Stochastic MPC Design for a Two-Component Granulation Process

We address the issue of control of a stochastic two-component granulation process in pharmaceutical applications through using Stochastic Model Predictive Control (SMPC) and model reduction to obtain the desired particle distribution. We first use the method of moments to reduce the governing integro-differential equation down to a nonlinear ordinary differential equation (ODE). This reduced-order model is employed in the SMPC formulation. The probabilistic constraints in this formulation keep the variance of particles' drug concentration in an admissible range. To solve the resulting stochastic optimization problem, we first employ polynomial chaos expansion to obtain the Probability Distribution Function (PDF) of the future state variables using the uncertain variables' distributions. As a result, the original stochastic optimization problem for a particulate system is converted to a deterministic dynamic optimization. This approximation lessens the computation burden of the controller and makes its real time application possible.Comment: American control Conference, May, 201

COMPLEX HUMAN AUDITORY PERCEPTION AND SIMULATED SOUND PERFORMANCE PREDICTION

This paper reports an investigation into the degree of consistency between three different methods of sound performance evaluation through studying the performance of a built project as a case study. The non-controlled office environment with natural human speech as a source was selected for the subjective experiment and ODEON room acoustics modelling software was applied for digital simulation. The results indicate that although each participant may interpret and perceive sound in a particular way, the simulation can pre- dict this complexity to some extent to help architects in designing acoustically better spaces. Also the results imply that architects can make valid comparative evaluations of their designs in an architecturally intuitive way, using architectural language. The research acknowledges that complicated engineering approaches to subjective analysis and to controlling the test environment and participants is difficult for architects to comprehend and implement

HySenSe: A Hyper-Sensitive and High-Fidelity Vision-Based Tactile Sensor

In this paper, to address the sensitivity and durability trade-off of Vision-based Tactile Sensor (VTSs), we introduce a hyper-sensitive and high-fidelity VTS called HySenSe. We demonstrate that by solely changing one step during the fabrication of the gel layer of the GelSight sensor (as the most well-known VTS), we can substantially improve its sensitivity and durability. Our experimental results clearly demonstrate the outperformance of the HySenSe compared with a similar GelSight sensor in detecting textural details of various objects under identical experimental conditions and low interaction forces (<= 1.5 N).Comment: Accepted to IEEE Sensors 2022 Conferenc

Complex human auditory perception and simulated sound performance prediction: A case study for investigating methods of sound performance evaluations and corresponding relationship

© 2016, The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong. This paper reports an investigation into the degree of consistency between three different methods of sound performance evaluation through studying the performance of a built project as a case study. The non-controlled office environment with natural human speech as a source was selected for the subjective experiment and ODEON room acoustics modelling software was applied for digital simulation. The results indicate that although each participant may interpret and perceive sound in a particular way, the simulation can predict this complexity to some extent to help architects in designing acoustically better spaces. Also the results imply that architects can make valid comparative evaluations of their designs in an architecturally intuitive way, using architectural language. The research acknowledges that complicated engineering approaches to subjective analysis and to controlling the test environment and participants is difficult for architects to comprehend and implement

Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing

In this study, to address the current high earlydetection miss rate of colorectal cancer (CRC) polyps, we explore the potentials of utilizing transfer learning and machine learning (ML) classifiers to precisely and sensitively classify the type of CRC polyps. Instead of using the common colonoscopic images, we applied three different ML algorithms on the 3D textural image outputs of a unique vision-based surface tactile sensor (VS-TS). To collect realistic textural images of CRC polyps for training the utilized ML classifiers and evaluating their performance, we first designed and additively manufactured 48 types of realistic polyp phantoms with different hardness, type, and textures. Next, the performance of the used three ML algorithms in classifying the type of fabricated polyps was quantitatively evaluated using various statistical metrics.Comment: Accepted to IEEE Sensors 2022 Conferenc