WIOMAX has top researchers in the fields of data-driven, smart modeling and optimization solutions for hard computational and real-world problems. Our scientists and engineers have performed cutting-edge research and development in several areas.
Our Co-Founder, Dr. Xie, is the lead inventor of Smart and Scalable URban TRAffic Control (SURTRAC), which is a real-time, adaptive traffic control system that improves dynamic traffic flows in urban road networks.
He has created the core control engine (the brain) of SURTRAC, including schedule-driven intersection control and decentralized coordination mechanisms. He has also worked on strengthening strategies of SURTRAC to enable its operations by coping with real-world traffic challenges in urban environment.
Our goal is to analyze the relative performance of alternative route choice models as different assumptions are made about the type of traffic control in use in the urban road network. To this end, we deﬁne a uniﬁed agent-based framework for formulating different route choice models, and integrate this framework with a microscopic trafﬁc simulation environment. Within this framework, each agent’s memory is updated repeatedly (daily) to reﬂect available prior individual and social experience, and then a route is chosen by a probabilistic sequential decision-making process that is a function of the agent’s updated current memory.
We have focused on improving the broader mobility of all modes of traffic that are central to sustainable urban living – especially pedestrians – in an integrated way, specifically on optimizing the delay tradeoffs between vehicles and pedestrians. Three basic extensions are introduced and evaluated on a real-world road network. Tests are performed in the field at selected intersections of the target road network, to demonstrate the effectiveness of the approach in operation.
We present a study combining participatory and physical sensing data, based on location-based checkins collected in the Pittsburgh metropolitan area, and vehicle records collected in a sub-area controlled by an adaptive urban traffic control system. Our aim is to disclose how we could utilize the combined data for a better understanding on urban mobility networks and activity patterns in urban environments, and how we may take advantage of such combined data to improve urban mobility applications such as anomaly traffic detection and reasoning, topic-based nontrivial traffic information extraction, and traffic demand analysis.
We have designed an agent-mediated approach to on-demand e-business supply chain integration. Each agent works as a service broker, exploring individual service decisions as well as interacting with each other for achieving compatibility and coherence among the decisions of all services. Based on the multi-agent constraint-based framework, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach.
In Apache OpenOffice, by default Calc ships with a solver engine for linear programming only. This allows the optimization of models to a certain degree. However, if the formulas or constraints become more complex, nonlinear programming is required.
That missing gap is filled by the Solver for Nonlinear Programming extension. It incorporates two algorithms, SCO and DEPSO, which are able to handle floating point and integer variables as well as nonlinear constraints.
Both algorithms include work from Dr. Xie, who did a lot of research in this area.
Dr. Xie was the developer of first commercial version of Model Quality Assurance (MQA), an expert system to automate the circuit model QA procedure and to link advance process technologies and design success. This system was developed for Accelicon (now acquired by Keysight Technologies, Inc NYSE:KEYS).
We presented an implicit solvent coarse-grained (CG) model for quantitative simulations. The effects of reduced molecular friction and more efficient integration combine to an overall speedup of three to four orders of magnitude compared to all-atom bilayer simulations. Despite an improved computational efficiency, the model preserves chemical specificity and quantitative accuracy. Our CG model is especially useful for studies of large-scale phenomena in membranes which nevertheless require a fair description of chemical specificity.