Prediction error identification of linear dynamic networks with rank-reduced noise

Dynamic networks are interconnected dynamic systems with measured node signals and dynamic modules reflecting the links between the nodes. We address the problem of \red{identifying a dynamic network with known topology, on the basis of measured signals}, for the situation of additive process noise on the node signals that is spatially correlated and that is allowed to have a spectral density that is singular. A prediction error approach is followed in which all node signals in the network are jointly predicted. The resulting joint-direct identification method, generalizes the classical direct method for closed-loop identification to handle situations of mutually correlated noise on inputs and outputs. When applied to general dynamic networks with rank-reduced noise, it appears that the natural identification criterion becomes a weighted LS criterion that is subject to a constraint. This constrained criterion is shown to lead to maximum likelihood estimates of the dynamic network and therefore to minimum variance properties, reaching the Cramer-Rao lower bound in the case of Gaussian noise.Comment: 17 pages, 5 figures, revision submitted for publication in Automatica, 4 April 201

Local module identification in dynamic networks with correlated noise: the full input case

The identification of local modules in dynamic networks with known topology has recently been addressed by formulating conditions for arriving at consistent estimates of the module dynamics, typically under the assumption of having disturbances that are uncorrelated over the different nodes. The conditions typically reflect the selection of a set of node signals that are taken as predictor inputs in a MISO identification setup. In this paper an extension is made to arrive at an identification setup for the situation that process noises on the different node signals can be correlated with each other. In this situation the local module may need to be embedded in a MIMO identification setup for arriving at a consistent estimate with maximum likelihood properties. This requires the proper treatment of confounding variables. The result is an algorithm that, based on the given network topology and disturbance correlation structure, selects an appropriate set of node signals as predictor inputs and outputs in a MISO or MIMO identification setup. As a first step in the analysis, we restrict attention to the (slightly conservative) situation where the selected output node signals are predicted based on all of their in-neighbor node signals in the network.Comment: Extended version of paper submitted to the 58th IEEE Conf. Decision and Control, Nice, 201

A Web-platform for Linking IFC to External Information during the Entire Lifecycle of a Building

AbstractDuring the lifecycle of a building, much more information is used and produced than can be contained inside a Building Information Model (BIM). The information outside the BIM is seldom connected to the BIM or connected across domains. Furthermore, information in BIM is only accessible to people with sufficient CAD or architectural background, and often expensive software is needed to edit and add data. This inefficient information management causes significant costs in practice. Our research contributes to the development of IFC based web applications in practice and demonstrates a way of linking machine to human readable data thus making the data accessible to non CAD users or architectural experts. In this paper we describe such a platform for the integration of model-based data and non-model based data. We tried to construct a mapping process from IFC properties to a national building element classification system, as a way of structuring the objects and information for use in our web platform. Since both the structure of IFC and most building element classification systems are based on semantic relations of building elements (i.e. holonym, meronym, hypernym), translations by means of a basic reasoning system should be feasible. We elaborate on several uses of this platform in applications for maintenance and reuse of building materials, buildings and built structures

Single module identifiability in linear dynamic networks

A recent development in data-driven modelling addresses the problem of identifying dynamic models of interconnected systems, represented as linear dynamic networks. For these networks the notion network identifiability has been introduced recently, which reflects the property that different network models can be distinguished from each other. Network identifiability is extended to cover the uniqueness of a single module in the network model. Conditions for single module identifiability are derived and formulated in terms of path-based topological properties of the network models.Comment: 6 pages, 2 figures, submitted to Control Systems Letters (L-CSS) and the 57th IEEE Conference on Decision and Control (CDC

Diagnose, indicate, and treat severe mental illness (DITSMI) as appropriate care:A three-year follow-up study in long-term residential psychiatric patients on the effects of re-diagnosis on medication prescription, patient functioning, and hospital bed utilization

BACKGROUND: While polypharmacy is common in long-term residential psychiatric patients, prescription combinations may, from an evidence-based perspective, be irrational. Potentially, many psychiatric patients are treated on the basis of a poor diagnosis. We therefore evaluated the DITSMI model (i.e., Diagnose, Indicate, and Treat Severe Mental Illness), an intervention that involves diagnosis (or re-diagnosis) and appropriate treatment for severely mentally ill long-term residential psychiatric patients. Our main objective was to determine whether DITSMI affected changes over time regarding diagnoses, pharmacological treatment, psychosocial functioning, and bed utilization. METHODS: DITSMI was implemented in a consecutive patient sample of 94 long-term residential psychiatric patients during a longitudinal cohort study without a control group. The cohort was followed for three calendar years. Data were extracted from electronic medical charts. As well as diagnoses, medication use and current mental status, we assessed psychosocial functioning using the Health of the Nations Outcome Scale (HoNOS). Bed utilization was assessed according to length of stay (LOS). Change was analyzed by comparing proportions of these data and testing them with chi-square calculations. We compared the numbers of diagnoses and medication changes, the proportions of HoNOS scores below cut-off, and the proportions of LOS before and after provision of the protocol. RESULTS: Implementation of the DITSMI model was followed by different diagnoses in 49% of patients, different medication in 67%, some improvement in psychosocial functioning, and a 40% decrease in bed utilization. CONCLUSIONS: Our results suggest that DITSMI can be recommended as an appropriate care for all long-term residential psychiatric patients