Problem in Recommender Systems Based on Modularity Maximization Community Detection Algorithm

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.

Cold Start Problem of Vehicle Model Recognition under Cross-Scenario Based on Transfer Learning

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%.

Content Feature Extraction-based Hybrid Recommendation for Mobile Application Services

The number of mobile application services is showing an explosive growth trend,which makes it difficult for users to determine which ones are of interest.Especially,the new mobile application services are emerge continuously,most of them have not be rated when they need to be recommended to users.This is the typical problem of cold start in the field of collaborative filtering recommendation.This problem may makes it difficult for users to locate and acquire the services that they actually want,and the accuracy and novelty of service recommendations are also difficult to satisfy users.To solve this problem,a hybrid recommendation method for mobile application services based on content feature extraction is proposed in this paper.First,the proposed method in this paper extracts service content features through Natural Language Processing technologies such as word segmentation,part-of-speech tagging,and dependency parsing.It improves the accuracy of describing service attributes and the rationality of the method of calculating service similarity.Then,a language representation model called Bidirectional Encoder Representation from Transformers(BERT)is used to vectorize the content feature text,and an improved weighted word mover’s distance algorithm based on Term Frequency-Inverse Document Frequency(TFIDF-WMD)is used to calculate the similarity of mobile application services.Finally,the recommendation process is completed by combining the item-based collaborative filtering recommendation algorithm.The experimental results show that by using the proposed hybrid recommendation method presented in this paper,the cold start problem is alleviated to a certain extent,and the accuracy of the recommendation result has been significantly improved.

一种常温冷启动修正系数的测试方法

冷启动修正系数对循环油耗的仿真结果影响很大,本文采用怠速工况下油耗消耗量变化等效计算出循环工况的冷启动修正系数,并采用实车进行试验对比,利用仿真进行差异性分析。为后续在项目初期采用成本低试验方法简单的冷启动修正系数的测量提供一种参考。

Effect of catalyst layer mesoscopic pore-morphology on cold start process of PEM fuel cells

Water transport is of paramount importance to the cold start of proton exchange membrane fuel cells(PEMFCs).Analysis of water transport in cathode catalyst layer(CCL)during cold start reveals the distinct characteristics from the normal temperature operation.This work studies the effect of CCL mesoscopic pore-morphology on PEMFC cold start.The CCL mesoscale morphology is characterized by two tortuosity factors of the ionomer network and pore structure,respectively.The simulation results demonstrate that the mesoscale morphology of CCL has a significant influence on the performance of PEMFC cold start.It was found that cold-starting of a cell with a CCL of less tortuous mesoscale morphology can succeed,whereas starting up a cell with a CCL of more tortuous mesoscale morphology may fail.The CCL of less tortuous pore structure reduces the water back diffusion resistance from the CCL to proton exchange membrane(PEM),thus enhancing the water storage in PEM,while reducing the tortuosity in ionomer network of CCL is found to enhance the water transport in and the water removal from CCL.For the sake of better cold start performance,novel preparation methods,which can create catalyst layers of larger size primary pores and less tortuous pore structure and ionomer network,are desirable.

Experimental Study on the Combustion and Energy Flows of Vehicle Engine Under NEDC of Cold Start

To enhance the fuel economy of a vehicle powered by a gasoline engine under road conditions,an energy flow test of a vehicle was performed experimentally under the New European Driving Cycle of cold start.The energy distributions and related influencing factors were analyzed using the test data.Results show that the effective power and thermal efficiency are mainly affected by the engine load except in the early stage of the New European Driving Cycle.Because of the retarded CA50 and longer CA10-90,the effective thermal efficiency is lower in the early phase of driving conditions.Initially,the heat transfer loss mainly comprises the loss of the heating,ventilation,and air conditioning system.The radiator then plays the major role,with its percentage affected by the engine load and decreasing under the extra-urban driving cycle.The exhaust gas loss is decided by the temperature and flow rate of the exhaust gas,while its percentage is mainly affected by the temperature of the exhaust gas.In the early phase of driving conditions,the retarded spark advance angle leads to a higher temperature of the exhaust gas and a greater exhaust gas loss.The pumping loss and its percentage are mainly determined by the engine speed under the urban driving cycle,and both decrease under the extra-urban driving cycle except at maximum vehicle speed.

A novel material for passive NO_(x) adsorber:Ce-based BEA zeolite

Passive NO_(x) adsorbers(PNAs)were proposed to address the NO_(x) emissions during the cold start phase.Here we show a novel Ce-based BEA zeolite,as a noble-metal-free passive NO_(x)adsorber.The NO_(x) adsorption capacity of Ce/BEA reaches 36μmol/g in the feed gas close to realistic exhaust conditions,and the NO_(x) desorption temperature,which is around 290℃,is ideal for diesel exhaust after-treatment systems.Ce/BEA also behaves notable stability of high temperature CO exposure conditions.Multiple characterizations were performed to explore the NO_(x) adsorption chemistry of Ce/BEA.The Ce(Ⅳ)species in the BEA zeolite serves as the active center for NO_(x) adsorption.The bidentate nitrate species is responsible for the observed NO_(x) storage capacity,and the active oxygen around Ce(Ⅳ)plays a critical role in its formation.Considering the significantly better cost efficiency of Ce compared to Pd,Ce/BEA presents an enormous potential for the PNA applications and provides a novel formulation for the noblemetal choice of PNA materials.

Recent progress of Pd/zeolite as passive NO_(x) adsorber: Adsorption chemistry, structure-performance relationships, challenges and prospects

Due to the technology limitation and inferior deNO_(x) efficiency of urea selective catalytic reduction (SCR) catalysts at low temperatures, passive NO_(x) adsorber (PNA) for decrease of NO_(x), CO and hydrocarbons (HCs) during “cold start” of vehicles was proposed to meet the further tighten NO_(x) emission regulations in future. Among them, Pd modified zeolite PNA materials have received more attention because of their excellent NO_(x) storage capacity, anti-poisoning and hydrothermal stability and since Pd/zeolite PNA was proposed, a variety of advanced characterization methods have been applied to investigate its adsorption behavior and structure-performance relationship. The comprehension of the active sites and adsorption chemistry of Pd/zeolite PNA was also significantly improved. However, there are few reviews that systematically summarize the recent progress and application challenges in atomic-level understanding of this material. In this review, we summarized the latest research progress of Pd/zeolite PNA, including active adsorption sites, adsorption mechanism, material physicochemical properties, preparation methods, storage and release performance and structure-performance relationships. In addition, the deactivation challenges faced by Pd/zeolite PNA in practical applications, such as chemical poisoning, high temperature hydrothermal aging deactivation, etc., were also discussed at the micro-level, and some possible effective countermeasures are given. Besides, some possible improvements and research hotspots were put forward, which could be helpful for designing and constructing more efficient PNA materials for meeting the ultra-low NO_(x) emission regulation in the future.

A novel movies recommendation algorithm based on reinforcement learning with DDPG policy

Purpose-English original movies played an important role in English learning and communication.In order to find the required movies for us from a large number of English original movies and reviews,this paper proposed an improved deep reinforcement learning algorithm for the recommendation of movies.In fact,although the conventional movies recommendation algorithms have solved the problem of information overload,they still have their limitations in the case of cold start-up and sparse data.Design/methodology/approach-To solve the aforementioned problems of conventional movies recommendation algorithms,this paper proposed a recommendation algorithm based on the theory of deep reinforcement learning,which uses the deep deterministic policy gradient(DDPG)algorithm to solve the cold starting and sparse data problems and uses Item2vec to transform discrete action space into a continuous one.Meanwhile,a reward function combining with cosine distance and Euclidean distance is proposed to ensure that the neural network does not converge to local optimum prematurely.Findings-In order to verify the feasibility and validity of the proposed algorithm,the state of the art and the proposed algorithm are compared in indexes of RMSE,recall rate and accuracy based on the MovieLens English original movie data set for the experiments.Experimental results have shown that the proposed algorithm is superior to the conventional algorithm in various indicators.Originality/value-Applying the proposed algorithm to recommend English original movies,DDPG policy produces better recommendation results and alleviates the impact of cold start and sparse data.