arXiv subjects

Zhenyu Pan

Publications and source records attributed to Zhenyu Pan.

22 records · Page 2Linked to original sources

arXiv2019

Photo-thermoelectric properties and their use in study of transport properties of both carriers from a single bulk sample

We describe a theory on photo-thermoelectric properties of a semiconductor, which include photo-conductivity, photo-Seebeck coefficient, and photo-Hall effect. We demonstrate that these properties provide a powerful tool for the study of carrier transport in semiconductors. Even though photo-carrier generation is a complicated process which often prohibits quantitative analysis as their species or numbers are not known. Using bulk samples seems even less likely as the photo-carrier only affect a thin layer. Our method will allow researchers to bypass these difficulties, to use only measured properties and determine both electron and hole mobilities as well as the ratio between electrons and holes from a bulk sample. We provide initial experiment verification of our theory in the end using two distinctively different semiconductors.

cond-mat.mtrl-sci↗

arXiv2019

Tackling Challenges in Seebeck Coefficient Measurement of Ultra-High Resistance Samples with an AC Technique

Seebeck coefficient is a widely-studied semiconductor property. Conventional Seebeck coefficient measurements are based on DC voltage measurement. Normally this is performed on samples with low resistances below a few Mohm level. Meanwhile, certain semiconductors are highly intrinsic and resistive, many examples can be found in optical and photovoltaic materials. The hybrid halide perovskites that have gained extensive attention recently are a good example. Few credible studies exist on the Seebeck coefficient of, CH3NH3PbI3, for example. We report here an AC technique based Seebeck coefficient measurement, which makes high quality voltage measurement on samples with resistances up to 100Gohm. This is achieved through a specifically designed setup to enhance sample isolation and reduce meter loading. As a demonstration, we performed Seebeck coefficient measurement of a CH3NH3PbI3 thin film at dark and found S = +550 microV/K. Such property of this material has not been successfully studied before.

physics.app-ph↗

arXiv2019

Bayesian Grasp: Robotic visual stable grasp based on prior tactile knowledge

Robotic grasp detection is a fundamental capability for intelligent manipulation in unstructured environments. Previous work mainly employed visual and tactile fusion to achieve stable grasp, while, the whole process depending heavily on regrasping, which wastes much time to regulate and evaluate. We propose a novel way to improve robotic grasping: by using learned tactile knowledge, a robot can achieve a stable grasp from an image. First, we construct a prior tactile knowledge learning framework with novel grasp quality metric which is determined by measuring its resistance to external perturbations. Second, we propose a multi-phases Bayesian Grasp architecture to generate stable grasp configurations through a single RGB image based on prior tactile knowledge. Results show that this framework can classify the outcome of grasps with an average accuracy of 86% on known objects and 79% on novel objects. The prior tactile knowledge improves the successful rate of 55% over traditional vision-based strategies.

cs.RO↗

arXiv2019

Object-Agnostic Suction Grasp Affordance Detection in Dense Cluster Using Self-Supervised Learning.docx

In this paper we study grasp problem in dense cluster, a challenging task in warehouse logistics scenario. By introducing a two-step robust suction affordance detection method, we focus on using vacuum suction pad to clear up a box filled with seen and unseen objects. Two CNN based neural networks are proposed. A Fast Region Estimation Network (FRE-Net) predicts which region contains pickable objects, and a Suction Grasp Point Affordance network (SGPA-Net) determines which point in that region is pickable. So as to enable such two networks, we design a self-supervised learning pipeline to accumulate data, train and test the performance of our method. In both virtual and real environment, within 1500 picks (~5 hours), we reach a picking accuracy of 95% for known objects and 90% for unseen objects with similar geometry features.

cs.RO↗