For the sake of clarity it is important to stress that both approaches require a lot of time for the training process where the algorithm models are then created. At the end of the training process both approaches are able to return classification in a very short time with the great difference that the Deep Recurrent needs about 2 seconds of data collection and also it needs to process the raw data by extracting features in order to perform the classification; whereas the Deep Convolutional needs at most 1.28 seconds of time windows data collection for providing the classification, reaching almost real-time recognition.
3.4
Traffic Control based Future Works
A potential future work that I have preliminary investigated, is able to exploit the implemented TMR solution to bring awareness Traffic Control.
In fact, as already introduced the TMR solution could be adopted by several transportation management providers and in particular by the traffic manage- ment departure.
The TMR solution, presented in the previous paragraphs, permits of identi- fying the transportation means in urban and extra urban area, while people are moving on the streets. For instance, figure 3.18 shows the motion flows of two transportation modalities (Car in blue and Motorbike in orange, and the axis represent the Latitude and Longitude) identified with the Real Time Recognition APP for testing purposes. Starting from the real time recognition
Fig. 3.18 Car and Motorbike Motion Flow in Rome
is possible to identify the people motion flows. The motion flow determines how a recognized transport modality is moving on the streets and with which velocity. In this respect the Real Time recognition APP is able to monitor the speed, Latitude, Longitude and recognize the transport modality.
Recent studies [65] [66] [106] try to improve the efficiency of the traffic lights providing multiple solutions for managing the traffic lights signals. The three mentioned solutions, [65] [66] and [106], to perform the traffic lights control use a Deep Reinforcement Learning [103] approach for controlling the traffic light signals, by using sensors on the streets, camera sensors positioned in proximity of the traffic lights, vehicular networks [42], and traffic data collection.
The most promising and recent approach (at least from my perspective) is described in [66] where the authors propose a Deep Reinforcement Learning solution with a Deep Convolutional Neural Network able to process the real time input data and the Reinforcement Learning algorithm able to decide the timing for the traffic light signals. This approach entails the presence of sensors equipped in each traffic light to monitor the status of the vehicular network as for instance number of vehicles, the location of vehicles, and their waiting time. By using the TMR solution basic input data <position,speed> to perform the traffic lights control as described in [66] can be used with the transportation modality by optimizing the traffic light signals also according to the type of transportation means on the streets. In fact, the basic input data as number of vehicles, the location of vehicles, and their waiting time can be estimate by processing the recognition from real-time TMR solution.
Chapter 4
Deep Model Predictive Control
based techniques
The problem of controlling the glucose level for diabetic patients is the starting point for most of the available treatments. Despite the importance of this problem, the complex dynamics that regulate the evolutions of the glycaemic index and many of connected biological factors, force the solutions commonly implemented in medical devices as for instance artificial pancreas to make several simplifying assumptions. Several different approaches have widely inves- tigated this problem, implementing control solutions spacing from traditional threshold based ones to PID and MPC.
In this chapter a new solution is presented for improving the State-of-the-art algorithms performances and methodologies proposing a work inspired to what developed in [112], [80], and [74]. The proposed solution is based on Bidi- rectional Neural Network and Model Predictive Control techniques aimed at maintaining the blood glucose level within a safe range.
The Deep Bidirectional Neural Network are mainly used for time series pre- diction evaluating the evolution of the blood glucose signal, and the Model Predictive Control scheme is used as optimization strategy for controlling the biological factor of interest (blood glucose level) via insulin injection quantity. The approach reported in this chapter aims to highlight the results obtained with the Deep Neural Network techniques since the Model Predictive Control controller solution is still under a finishing process. Therefore, the MPC solu- tion is presented as objective that will be reached when the work will be indeed completed.
4.1
Blood Glucose Level Control
The design and control of a system dedicated to the blood glucose level con- trol is widely investigated in literature. In [79] the authors have proposed a solution with a model based predictive control algorithm for controlling the normoglycemia in diabetic patients with diabetes of Type 1 by using a closed loop insulin injection; they also develop a compartmental modeling.
The authors in [20] develop a system of a closed-loop control for glyceamic index in diabetes patients by combining the closed-loop control algorithm a glucose sensor and an actuator for insulin infusion. In [92] the authors propose a closed-loop insulin delivery system with an associated subcutaneous glucose sensor and related subcutaneous insulin injection; this solution for controlling the glycaemic index was experimented over 10 patients afflicted by diabetes of type 1.
In [33] the authors have introduced a solution based on a Bergamn nonlinear mathematical model Bergman describing the the plasma insulin injection; a semi closed loop algorithm is presented for the correction of hyperglycemia in diabetic patients. The performances of such an approaches was then evaluated with a simulation for testing the effectiveness.
The glucose control have been also investigated in several other different works, following the methodologies presented achieving important results as described in [60], [96], [83], [6] and [27]. In these works the authors have presented solutions dealing with closed loop control algorithms aimed at achieving desired level of blood glucose level with respect to the actual level; the main problem in closed loop based algorithms is that this methodology adapts its control actions following the biological signals without any prediction of further effects and it is has not optimization properties.
The Model Predictive Control methodologies imposes an iterative open loop op- timization following the residing horizon paradigm, hence bringing the advance of the closed loop control to the optimality of the open loop. The recent and most advanced solution in this field, at least from my knowledge, is presented in [74]; in this work the authors have presented a model predictive control based algorithm for implementing an artificial pancreas for a personalized treatment of type 1 diabetes, providing several personalized control schemes for blood-glucose concentration-regulation. The work in [74] is based on synthetic data, generated with the UVA/PADOVA simulator [71], a tool recognize bu