Impact of Ambient Temperatures on VOC Emissions and OFP during Cold Start for SI Car Real World Urban Driving

2009 ◽  
Author(s):  
Hu Li ◽  
Gordon E. Andrews ◽  
Dimitrios Savvidis
Keyword(s):  
Real World ◽  
Cold Start ◽  
Voc Emissions ◽  
Ambient Temperatures

Improving recommender systems’ performance on cold-start users and controversial items by a new similarity model

2016 ◽  
Vol 12 (2) ◽  
pp. 126-149 ◽  
Author(s):  
Masoud Mansoury ◽  
Mehdi Shajari
Keyword(s):  
Real World ◽  
Design Methodology ◽  
Cold Start ◽  
Selection Function ◽  
Data Sets ◽  
Real World Data ◽  
Content Type ◽  
User Similarity ◽  
Active User ◽  
Similarity Model

Purpose This paper aims to improve the recommendations performance for cold-start users and controversial items. Collaborative filtering (CF) generates recommendations on the basis of similarity between users. It uses the opinions of similar users to generate the recommendation for an active user. As a similarity model or a neighbor selection function is the key element for effectiveness of CF, many variations of CF are proposed. However, these methods are not very effective, especially for users who provide few ratings (i.e. cold-start users). Design/methodology/approach A new user similarity model is proposed that focuses on improving recommendations performance for cold-start users and controversial items. To show the validity of the authors’ similarity model, they conducted some experiments and showed the effectiveness of this model in calculating similarity values between users even when only few ratings are available. In addition, the authors applied their user similarity model to a recommender system and analyzed its results. Findings Experiments on two real-world data sets are implemented and compared with some other CF techniques. The results show that the authors’ approach outperforms previous CF techniques in coverage metric while preserves accuracy for cold-start users and controversial items. Originality/value In the proposed approach, the conditions in which CF is unable to generate accurate recommendations are addressed. These conditions affect CF performance adversely, especially in the cold-start users’ condition. The authors show that their similarity model overcomes CF weaknesses effectively and improve its performance even in the cold users’ condition.

Influence of Low Ambient Temperatures on the Exhaust Gas and Deposit Composition of Gasoline Engines

2021 ◽  
pp. 1-11
Author(s):  
Dominik Appel ◽  
Fabian P. Hagen ◽  
Uwe Wagner ◽  
Thomas Koch ◽  
Henning Bockhorn ◽  
...  
Keyword(s):  
Internal Combustion Engines ◽  
Gasoline Engine ◽  
Cold Start ◽  
Operating Conditions ◽  
Pollutant Emissions ◽  
Ex Situ ◽  
Exhaust Gas ◽  
Gasoline Engines ◽  
Ambient Temperatures ◽  
Aftertreatment Systems

Abstract To comply with future emission regulations for internal combustion engines, system-related cold-start conditions in short-distance traffic constitute a particular challenge. Under these conditions, pollutant emissions are seriously increased due to internal engine effects and unfavorable operating conditions of the exhaust aftertreatment systems. As a secondary effect, the composition of the exhaust gases has a considerable influence on the deposition of aerosols via different deposition mechanisms and on fouling processes of exhaust gas-carrying components. Also, the performance of exhaust gas aftertreatment systems may be affected disadvantageously. In this study, the exhaust gas and deposit composition of a turbocharged three-cylinder gasoline engine is examined in-situ upstream of the catalytic converter at ambient and engine starting temperatures of -22 °C to 23 °C using a Fourier-transform infrared spectrometer and a particle spectrometer. For the cold start investigation, a modern gasoline engine with series engine periphery is used. In particular, the investigation of the behavior of deposits in the exhaust system of gasoline engines during cold start under dynamic driving conditions represents an extraordinary challenge due to an average lower soot concentration in the exhaust gas compared to diesel engines and so far, has not been examined in this form. A novel sampling method allows ex-situ analysis of formed deposits during a single driving cycle. Both, particle number concentration and the deposition rate are higher in the testing procedure of Real Driving Emissions (RDE) than in the inner-city part of the Worldwide harmonized Light vehicles Test Cycle (WLTC). In addition, reduced ambient temperatures increase the amount of deposits, which consist predominantly of soot and to a minor fraction of volatile compounds. Although the primary particle size distributions of the deposited soot particles do not change when boundary conditions change, the degree of graphitization within the particles increases with increasing exhaust gas temperature.

scholarly journals Deep Interest-Shifting Network with Meta-Embeddings for Fresh Item Recommendation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhao Li ◽  
Haobo Wang ◽  
Donghui Ding ◽  
Shichang Hu ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Real World ◽  
Contextual Information ◽  
Cold Start ◽  
Training Data ◽  
Learning To Learn ◽  
User Interests ◽  
Synthetic Datasets ◽  
Fresh Products ◽  
Embedding Performance ◽  
Deep Interest

Nowadays, people have an increasing interest in fresh products such as new shoes and cosmetics. To this end, an E-commerce platform Taobao launched a fresh-item hub page on the recommender system, with which customers can freely and exclusively explore and purchase fresh items, namely, the New Tendency page. In this work, we make a first attempt to tackle the fresh-item recommendation task with two major challenges. First, a fresh-item recommendation scenario usually faces the challenge that the training data are highly deficient due to low page views. In this paper, we propose a deep interest-shifting network (DisNet), which transfers knowledge from a huge number of auxiliary data and then shifts user interests with contextual information. Furthermore, three interpretable interest-shifting operators are introduced. Second, since the items are fresh, many of them have never been exposed to users, leading to a severe cold-start problem. Though this problem can be alleviated by knowledge transfer, we further babysit these fully cold-start items by a relational meta-Id-embedding generator (RM-IdEG). Specifically, it trains the item id embeddings in a learning-to-learn manner and integrates relational information for better embedding performance. We conducted comprehensive experiments on both synthetic datasets as well as a real-world dataset. Both DisNet and RM-IdEG significantly outperform state-of-the-art approaches, respectively. Empirical results clearly verify the effectiveness of the proposed techniques, which are arguably promising and scalable in real-world applications.

scholarly journals Testing and evaluation of cold-start emissions from a gasoline engine in RDE test at two different ambient temperatures

2021 ◽  
Vol 11 (1) ◽  
pp. 425-434
Author(s):  
Jacek Pielecha ◽  
Kinga Skobiej ◽  
Karolina Kurtyka
Keyword(s):  
Ambient Temperature ◽  
Gasoline Engine ◽  
Cold Start ◽  
Exhaust Emissions ◽  
Operating Parameters ◽  
Time Interval ◽  
Vehicle Speed ◽  
Passenger Cars ◽  
Ambient Temperatures ◽  
The Impact

Abstract In order to better reflect the actual ecological performance of vehicles in traffic conditions, both the emission standards and the applied emission tests are being developed, for example by considering exhaust emissions for a cold engine start. This article presents the research results on the impact of ambient temperature during the cold start of a gasoline engine in road emission tests. The Real Driving Emissions (RDE) tests apply to passenger cars that meet the Euro 6 emissions norm and they are complementary to their type approval tests. A portable emissions measurement system was used to record the engine and vehicle operating parameters, as well as to measure the exhaust emissions during tests. This allowed for parameters such as engine load, engine speed and vehicle speed to be monitored. The cold start conditions for two different temperatures (8°C and 25°C) were compared in detail. Moreover, the engine operating parameters, exhaust concentration values and road emissions for the 300 s time interval, were compared. The summary of the article presents the share of a passenger car’s cold start phase for each exhaust compound in the urban part of the test and in the entire Real Driving Emissions test depending on the ambient temperature.

Startup and Idle Emissions Analysis of a 2009 VW Jetta Using PEMS SemtechD

2012 ◽  
Author(s):  
Michael V. Johnson ◽  
Samuel Duncan ◽  
Steven S. McConnell
Keyword(s):  
Fuel Consumption ◽  
Measurement System ◽  
Catalytic Converter ◽  
Cold Start ◽  
Idle Time ◽  
Initial Assessment ◽  
Ambient Temperatures ◽  
Time Period ◽  
Startup Time ◽  
Total Hydrocarbons

A 2009 Volkswagen Jetta was tested using a Portable Emissions Measurement System (PEMS) SemtechD emissions analyzer to determine when the emissions and fuel consumption will be the smallest in regards to startup vs. idle emissions. An idle time equivalent was calculated to determine when idle emissions became equal to startup emissions at four different ambient temperatures for a cold start. The mass of Total Hydrocarbons (THC) emitted limited the idle time equivalent to less than the startup time for all ambient temperatures. The temperature of the Catalytic Converter (CC) was monitored to determine how quickly the car cools down and therefore, how beneficial it is to turn the car off after a certain time period. For the temperature range tested, it was more beneficial for reduced emissions to turn off the car compared to idling by a factor of at least four. The data suggests a trend that idling would never be the best option; however, more testing needs to be done with a greater range of CC cool down temperatures to confirm this initial assessment.

scholarly journals Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation

2020 ◽  
Author(s):  
Ruobing Xie ◽  
Zhijie Qiu ◽  
Jun Rao ◽  
Yi Liu ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Recommendation System ◽  
Contextual Information ◽  
Recommendation Systems ◽  
Cold Start ◽  
Personalized Recommendation ◽  
Small Subset ◽  
Multiple Channels ◽  
Attention Network

Real-world integrated personalized recommendation systems usually deal with millions of heterogeneous items. It is extremely challenging to conduct full corpus retrieval with complicated models due to the tremendous computation costs. Hence, most large-scale recommendation systems consist of two modules: a multi-channel matching module to efficiently retrieve a small subset of candidates, and a ranking module for precise personalized recommendation. However, multi-channel matching usually suffers from cold-start problems when adding new channels or new data sources. To solve this issue, we propose a novel Internal and contextual attention network (ICAN), which highlights channel-specific contextual information and feature field interactions between multiple channels. In experiments, we conduct both offline and online evaluations with case studies on a real-world integrated recommendation system. The significant improvements confirm the effectiveness and robustness of ICAN, especially for cold-start channels. Currently, ICAN has been deployed on WeChat Top Stories used by millions of users. The source code can be obtained from https://github.com/zhijieqiu/ICAN.

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