Search

Diabetes intensive care unit (ICU) patients are at increased risk of complications leading to in-hospital mortality. Assessing the likelihood of death is a challenging and time-consuming task due to a large number of influencing factors. Healthcare providers are interested in the detection of ICU patients at higher risk, such that risk factors can possibly be mitigated. While such severity scoring methods exist, they are commonly based on a snapshot of the health conditions of a patient during the ICU stay and do not specifically consider a patient's prior medical history. In this paper, a process mining/deep learning architecture is proposed to improve established severity scoring methods by incorporating the medical history of diabetes patients. First, health records of past hospital encounters are converted to event logs suitable for process mining. The event logs are then used to discover a process model that describes the past hospital encounters of patients. An adaptation of Decay Replay Mining is proposed to combine medical and demographic information with established severity scores to predict the in-hospital mortality of diabetes ICU patients. Significant performance improvements are demonstrated compared to established risk severity scoring methods and machine learning approaches using the Medical Information Mart for Intensive Care III dataset.

Dynamic Time Warping (DTW) is a popular method for measuring the similarity of time series. It is widely used in various domains. A major drawback of DTW is that it has a high computational complexity. To address this problem, pruning techniques to calculate the exact DTW distance, as well as DTW approximation methods, have become important approaches. In this paper, we introduce Blocked Dynamic Time Warping (BDTW), a new similarity measure which works on run-length encoded time series representation. BDTW utilizes any repetitive values (zero and nonzero) in time series to reduce DTW computation time. BDTW closely approximates DTW distance, and it is significantly faster than traditional DTW for time series with high levels of value repetition. Moreover, BDTW can be combined with time series representation methods which provide constant segments, to serve as a close approximation method even for the time series without value repetition. Constrained BDTW, BDTW upper bound and BDTW lower bound are discussed as variations of BDTW. BDTW upper bound and BDTW lower bound are presented as a new DTW upper bound and lower bound which can be efficiently applied on time series with high levels of value repetition for pruning unhopeful alignments and matches in the exact DTW calculation. We show the effectiveness of BDTW and its variations on different applications using the following datasets: Almanac of Minutely Power, Refit Smart Homes, as well as the 85 datasets from the University of California, Riverside time series classification archive (UCR archive).

Throughout recent years, dynamic time warping (DTW) has remained as a robust similarity measure in time series classification (TSC). 1-nearest neighbor (1-NN) algorithm with DTW is the most widely used classification method on time series serving as a benchmark. With the increasing demand for TSC on low-resource devices and the widespread of wearable devices, the need for a efficient and accurate time series classifier has never been higher. Although 1-NN DTW attains accurate results, it highly falls back on efficiency due to its quadratic complexity in the length of time series. In this paper, we propose a new approximation method for reducing the length of the time series as the input of DTW. We call it control chart approximation (CCA), after a similar concept used in statistical quality control processing. CCA representation approximates raw time series by transforming them into a set of segments with aggregated values and durations forming a reduced 3-D vector. We also propose an adaptation of DTW in 3-D space as a distance measure for 1-NN classifier, and denote the method as 1-NN 3-D DTW. Our experiments on 85 datasets from UCR archive—including 28 long-length (>500 points) time series datasets—show up to two orders of magnitude performance gain in running time compared to the state-of-the-art 1-NN DTW implementation. Moreover, it shows similar or better accuracy on the long time series in the experiment.

We define a method for the automatic generation of supervisory controllers that force a plant to perform a given operation by a given deadline. The operation must be executed by a prespecified delay λ with respect to the previous execution of the operation. Although our supervisor generation occurs offline with respect to plant execution, the resulting controllers automatically take into account variable task durations in an effort to increase the flexibility of operation schedules in the controlled plant. We model both the controlled plant and control supervisors as time Petri nets. In this setting, our control supervisors must force a target transition td to fire within λ time units since the previous firing of td. Our supervisor generation is based on the concept of a transition latency. The latency of a Petri net transition t is the time interval during which t must be disabled in order for target transition td to fire by its deadline. If a transition t that may delay the firing of td has latency l(t), then t must be disabled at least l(t) time units before the expiration of the deadline on td. In this paper, we discuss in detail two algorithms for generating transition latencies, and we show an application to a maintenance system.

Sequential function charts (SFCs) are among the standard languages for programmable logic controllers (PLCs) ubiquitous in automated manufacturing and production systems. While SFCs are quite user friendly to developers, it is not easy to guarantee the correctness of safety-critical SFC programs. Thus, methods and tools for verifying the correctness of SFC programs are highly desirable. Here we introduce a transformation technique that converts a significant subset of an SFC to a time Petri net (TPN). Thanks to a large body of literature and mature tools that can analyze TPNs, our technique will bridge the gap between a user-friendly manufacturing control programming tool, the SFC, and powerful analysis tools for TPNs.

This paper presents a novel application of recurrent neural network (RRN) to fault-tolerant control (FTC) of automated sequential manufacturing systems (ASMS) subject to sensor faults. Two RRNs are employed: the first one acts as an I/O relations recognizer and is able to detect faulty sensors and the latter is used as an inverse model of the AMSM to compute the desired control action in a faulty case according to nominal specifications. The learning process of these networks is carried out based on training data generated from the healthy manufacturing system controlled by a programmable logic controller (PLC). Design of the proposed fault-tolerant control system (FTCS) scheme is based on utilizing the two RNNs, a reconfigurable controller and a fault decision subsystem. The design procedure of the proposed FTCS is introduced. The proposed FTCS has been implemented and tested experimentally for a benchmark industrial ASMS subject to single or multiple faulty sensors. Experimental results show the effectiveness of the procedure for a real simple plant. In addition, the results prove these features of the proposed FTCS: (a) effectively improving the faulty control system behaviors, (b) accomplishing its proper functionality in handling single and multiple sensor faults, (c) identifying the sensor faults, and (d) being advantageous in reducing the complexity of the hardware redundancy.

This paper defines a reconfiguration method for the class of discrete-event systems (DES) that is subject to linear constraints as their control specifications. Some existing methods for enforcing these constraints make use of Petri-net P-invariants for controller synthesis. These methods are quite appealing because their computational complexity is much more tractable than most other methods for controller synthesis. However, a common limitation of all existing P-invariant-based control architectures for DES plants is the assumption that the linear constraints defining the control specification of the plant do not change over time. Here, we relax this assumption and allow the control specifications to change during controller runtime. Under certain assumptions on DES behavior, we automatically reconfigure the DES controller after the control specification is changed. In addition, if the current state of the controlled DES has become infeasible under the new control specification, we automatically generate a so-called plant reconfiguration procedure whose execution leads the system back to a feasible state. This reconfiguration procedure is optimal in that it seeks to minimize the cost of reconfiguration actions through an Integer Programming (IP) model. The objective function of the IP model can be used to generate reconfiguration solutions that meet some desired properties. Depending on the cost of each reconfiguration action, a minimum cost reconfiguration solution may use only actions contained in the current plant configuration (an internal response), or ask for a change in the plant configuration, for instance, by adding new resources (an external response), or a combination of both strategies. Finally, we illustrate our method by applying it to a hospital control system example. Note to Practitioners-This paper proposes a dynamic reconfiguration framework that can revise the operations of systems whose control requirements change over time. The proposed framework can be applied to systems that satisfy the following two assumptions. First, the behavior of the system under study is described in terms of a set of discrete states and events. Events will cause the system to transition between states. Second, the control requirements must be expressed by linear equalities and inequalities on the system states. Under these circumstances, the proposed framework can identify an optimal transition to a new control policy that satisfies the new control requirements. Moreover, the system under consideration will continue operating while this transition is taking place. One application of this method is in modifying hospital control strategies when a hospital experiences unexpected events. In this case, the hospital operations-such as patient handling, resource assignment, and procedure scheduling-can be represented by discrete state models (e.g., Petri nets). Constraints on these operations can be modeled by linear inequalities on hospital and patient state. Upon a change in the constraints, the proposed reconfiguration method revises the hospital control strategies. For example, a shift in the hospital service demands (e.g., an increase in the flow of patients to the hospital due to a mass casualty situation) can be translated to changes in the constraints. In this case, the hospital operations must be revised to accommodate the new constraints without disrupting the operation of the hospital. The reconfiguration method of this paper provides a framework for modeling the reconfiguration steps and for calculating the least cost reconfiguration solution.

Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

Determining the appropriate dosage of warfarin is an important yet challenging task. Several prediction models have been proposed to estimate a therapeutic dose for patients. The models are either clinical models which contain clinical and demographic variables or pharmacogenetic models which additionally contain the genetic variables. In this paper, a new methodology for warfarin dosing is proposed. The patients are initially classified into two classes. The first class contains patients who require doses of >30 mg/wk and the second class contains patients who require doses of ≤30 mg/wk. This phase is performed using relevance vector machines. In the second phase, the optimal dose for each patient is predicted by two clinical regression models that are customized for each class of patients. The prediction accuracy of the model was 11.6 in terms of root mean squared error (RMSE) and 8.4 in terms of mean absolute error (MAE). This was 15% and 5% lower than IWPC and Gage models (which are the most widely used models in practice), respectively, in terms of RMSE. In addition, the proposed model was compared with fixed-dose approach of 35 mg/wk, and the model proposed by Sharabiani et al. and its outperformance were proved in terms of both MAE and RMSE.

Despite the efforts in scheduling and control of flexible manufacturing systems (FMSs) with resource constraints, the current pool of scheduling techniques faces two major drawbacks: modeling and complexity. Modeling is the task of converting the FMS data to a set of information, ready to be processed by a scheduling algorithm. Complexity has a direct relation with the amount of effort required to execute a scheduling algorithm successfully on the information set generated in the modeling phase. In this paper, we use finite automata (FA) theory to develop a modeling formalism and its accompanying scheduling algorithm for control and scheduling of FMS with resource constraints. While the FA-based modeling is completely automatic and does not need any human-designer interference, its related algorithm is both effective and efficient. We use IDEF3 standard to capture the FMS activities and resource data. We propose a three-step procedure. In the first step, the IDEF3 data set is converted to a finite automaton, preserving the activity precedence relationships. In the second step, the resulted finite automaton is decomposed to smaller (in size) scheduling problems that can be independently optimized. In the third step, a heuristic scheduling algorithm is used to handle each problem separately. We applied the developed procedure to 100 problems. The results are satisfactory and promising.

In this paper, we introduce the concept of switching from one supervisory control to another one when changes in the state of an embedded sensory network occur. The sensory devices are assumed to be single functional, that is, they are programmed to observe and report only one event. The switching model is built based on the two criteria of language observability and state synchronization. Switching model is the basis for a megacontroller, which monitors the state of the sensory network embedded in the plant as well as the state of the supervisor in charge. Every time the state of this network changes, the megacontroller reconfigures the control system by finding an appropriate supervisor.

In this paper, we develop a framework for reconfiguration of a discrete event system (DES) controller, which has a dynamic event observation set. We will show the designed reconfiguration yields a more tolerable controller than the one designed in [5]. Starting with a maximally permissive controller that has a full observation of its DES, we design a mega-controller that monitors the observation set of the DES controller and its state continuously. Upon a change in the observation set, the mega-controller reconfigures the controller by a aggregation or disaggregation of the controller states. The mega-controller is also responsible for feedback function adjustments if the available observation set causes a conflict in control. We illustrate the reconfiguration procedures by an example.