Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution

The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect of additional energy savings for hybrid powertrains. Torque split optimal control methodologies have been a focus in the automotive industry and academia for many years. Their real-time application in modern vehicles is, however, still lagging behind. While conventional exact and non-exact optimal control techniques such as Dynamic Programming and Model Predictive Control have been demonstrated, they suffer from the curse of dimensionality and quickly display limitations with high system complexity and highly stochastic environment operation. This paper demonstrates that Neuroevolution associated drive cycle classification algorithms can infer optimal control strategies for any system complexity and environment, hence streamlining and speeding up the control development process. Neuroevolution also circumvents the integration of low fidelity online plant models, further avoiding prohibitive embedded computing requirements and fidelity loss. This brings the prospect of optimal control to complex multi-physics system applications. The methodology presented here covers the development of the drive cycles used to train and validate the neurocontrollers and classifiers, as well as the application of the Neuroevolution process

Georgia Aquarium Design Space Analysis and Optimization

AbstractThe Ocean Voyager exhibit residing at the Georgia Aquarium Inc. (GAI) is one of the largest reef gallon aquariums in the world, with a capacity greater than 6.2M gallons. Reef aquariums are closed systems and must compensate by ‘turning over’ their complete volume of water many times a day through biological, chemical, and mechanical filtration. Due to the Georgia Aquarium being a non-profit organization, GAI sought to investigate ways to maximize efficiency and lower operating costs. This paper will focus on using low-cost software solutions to perform trade space analyses and optimization directed towards the Ocean Voyager exhibit and related GA Aquarium life support and energy systems.The software solution herein demonstrates a top-down System of Systems (SoS) to subsystem modeling approach that provides decision makers with interdisciplinary dashboard-level tools to visualize system design. The goal of the analysis is to provide executive level decision-making support for designing or enhancing existing complex systems and SoS. The analysis was performed as a capstone project by Georgia Tech graduate students progressing from cradle to finish in just 9 weeks to show the benefits of systems engineering to Georgia Aquarium staff. Integrating software SE tools into a single, aggregate model enables project engineers and decision makers to direct design directions with confidence

Feasibility and Effectiveness of Basic Lymphedema Management in Leogane, Haiti, an Area Endemic for Bancroftian Filariasis

Lymphatic filariasis is a parasitic disease that is spread by mosquitoes. In tropical countries where lymphatic filariasis occurs, approximately 14 million people suffer from chronic swelling of the leg, known as lymphedema. Repeated episodes of bacterial skin infection (acute attacks) cause lymphedema to progress to its disfiguring form, elephantiasis. To help achieve the goal of eliminating lymphatic filariasis globally, the World Health Organization recommends basic lymphedema management, which emphasizes hygiene, skin care, exercise, and leg elevation. Its effectiveness in reducing acute attack frequency, as well as the role of compressive bandaging, have not been adequately evaluated in filariasis-endemic areas. Between 1995 and 1998, we studied 175 people with lymphedema of the leg in Leogane, Haiti. During Phase I of the study, when compression bandaging was used to reduce leg volume, the average acute attack rate was 1.56 episodes per year; it was greater in people who were illiterate and those who used compression bandages. After March 1997, when hygiene and skin care were emphasized and bandaging discouraged, acute attack frequency significantly decreased to 0.48 episodes per year. This study highlights the effectiveness of hygiene and skin care, as well as limitations of compressive bandaging, in managing lymphedema in filariasis-endemic areas

Neuroevolution Application to Collaborative and Heuristics-Based Connected and Autonomous Vehicle Cohort Simulation at Uncontrolled Intersection

Artificial intelligence is gaining tremendous attractiveness and showing great success in solving various problems, such as simplifying optimal control derivation. This work focuses on the application of Neuroevolution to the control of Connected and Autonomous Vehicle (CAV) cohorts operating at uncontrolled intersections. The proposed method implementation’s simplicity, thanks to the inclusion of heuristics and effective real-time performance are demonstrated. The resulting architecture achieves nearly ideal operating conditions in keeping the average speeds close to the speed limit. It achieves twice as high mean speed throughput as a controlled intersection, hence enabling lower travel time and mitigating energy inefficiencies from stop-and-go vehicle dynamics. Low deviation from the road speed limit is hence continuously sustained for cohorts of at most 50 m long. This limitation can be mitigated with additional lanes that the cohorts can split into. The concept also allows the testing and implementation of fast-turning lanes by simply replicating and reconnecting the control architecture at each new road crossing, enabling high scalability for complex road network analysis. The controller is also successfully validated within a high-fidelity vehicle dynamic environment, showing its potential for driverless vehicle control in addition to offering a new traffic control simulation model for future autonomous operation studies

Connected and Autonomous Vehicle Cohort Speed Control Optimization via Neuroevolution

Predictive Energy Management (PrEM) research is at the forefront of modern transportation\u27s energy consumption reduction efforts. The development of PrEM optimization algorithms has been tailored to selfish vehicle operation and implemented in the form of vehicle dynamics and/or adaptive powertrain control functions. With the progress in vehicle automation, this paper focuses on extending PrEM into the realm of a System of Systems (SoS). The proposed approach uses the shared information among Connected and Automated Vehicles (CAV) and the infrastructure to synthesize a reduced energy speed trajectory at the cohort level within urban environments. Neuroevolution is employed to incorporate a generalized optimum controller, robust to the emergent behaviors typical of multi-agents SoS. The authors demonstrated the use of heuristics and systems engineering processes in abstracting and integrating the resulting neural network within the control architecture, which enables novel added-value features such as green wave pass/fail classification and e-Horizon velocity prediction. The resulting controller is faster than real-time and was validated with a multi-agent simulation environment and on a real-world closed-loop track at the American Center for Mobility (ACM). The GM Bolt and Volt CAV mixed cohort testing at ACM demonstrated energy reductions from 7% to 22% depending on scenarios

Deliver Signal Phase and Timing (SPAT) for Energy Optimization of Vehicle Cohort Via Cloud-Computing and LTE Communications

Predictive Signal Phase and Timing (SPAT) message set is one fundamental building block for vehicle-to-infrastructure (V2I) applications such as Eco-Approach and Departure (EAD) at traffic signal controlled urban intersections. Among the two complementary communication methods namely short-range sidelink (PC5) and long-range cellular radio link (Uu), this paper documents the work with long-range link: the complete data chain includes connecting to the traffic signals via existing backhaul communication network, collecting the raw signal phase state data, predicting the signal state changes and delivering the SPAT data via a geofenced service to requests over HTTP protocols. An Application Programming Interface (API) library is developed to support various cellular data transmission reduction and latency improvement techniques. An emulation-based algorithm is applied to predict the traffic signal state changes to provide adequate prediction horizon (e.g., at minimum 2 minutes) for the cohort energy optimization. In fact, the same connectivity and SPAT delivery methodology has been applied to traffic signalized intersections nationwide in the United States upon public agency approvals for access to their firewalled traffic control network and signal control systems or directly to individual controllers. This methodology proves its effectiveness and potential for rapid growth of such SPAT deliveries at mass production scale without needing infrastructure hardware retrofit or excessive communication means. To support the energy optimization of light and heavy-duty vehicle cohorts of mixed automation and propulsion systems (EV, ICE and hybrid), the connection and SPAT deliveries at two sites were completed, including public roads in Washtenaw County, Michigan and closed track test sites at American Center for Mobility (ACM) in Ypsilanti, Michigan. However, only closed test track results at ACM will be presented in this paper. A neuroevolution based optimizer is developed and implemented to control the speed of a vehicle cohort with different propulsion systems and automation levels. Closed track tests showed significant energy savings of the cohort operation

Mutations in the latent TGF-beta binding protein 3 (LTBP3) gene cause brachyolmia with amelogenesis imperfecta

Inherited dental malformations constitute a clinically and genetically heterogeneous group of disorders. Here, we report on four families, three of them consanguineous, with an identical phenotype, characterized by significant short stature with brachyolmia and hypoplastic amelogenesis imperfecta (AI) with almost absent enamel. This phenotype was first described in 1996 by Verloes et al. as an autosomal recessive form of brachyolmia associated with AI. Whole-exome sequencing resulted in the identification of recessive hypomorphic mutations including deletion, nonsense and splice mutations, in the LTBP3 gene, which is involved in the TGF-beta signaling pathway. We further investigated gene expression during mouse development and tooth formation. Differentiated ameloblasts synthesizing enamel matrix proteins and odontoblasts expressed the gene. Study of an available knockout mouse model showed that the mutant mice displayed very thin to absent enamel in both incisors and molars, hereby recapitulating the AI phenotype in the human disorder