Algorithmic Synthesis Laboratory (ASL) investigates theories and methods for modeling, abstraction, and algorithmic synthesis of mechanical, industrial, and biomedical systems. We emphasize the mathematical abstraction rooted on the fundamental understanding the target systems and the algorithmic generalization utilizing the tools in mechanical, industrial, computer science and engineering. Application domains range from mechanical and industrial engineering to biomedical and pharmaceutical engineering. Below are the research topics we currently investigate. Please click here to see some of our recent work. Click here to learn more about the forthcoming NSF I/UCRC Center for Assembly Research.
Mechanical/Industrial Systems
Adaptive Solar Power Concentrator System for Efficient and Robust Hydrogen Production
Solar power is a promising renewable energy source for hydrogen production for fuel-cell vehicles. Solar power production is beset however by challenges in investment costs, power efficiency, variability in irradiance over daily and seasonal cycles, as well as maintenance costs. This project aims to develop an adaptive solar power concentration system employing state of the art technologies such as spectral beam splitting, thermal storage and hybrid thermal-photovoltaic power generation in order to overcome many of the cost and efficiency challenges
Customer Driven Co-Design of Complex Products
In order to design innovative products, companies are involving customers in product design process through co-design. Co-design is changing the conventional design processes and replacing them with design processes that make customers an integral and active member of the design team. This co-operation between customers and companies has resulted in some very successful product designs. Nevertheless, co-design is still limited to very specific types of products. The research work will develop modified product design processes that are suitable for co-design and measure their success in innovative designs of complex products.
DFIP: Design for IP Protection
Component outsourcing is often practiced in today’s markets in order to reduce the overall cost of products and maximized profitability. However, when new innovative products are introduced into markets, there is the concern that outsourcing would cause the product’s details to be reviled, and ‘similar’ products finding their way into the market. This research aims at developing a framework for product functionality decomposition and selective outsourcing in order to protect the key competencies and intellectual property, while satisfying quality, cost and lead-time requirements.
Optimally Adaptive Product and Supply chain Systems under Severe Uncertainty
Currently, manufacturing enterprises in the high competitive global markets are vulnerable to uncertainties arising from disruptive events such as natural disasters, new technologies, or changes in environmental regulations. It is highly desirable to invest in resilience and flexibility so as to make a decision before the disruptions. The objective of the research is to enable a manufacturing enterprise to proactively adapt the product designs and the supply chains in anticipation of unplanned, but foreseeable high impact events, while still maintaining the competitive product quality, cost, and lead-time. This work is done in collaboration with Dr. Lalit Patil in Department of Mechanical Engineering.
University of Michigan Solar Car Team
Well, this is not exactly a research topic, but I am the Engineering Faculty Advisor for the University of Michigan Solar Car Team. Click here to learn more about the team's activities. Photo courtesy of UM Solar Car Team. Copyright The University of Michigan Solar Cart Team, all rights reserved.
Biomedical/Pharmaceutical Systems
ChemReader : Automated Annotation of Chemical Structural Database
The goal of this project is to develop an automated system annotating chemical database, ChemReader for recognizing chemical structure diagrams in research articles and linking them with entries in the database. ChemReader system consists of mainly four steps: 1) Document segmentation building logical relationships of objects and elements in the input literature and natural language processing of text components to extract the contextual scientific knowledge, 2) Classification of chemical structure diagrams from documents objects or elements, 3) Recognition step converting classified chemical structure diagrams into standard, searchable chemical file formats, and 4) Annotation of chemical database entries which match to extracted structures of step 3) with obtained knowledge at the step 1). By annotating each molecule in the database with one or more relevant links to the scientific literature, the database would be a more useful resource to bio/chemical research scientists. This work is done in collaboration with Prof. Gus Rosania in Department of Pharmaceutical Sciences.
Image-Guided Intensity-Modulated Radiation Therapy (IMRT)
Intensity-modulated radiation therapy (IMRT) is now a well-established cancer treatment technique for delivering highly conformal dose to tumors while sparing normal tissues and organs. However, the quality of IMRT is limited by daily treatment uncertainties, e.g., registration error, tumor deformation. With the recent development of imaging techniques, image-guided radiation therapy has been rapidly developed to address these uncertainties. The goal of our research is to develop an image guidance strategy for coping with the uncertainties. This work is done in collaboration with Prof. Benedick A. Fraass and Dr. Kenneth Jee in Department of Radiation Oncology.
Reduced Order Protein Model for Flexible Docking
In this project, we are developing a reduced-order protein model which can represent backbone flexibility for molecular docking simulation. In order to build a reduced dynamic model in docking simulation, an enhanced conformational sampling strategy which attempts to visit bound state conformations as many as possible within a limited sampling time has been studied. With different solvent conditions, the protein can show quiet different structural and dynamical properties during MD simulation because solvent molecules contribute on entropy including dielectric fluctuation and hydrophobic effect, as well as enthalpy of the entire system. Once a number of bound state conformations are sampled, a few dynamic modes approximating structural variations of bound conformations are extracted using Principal Component Analysis (PCA). With a surrogate model for protein energy surface in a subspace spanned by extracted principal modes, the protein reduced order model enables a docking simulation allowing protein’s backbone motion effectively.
