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| 1 | Estimating impacts of emission specific characteristics on vehicle operation for quantifying air pollutant emissions and energy use显示文摘This paper proposes and illustrates a methodology to predict the fraction of time motor vehicles spend in different operating conditions from readily observable variables called emission specific characteristics(ESC). ESC describe salient characteristics of vehicles,roadway geometry, the roadside environment, traffic, and driving style(aggressive,normal, and calm). The information generated by our methodology can then be entered in vehicular emission models that rely on vehicle specific power, i.e., comprehensive modal emissions model(CMEM), international vehicle emissions(IVE), or motor vehicle emission simulator(MOVES) to compute energy consumption and vehicular emissions for various air pollutants. After generating second-by-second vehicle trajectories from a calibrated micro-simulation model, the authors estimated structural equation models to examine the influence of link ESC on vehicle operation. Authors' results show that 67% of the link speed variance is explained by ESC. Overall, the roadway geometry exerts a greater influence on link speed than traffic characteristics, the roadside environment, and driving style.Moreover, the speed limit has the strongest influence on vehicle operation, followed by facility type and driving style. Better understanding the impact on vehicle operation of ESC could help metropolitan planning organizations(MPOs) and regional transportation authorities predict vehicle operations and reduce the environmental footprint of motor vehicles. | K. S. Nesamani Jean-Daniel Saphores Michael G. McNally R. Jayakrishnan | 2017 | Journal of Traffic and Transportation Engineering(English Edition)2017，4，3： | 3 |
| 2 | A Review of Graph Neural Networks and Their Applications in Power Systems显示文摘Deep neural networks have revolutionized many machine learning tasks in power systems,ranging from pattern recognition to signal processing.The data in these tasks are typically represented in Euclidean domains.Nevertheless,there is an increasing number of applications in power systems,where data are collected from non-Euclidean domains and represented as graph-structured data with high-dimensional features and interdependency among nodes.The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains.Recently,many publications generalizing deep neural networks for graph-structured data in power systems have emerged.In this paper,a comprehensive overview of graph neural networks(GNNs)in power systems is proposed.Specifically,several classical paradigms of GNN structures,e.g.,graph convolutional networks,are summarized.Key applications in power systems such as fault scenario application,time-series prediction,power flow calculation,and data generation are reviewed in detail.Furthermore,main issues and some research trends about the applications of GNNs in power systems are discussed. | Wenlong Liao Birgitte Bak-Jensen Jayakrishnan Radhakrishna Pillai Yuelong Wang Yusen Wang | 2022 | Journal of Modern Power Systems and Clean Energy2022，10，2： | 3 |
| 3 | Electrodeposition of Silver from Nonconventional Baths 显示文摘 | Jayakrishnan Sobha | 1996 | Transactions of the SAEST1996，31，12： | 1 |
| 4 | Glutaraldehyde cross - linked chitosan microspheres as a long acting biodegradable drug delivery vehicle: studies on the in vitro release of mitoxantrone and in vivo degradation of microspheres in rat muscle 显示文摘 | Jameela S R Jayakrishnan A | 1995 | Biomaterials1995，16，： | 1 |
| 5 | Effect of taxi information system on efficiency and quality of taxi services 显示文摘 | KIM H OH Jun-seok JAYAKRISHNAN R | 2005 | Transportation Research Record: Journal of the Transportation Research Board2005，1903，： | 1 |
| 6 | Metal distribution in electroplating of nickel and chro- mium显示文摘 | JAYAKRISHNAN S DHAYANAND K KRISHNAN R M | 1998 | Transactions of the Institute of'' Metal Finishing1998，76，3： | 1 |
| 7 | Subtraction helical CT angiography of intra- and extracranial vessels: Technical, considerations and preliminary experience显示文摘 | Jayakrishnan VK White PM Aitken D | 2003 | AJNR hmJ Neuroradiol2003，24，3： | 1 |
| 8 | Subtraction helical CT angiography of intra-and extracranial vessel: technical considerations and preliminary experience 显示文摘 | Jayakrishnan VK Aitken D | 2003 | AJNR2003，24，3： | 1 |
| 9 | Self-cross-linking biopolymers as injectable in situ forming biodegradable scaffolds显示文摘 | Balakrishnan B Jayakrishnan A | 2005 | Biomaterials2005，26，18： | 1 |
| 10 | Characteristics of zinc electrodeposits from acetate sohtiom显示文摘 | Sekar R Jayakrishnan S | | 0，，05： | 1 |
| 11 | Glutaraldehyde cross-linked chitosan microspheres as a long acting biodegradable drug delivery vehicle: studies on the in vitro release of mitoxantrone andin vivo degradation of microspheres in rat muscle显示文摘 | Jameela SR Jayakrishnan A | 1995 | Biomaterials1995，16，10： | 1 |
| 12 | Subtraction helical CT angi-ography of intra-and extracranial vessels:technical considerations and preliminary experience显示文摘 | Jayakrishnan VK White PM Aitken D | 2003 | AJNR2003，24，： | 1 |
| 13 | Algorithms for dynamic spectrum access with learning for cognitive radio显示文摘 | JAYAKRISHNAN U VENUGOPAL V V | 2010 | IEEE Transactions on Signal Processing2010，58，2： | 1 |
| 14 | Structur- al and electrochemical characterization of Ni nanostruc- ture films on steels with brush plating and sputter depo- sition 显示文摘 | Subramanian B Mohan S Jayakrishnan S | 2007 | Current Applied Physics2007，7，3： | 1 |
| 15 | Alkaline noncyanide bath for electrodeposition of silver 显示文摘 | JAYAKRISHNAN S NATARAJAN S R VASU K I | 1996 | Metal Finishing1996，94，5： | 1 |
| 16 | Alkaline Non- Cyanide Bath for Electro- Deposition of Silver显示文摘 | Jayakrishnan S Natarajan S R | 1996 | Metal Finishing1996，94，5： | 1 |
| 17 | Asthma control:importance of compliance and inhaler technique assessments显示文摘 | BADDAR S JAYAKRISHNAN B AL-Rawas O A | 2014 | Journal of asthma2014，51，4： | 1 |
| 18 | Glutaraldehyde Cross-linked Chitosan Microspheres as a Long Acting Biodegrada-ble Drug Delivery Vehicle: Studies on the in Vitro Releasesof Mitoxantrone and in Vivo Degradation of Microspheres inRat Muscle显示文摘 | JAMEELA S R JAYAKRISHNAN A | 1995 | Biomaterials1995，16，10： | 1 |
| 19 | Coordinated traffic-responsive ramp control via nonlinear state feedback显示文摘 | H.M. Zhang Stephen G. Ritchie R. Jayakrishnan | 2001 | Transportation Research Part C2001，，5： | 1 |
| 20 | Data-driven Missing Data Imputation for Wind Farms Using Context Encoder显示文摘High-quality datasets are of paramount importance for the operation and planning of wind farms.However,the datasets collected by the supervisory control and data acquisition(SCADA)system may contain missing data due to various factors such as sensor failure and communication congestion.In this paper,a data-driven approach is proposed to fill the missing data of wind farms based on a context encoder(CE),which consists of an encoder,a decoder,and a discriminator.Through deep convolutional neural networks,the proposed method is able to automatically explore the complex nonlinear characteristics of the datasets that are difficult to be modeled explicitly.The proposed method can not only fully use the surrounding context information by the reconstructed loss,but also make filling data look real by the adversarial loss.In addition,the correlation among multiple missing attributes is taken into account by adjusting the format of input data.The simulation results show that CE performs better than traditional methods for the attributes of wind farms with hallmark characteristics such as large peaks,large valleys,and fast ramps.Moreover,the CE shows stronger generalization ability than traditional methods such as auto-encoder,K-means,k-nearest neighbor,back propagation neural network,cubic interpolation,and conditional generative adversarial network for different missing data scales. | Wenlong Liao Birgitte Bak-Jensen Jayakrishnan Radhakrishna Pillai Dechang Yang Yusen Wang | 2022 | Journal of Modern Power Systems and Clean Energy2022，10，4： | 1 |
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