About
7
Publications
12,048
Reads
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23
Citations
Introduction
Lucas currently works as a research scientist at the Ostschweizer Fachhochschule. He completed his PhD in the Industrial Automation Laboratory (IAL) under supervision of Professor Clarence W. de Silva (Department of Mechanical Engineering) at The University of British Columbia. His research focused on Multi-objective design optimization.
Skills and Expertise
Current institution
Additional affiliations
July 2020 - August 2022
Position
- Software Engineer
Description
- -Development of object oriented software, control concepts and initial operation of test benches, where different types of steering systems are tested -Development of algorithms (Python) that evaluate the quality of a product based on measured data in End Of Line testing -Development of an interface in Python to the PLC so a user can write automatic sequences in Python -Development of a torque control concept including a Kalman Filter -Implementation of micro services, REST APIs, Mqtt services,
February 2016 - December 2016
Position
- Developer
Description
- - Sensor evaluation (radar sensors, IMUs, laser) on a Hexapod platform for active heave compensation of offshore cranes - Software validation of the collision avoidance system of cranes (consideration of crane kinematics) - Implementation of a load measurement system for the Liebherr Reachstacker
March 2015 - September 2015
Position
- Research Associate
Description
- During my time at the Bristol Robotics Laboratory I worked under supervision of Prof. Dr.-Ing. Angelika Peer, on the design of a novel robotic end-effector with the ability to measure force, temperature and humidity. This robotic end-effector is used as part of a telepresence system to palpate the abdominal of a patient. Specific tasks: - Literature research of state of the art robotic telepresence palpation end-effectors - Design of a novel robotic end-effector to measure force, temperature and
Education
January 2017 - May 2020
April 2014 - October 2015
May 2011 - April 2014
Publications
Publications (7)
This paper presents a novel webcam-based approach for gaze estimation
on computer screens. Utilizing appearance based gaze estimation models,
the system provides a method for mapping the gaze vector from the user’s
perspective onto the computer screen. Notably, it determines the user’s 3D
position in front of the screen, using only a 2D webcam with...
For the design process of mechanical or electrical devices it is often necessary to consider multiple objectives. The design problem can then be formulated as multi-objective optimization problem. Multiple objectives can be conflicting and to pick a design solution a trade-off between those is required. A good trade-off is important for a successfu...
The design of mechatronic devices commonly includes qualitative design objectives that can play a significant role in the consumer appeal and the success of the product. This paper presents an approach to incorporate the qualitative design objective comfort into the design process of a wearable device by determining the most suitable locations to m...
This paper presents an approach to optimize multiple design objectives that have qualitative and quantitative design variables, with specific application for a wearable body sensor system. The methodology incorporates a way to group the qualitative and quantitative design variables and the design objectives that are present in the problem and the e...
This thesis shows a multi-modal palpation end-effector with a variety of sensors on its surface. This palpation device is the end-effector for the ReMeDi (Remote Medical Diagnostician) project, a robot system that features medical tele-examination of patients. Palpation is one of the most widespread examination techniques to observe the stiffness of i...
Questions
Question (1)
For example:
max -1/3x1+x2
subject to 1) -x1+x2<=-0.5
2) -0.5x1+x2=0.5
3) 0.5x1+x2<=1.5
In Matrix form
A=[-1 1; -0.5 1; 0.5 1]; b=[-0.5; 0.5; 1.5]; so A*x<=b
In this case constraint number 2) is not needed. The solution will be the same when inequality number 2) is omitted.
In my problem the size of the A matrix is 264x100. Is there a way to find out which constraints or inequalities are not needed?
Basically a way to find unnecessary inequalities for defining a problem?
