With the ongoing development of sensor devices and network techniques, big data is being generated from the cyber-physical systems. Because of sensor equipment occasional failure and network transmission unreliability, a large number of low-quality data, such as noisy data and incomplete data, is collected from the cyber-physical systems. Low-quality data poses a remarkable challenge on deep learning models for big data feature learning. As a novel deep learning model, the deep computation model achieves the super performance for big data feature learning. However, it is difficult for the deep computation model to learn dependable features for low-quality data since it uses the nonlinear function as the encoder. In this paper, a dependable deep computation model is proposed for feature learning on low-quality big data in cyber-physical systems. Specially, a regularity is added into the objective function of the deep computation model to obtain reliable features in the intermediate-level representation space. Furthermore, a learning algorithm based on the back-propagation strategy is devised to train the parameters of the proposed model. Finally, some experiments are conducted to evaluate the effectiveness of the dependable deep computation model for low-quality big data feature learning. Results indicate that the proposed model performs better than the conventional deep computation model and the denoising deep computation model for the classification and the restoration for the low-quality data in cyber-physical systems.
Simulink is widely used for model-driven development (MDD) of cyber-physical systems. Typically, the Simulink based development starts with Stateflow modeling, followed by simulation, validation and code generation mapped to physical execution platforms. However, recent industrial trends have raised the demands of rigorous verification on safety-critical applications, which is unfortunately challenging. Even the constructed Stateflow model and the generated code pass the validation of Simulink Design Verifier and Simulink Polyspace, respectively, the system may still fail due to some implicit bugs contained in the model and the generated code. In this paper, we present a novel approach to bridge the Stateflow based model driven development and a well-defined rigorous verification. First, we develop a self-contained toolkit to translate Stateflow model into timed automata, where major advanced modeling features in Stateflow are supported. Taking advantage of the strong verification capability of Uppaal, we can not only find bugs in Stateflow models which are missed by Simulink Design Verifier, but also check more important temporal properties. Next, we customize a runtime verifier for the generated non-intrusive VHDL and C code of Stateflow model for monitoring. The major strength of the customization is the flexibility to collect and analyze runtime properties with a pure software monitor, which offers more opportunities for engineers to achieve high reliability of the target system compared with the traditional act that only relies on Simulink Polyspace. We incorporate these two parts into original Stateflow based MDD seamlessly. In this way, safety-critical properties are both verified at the model level, and at the consistent system implementation level with physical execution environment in consideration. We apply our approach to the development of a typical cyber-physical systemtrain communication controller based on the International Electrotechnical Commission standard 61375. Experiments show that more ambiguousness in the standard are detected during Uppaal verification, and the errors have been confirmed. Furthermore, the verified implementation has been deployed on real trains.
Commercial buildings are significant consumers of electricity. We propose a number of methods for managing power in commercial buildings. The first step towards better energy management in commercial buildings is monitoring consumption. However, instrumenting every electrical panel in a large commercial building is an expensive proposition. In this paper, we demonstrate that it is also unnecessary. Specifically, we propose a greedy meter (sensor) placement algorithm based on maximization of information gain subject to a cost constraint. The algorithm provides a near-optimal solution guarantee, and our empirical results demonstrate a 15% improvement in prediction power over conventional methods. Next, to identify power saving opportunities, we use an unsupervised anomaly detection technique based on a low-dimensional embedding. Furthermore, to enable a building manager to effectively plan for demand response programs, we evaluate several solutions for fine-grained, short-term load forecasting. Our investigation reveals that support vector regression and an ensemble model work best overall. Finally, to better manage resources such as lighting and HVAC, we propose a semi-supervised approach combining hidden Markov models (HMM) and a standard classifier to model occupancy based on readily available port-level network statistics. We show that the proposed two step approach simplifies the occupancy model while achieving good accuracy. The experimental results demonstrate an average occupancy estimation error of 9.3% with a potential reduction of 9.5% in lighting load using our occupancy models.