ME-EM 2014-15 Annual Report

Table of Contents Human-centered Engineering Endowed Department Chair Enrollment & Degrees Graduates Department News Faculty & Staff Alumni Donors Contracts & Grants Patents & Publications Faculty & Staff Directoryhttps://digitalcommons.mtu.edu/mechanical-annualreports/1004/thumbnail.jp

Deep Learning for Abstraction, Control and Monitoring of Complex Cyber-Physical Systems

Cyber-Physical Systems (CPS) consist of digital devices that interact with some physical components. Their popularity and complexity are growing exponentially, giving birth to new, previously unexplored, safety-critical application domains. As CPS permeate our daily lives, it becomes imperative to reason about their reliability. Formal methods provide rigorous techniques for verification, control and synthesis of safe and reliable CPS. However, these methods do not scale with the complexity of the system, thus their applicability to real-world problems is limited. A promising strategy is to leverage deep learning techniques to tackle the scalability issue of formal methods, transforming unfeasible problems into approximately solvable ones. The approximate models are trained over observations which are solutions of the formal problem. In this thesis, we focus on the following tasks, which are computationally challenging: the modeling and the simulation of a complex stochastic model, the design of a safe and robust control policy for a system acting in a highly uncertain environment and the runtime verification problem under full or partial observability. Our approaches, based on deep learning, are indeed applicable to real-world complex and safety-critical systems acting under strict real-time constraints and in presence of a significant amount of uncertainty.Cyber-Physical Systems (CPS) consist of digital devices that interact with some physical components. Their popularity and complexity are growing exponentially, giving birth to new, previously unexplored, safety-critical application domains. As CPS permeate our daily lives, it becomes imperative to reason about their reliability. Formal methods provide rigorous techniques for verification, control and synthesis of safe and reliable CPS. However, these methods do not scale with the complexity of the system, thus their applicability to real-world problems is limited. A promising strategy is to leverage deep learning techniques to tackle the scalability issue of formal methods, transforming unfeasible problems into approximately solvable ones. The approximate models are trained over observations which are solutions of the formal problem. In this thesis, we focus on the following tasks, which are computationally challenging: the modeling and the simulation of a complex stochastic model, the design of a safe and robust control policy for a system acting in a highly uncertain environment and the runtime verification problem under full or partial observability. Our approaches, based on deep learning, are indeed applicable to real-world complex and safety-critical systems acting under strict real-time constraints and in presence of a significant amount of uncertainty

Seat belt control : from modeling to experiment

In the last decades, vehicle safety has improved considerably. For example, major improvements have been made in the area of the structural crashworthiness of the vehicle, various driver assistance systems have been developed, and enhancements can be found in the restraint systems, the final line of defense in occupant protection. Despite this increase of vehicle safety measures, many fatalities still occur in road transportation. Regarding the unavoidable crashes, a significant amount can be attributed to the fact that the seat belt system does not perform optimally. No crash event or occupant is identical, yet conventional seat belts are – in general – not able to adjust their characteristics accordingly. The system is therefore optimal for only a limited number of crash scenarios and occupant types. With the current sensor and processor technology, it may be possible to develop a seat belt that continuously adapts to the actual crash and occupant conditions. Such a device is referred to as a Continuous Restraint Control (CRC) system, and the work presented in this thesis contributes to the development of this type of systems. The main idea of seat belt control is to add sensors and actuators to the seat belt system. The force in the seat belt is prescribed by the actuator during the crash, such that the risk of injuries are minimized given the current impact severity and occupant size and position. This concept poses several technological challenges, which are in this thesis divided into four research topics. Although many sensor technologies exist nowadays, so far no methods have been proposed to measure the occupant injury responses in real-time. These responses are essential when deciding on the optimal belt force. In this thesis, a solution has been presented for the problem of real-time estimation of (thoracic) injuries and occupant position during a crash. An estimation is performed based on modelbased filtering of a small number of readily available and cheap sensors. Simulation results with a crash victim model indicate that the injury responses can be estimated with sufficient accuracy for control purposes, but that the estimation heavily depends on the accuracy of the model used in the filter. A numerical controller uses these estimated injury responses to compute the optimal seat belt force. In this computation, it has to be taken into account that the occupant position is constrained during the crash by the available space in the vehicle, since contact with the interior may result in serious injury. The controller therefore has to predict the future occupant motion, using a prediction of the future crash behavior, a choice for the future seat belt force, and a model of the vehicle-occupant-belt system. Given the type of control problem, a Model Predictive Control (MPC) approach is used to develop the controller. Simulation results with crash victim models indicate that using this controller lead to a significant injury risk reduction for the thorax, given that an ideal belt actuator is available. The injury estimator, the prediction and control algorithm proposed in the foregoing are designed with simple mathematical models of occupant, seat belt and vehicle interior. It is therefore recognized that such accurate, manageable models are essential in the development of CRC systems. In this thesis, models of various complexities have been constructed that represent three types of widely used crash test dummies. These models are validated against both numerical as experimental data. The conclusion of this validation is that in frontal crashes, the neck and thoracic injury criteria can well be described by linear (time-invariant) models. However, when the models are to be used in the design of a belt control system, more attention has to be given to the modeling of the chest and seat belt. The severity and duration of a typical impact require a seat belt actuator with challenging specifications. For example, it has to deliver very high forces over a large stroke, it must have a high bandwidth, and must be small enough to be fitted in a vehicle post. These devices do not yet exist. In this thesis, a semi-active belt actuator concept is presented. It is based on a pressure-controlled hydraulic valve, which regulates the belt force through an hydraulic cylinder. The actuator is designed and constructed at the TU/e, and evaluated experimentally. Moreover, a moving sled setup has been developed which allows testing the actuator under impact conditions. Experimental results show that the belt actuator meets the requirements, except for the maximum force. The actuator can therefore at this point be used to prescribe belt forces in a safety belt in low-speed impacts

Real-time spatial modeling to detect and track resources on construction sites

For more than 10 years the U.S. construction industry has experienced over 1,000 fatalities annually. Many fatalities may have been prevented had the individuals and equipment involved been more aware of and alert to the physical state of the environment around them. Awareness may be improved by automatic 3D (three-dimensional) sensing and modeling of the job site environment in real-time. Existing 3D modeling approaches based on range scanning techniques are capable of modeling static objects only, and thus cannot model in real-time dynamic objects in an environment comprised of moving humans, equipment, and materials. Emerging prototype 3D video range cameras offer another alternative by facilitating affordable, wide field of view, automated static and dynamic object detection and tracking at frame rates better than 1Hz (real-time). This dissertation presents an imperical work and methodology to rapidly create a spatial model of construction sites and in particular to detect, model, and track the position, dimension, direction, and velocity of static and moving project resources in real-time, based on range data obtained from a three-dimensional video range camera in a static or moving position. Existing construction site 3D modeling approaches based on optical range sensing technologies (laser scanners, rangefinders, etc.) and 3D modeling approaches (dense, sparse, etc.) that offered potential solutions for this research are reviewed. The choice of an emerging sensing tool and preliminary experiments with this prototype sensing technology are discussed. These findings led to the development of a range data processing algorithm based on three-dimensional occupancy grids which is demonstrated in detail. Testing and validation of the proposed algorithms have been conducted to quantify the performance of sensor and algorithm through extensive experimentation involving static and moving objects. Experiments in indoor laboratory and outdoor construction environments have been conducted with construction resources such as humans, equipment, materials, or structures to verify the accuracy of the occupancy grid modeling approach. Results show that modeling objects and measuring their position, dimension, direction, and speed had an accuracy level compatible to the requirements of active safety features for construction. Results demonstrate that video rate 3D data acquisition and analysis of construction environments can support effective detection, tracking, and convex hull modeling of objects. Exploiting rapidly generated three-dimensional models for improved visualization, communications, and process control has inherent value, broad application, and potential impact, e.g. as-built vs. as-planned comparison, condition assessment, maintenance, operations, and construction activities control. In combination with effective management practices, this sensing approach has the potential to assist equipment operators to avoid incidents that result in reduce human injury, death, or collateral damage on construction sites.Civil, Architectural, and Environmental Engineerin

Publications of the Jet Propulsion Laboratory, 1988

This bibliography describes and indexes by primary author the externally distributed technical reporting, released during calendar year 1988, that resulted from scientific and engineering work performed, or managed, by the Jet Propulsion Laboratory. Three classes of publications are included: JPL publications in which the information is complete for a specific accomplishment; articles from the quarterly Telecommunications and Data Acquisition (TDA) Progress Report; and articles published in the open literature

DEVELOPMENT AND OPERATION OF A MOBILE TEST FACILITY FOR EDUCATION

The automotive industry saw a large shift towards vehicle electrification after the turn of the century. It became necessary to ensure that new and existing engineers were qualified to design and calibrate these new systems. To ensure this training, Michigan Tech received a grant to develop a curriculum based around vehicle electrification. As part of this agenda, the Michigan Tech Mobile Laboratory was developed to provide hands-on training for professional engineers and technicians in hybrid electric vehicles and vehicle electrification. The Mobile Lab has since then increased the scope of the delivered curriculum to include other automotive areas and even customizable course content to meet specific needs. This thesis outlines the development of the Mobile Laboratory and its powertrain test facilities. The focus of this thesis is to discuss the different hardware and software systems within the lab and test cells. Detailed instructions on the operation and maintenance of each of the systems are discussed. In addition, this thesis outlines the setup and operation of the necessary equipment for several of the experiments for the on and off campus courses and seminars

Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization

In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems

Dual-layered Multi-Objective Genetic Algorithms (D-MOGA): A Robust Solution for Modern Engine Development and Calibrations

Heavy-duty (HD) diesel engines are the primary propulsion systems used within the freight transportation sector and are subjected to stringent emissions regulations. The primary objective of this study is to develop a robust calibration technique for HD engine optimization in order to meet current and future regulated emissions standards during certification cycles and off-cycle vocation activities. Recently, California - Air Resources Board (C-ARB) has also shown interests in controlling off-cycle emissions from vehicles operating in California by funding projects such as the Ultra-Low NOx study by Sharp et. al [1]. Moreover, there is a major push for the complex real-world driving emissions testing protocol as the confirmatory and certification testing procedure in Europe and Asia through the United Nations - Economic Commission for Europe (UN-ECE) and International Organization for Standardization (ISO). This calls for more advanced and innovative approaches to optimize engine operation to meet the regulated certification levels.;A robust engine calibration technique was developed using dual-layered multi-objective genetic algorithms (D-MOGA) to determine necessary engine control parameter settings. The study focused on reducing fuel consumption and lowering oxides of nitrogen (NOx) emissions, while simultaneously increasing exhaust temperatures for thermal management of exhaust after-treatment system. The study also focused on using D-MOGA to develop a calibration routine that simultaneously calibrates engine control parameters for transient certification cycles and vocational drayage operation. Several objective functions and alternate selection techniques for D-MOGA were analyzed to improve the optimality of the D-MOGA results.;The Low-NOx calibration for the Federal Test Procedure (FTP) which was obtained using the simple desirability approach was validated in the engine dynamometer test cell over the FTP and near-dock test cycles. In addition, the 2010 emissions compliant calibration was baselined for performance and emissions over the FTP and custom developed low-load Near-Dock engine dynamometer test cycles. Performance and emissions of the baseline calibrations showed a 63% increase in engine-out brake-specific NOx emissions and a proportionate 77% decrease in engine-out soot emissions over the Near-Dock cycle as compared to the FTP cycle. Engine dynamometer validation results of the Low-NOx FTP cycle calibration developed using D-MOGA, showed a 17% increase brake-specific NOx emissions over the FTP cycle, compared to the baseline calibrations. However, a 50% decrease in engine-out soot emissions and substantial increase in exhaust temperature were observed with no penalties on fuel consumption.;The tools developed in this study can play a role in meeting current and future regulations as well as bridging the gap between emissions during certification and real-world engine operations and eventually could play a vital role in meeting the National Ambient Air Quality Standards (NAAQS) in areas such as the port of Los Angeles, California in the South Coast Air Basin