Ocean University of China

About: Ocean University of China is a education organization based out in Qingdao, China. It is known for research contribution in the topics: Population & Sea surface temperature. The organization has 27604 authors who have published 27886 publications receiving 440181 citations. The organization is also known as: Zhōngguó Hǎiyáng Dàxué & OUC.

Effects of excessive equatorial cold tongue bias on the projections of tropical Pacific climate change. Part I: the warming pattern in CMIP5 multi-model ensemble

Abstract: The excessive cold tongue error in the equatorial Pacific has persisted in several generations of climate models. Based on the historical simulations and Representative Concentration Pathway (RCP) 8.5 experiments in the Coupled Model Intercomparison Project phase 5 (CMIP5) multi-model ensemble (MME), this study finds that models with an excessive westward extension of cold tongue and insufficient equatorial western Pacific precipitation tend to project a weaker east-minus-west gradient of sea surface temperature (SST) warming along the equatorial Pacific under increased greenhouse gas (GHG) forcing. This La Nina-like error of tropical Pacific SST warming is consistent with our understanding of negative SST-convective feedback over the western Pacific warm pool. Based on this relationship between the present simulations and future projections, the present study applies an “observational constraint” of equatorial western Pacific precipitation to calibrate the projections of tropical Pacific climate change. After the corrections, CMIP5 models robustly project an El Nino-like warming pattern, with a MME mean increase by a factor of 2.3 in east-minus-west gradient of equatorial Pacific SST warming and reduced inter-model uncertainty. Corrections in projected changes in tropical precipitation and atmospheric circulation are physically consistent. This study suggests that a realistic cold tongue simulation would lead to a more reliable tropical Pacific climate projection.

Seasonality and Predictability of the Indian Ocean Dipole Mode: ENSO Forcing and Internal Variability

Abstract: This study evaluates the relative contributions to the Indian Ocean dipole (IOD) mode of interannual variability from the El Nino–Southern Oscillation (ENSO) forcing and ocean–atmosphere feedbacks internal to the Indian Ocean. The ENSO forcing and internal variability is extracted by conducting a 10-member coupled simulation for 1950–2012 where sea surface temperature (SST) is restored to the observed anomalies over the tropical Pacific but interactive with the atmosphere over the rest of the World Ocean. In these experiments, the ensemble mean is due to ENSO forcing and the intermember difference arises from internal variability of the climate system independent of ENSO. These elements contribute one-third and two-thirds of the total IOD variance, respectively. Both types of IOD variability develop into an east–west dipole pattern because of Bjerknes feedback and peak in September–November. The ENSO forced and internal IOD modes differ in several important ways. The forced IOD mode develops in Au...

Passive diagnosis for wireless sensor networks

Abstract: Network diagnosis, an essential research topic for traditional networking systems, has not received much attention for wireless sensor networks. Existing sensor debugging tools like sympathy or EmStar rely heavily on an add-in protocol that generates and reports a large amount of status information from individual sen-sor nodes, introducing network overhead to a resource constrained and usually traffic sensitive sensor network. We report in this study our initial attempt at providing a light-weight network diag-nosis mechanism for sensor networks. We propose PAD, a prob-abilistic diagnosis approach for inferring the root causes of ab-normal phenomena. PAD employs a packet marking algorithm for efficiently constructing and dynamically maintaining the inference model. Our approach does not incur additional traffic overhead for collecting desired information. Instead, we introduce a prob-abilistic inference model which encodes internal dependencies among different network elements, for online diagnosis of an operational sensor network system. Such a model is capable of additively reasoning root causes based on passively observed symptoms. We implement the PAD design in our sea monitoring sensor network test-bed and validate its effectiveness. We further evaluate the efficiency and scalability of this design through ex-tensive trace-driven simulations.

Local Differential Privacy-Based Federated Learning for Internet of Things

Abstract: The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users’ location information, traffic information, motor vehicle information, environmental information, etc., which raises severe sensitive personal information privacy concerns of the users. In addition, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs unexpected amount of communication cost. To avoid the privacy threat and reduce the communication cost, in this article, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model. Specifically, we propose four LDP mechanisms to perturb gradients generated by vehicles. The proposed Three-Outputs mechanism introduces three different output possibilities to deliver a high accuracy when the privacy budget is small. The output possibilities of Three-Outputs can be encoded with two bits to reduce the communication cost. Besides, to maximize the performance when the privacy budget is large, an optimal piecewise mechanism ( PM-OPT ) is proposed. We further propose a suboptimal mechanism ( PM-SUB ) with a simple formula and comparable utility to PM-OPT . Then, we build a novel hybrid mechanism by combining Three-Outputs and PM-SUB . Finally, an LDP-FedSGD algorithm is proposed to coordinate the cloud server and vehicles to train the model collaboratively. Extensive experimental results on real-world data sets validate that our proposed algorithms are capable of protecting privacy while guaranteeing utility.

Interface Engineering of Imidazolium Ionic Liquids toward Efficient and Stable CsPbBr3 Perovskite Solar Cells.

Abstract: The defect passivation of perovskite films is an efficacious way to further boost the power conversion efficiency (PCE) and long-term stability of perovskite solar cells (PSCs). In this work, ionic...