01905nas a2200217   4500008004100000022001400041245009000055210006900145260001200214300001100226520113500237653001301372653003101385653003101416653002301447653003301470100001401503700001901517700001501536856013601551       2018                        eng d  a1940-149300aBuildings.Occupants: a Modelica package for modelling occupant behaviour in buildings0 aBuildingsOccupants a Modelica package for modelling occupant beh  c11/2018  a1 - 123 a<p>Energy-related occupant behaviour is crucial to design and operation of energy and control systems in buildings. Occupant behaviours are often oversimplified as static schedules or settings in building performance simulation ignoring their stochastic nature. The continuous and dynamic interaction between occupants and building systems motivates their simultaneous simulation in an efficient manner. In the past, simultaneous simulation has relied on co-simulation approaches or customized source code changes to building simulation programmes. This paper presents Buildings. Occupants, an open-source package implemented in Modelica, for the simulation of occupant behaviours of lighting, windows, blinds, heating and air conditioning systems in office and residential buildings. Examples were presented to illustrate how the models in the Occupants package are capable to simulate stochastic occupant behaviours. The major contribution of this work is to introduce the equation-based modelling approach to simulate occupant behaviours in buildings and to develop an open-source Occupants package in the Modelica language</p>10amodelica10aModelica Buildings Library10aModelica Occupants Package10aOccupant Behaviour10aOccupant behaviour modelling1 aWang, Zhe1 aHong, Tianzhen1 aJia, Ruoxi  uhttps://www.tandfonline.com/doi/full/10.1080/19401493.2018.1543352https://www.tandfonline.com/doi/pdf/10.1080/19401493.2018.154335202000nas a2200253   4500008004100000022001300041245010400054210006900158260001200227300001100239490000700250520118500257653002701442653001801469653001601487653002501503653002001528100003001548700001501578700001901593700001901612700002901631856008601660       2018                        eng d  a1550485900aA Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems0 aFramework for PrivacyPreserving Data Publishing with Enhanced Ut  c12/2018  a1 - 220 v143 a<p>Cyber-physical systems have enabled the collection of massive amounts of data in an unprecedented level of spatial and temporal granularity. Publishing these data can prosper big data research, which, in turn, helps improve overall system efficiency and resiliency. The main challenge in data publishing is to ensure the usefulness of published data while providing necessary privacy protection. In our previous work (Jia et al. 2017a), we presented a privacy-preserving data publishing framework (referred to as PAD hereinafter), which can guarantee k-anonymity while achieving better data utility than traditional anonymization techniques. PAD learns the information of interest to data users or features from their interactions with the data publishing system and then customizes data publishing processes to the intended use of data. However, our previous work is only applicable to the case where the desired features are linear in the original data record. In this article, we extend PAD to nonlinear features. Our experiments demonstrate that for various data-driven applications, PAD can achieve enhanced utility while remaining highly resilient to privacy threats.</p>10acyber physical systems10adeep learning10ak-anonymity10aPrivacy preservation10aSmart buildings1 aSangogboye, Fisayo, Caleb1 aJia, Ruoxi1 aHong, Tianzhen1 aSpanos, Costas1 aKjærgaard, Mikkel, Baun  uhttps://simulationresearch.lbl.gov/publications/framework-privacy-preserving-data