A Remote Sim2real Aerial Competition: Fostering Reproducibility and Solutions' Diversity in Robotics Challenges

Shared benchmark problems have historically been a fundamental driver of progress for scientific communities. In the context of academic conferences, competitions offer the opportunity to researchers with different origins, backgrounds, and levels of seniority to quantitatively compare their ideas. In robotics, a hot and challenging topic is sim2real-porting approaches that work well in simulation to real robot hardware. In our case, creating a hybrid competition with both simulation and real robot components was also dictated by the uncertainties around travel and logistics in the post-COVID-19 world. Hence, this article motivates and describes an aerial sim2real robot competition that ran during the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, from the specification of the competition task, to the details of the software infrastructure supporting simulation and real-life experiments, to the approaches of the top-placed teams and the lessons learned by participants and organizers.Comment: 13 pages, 16 figures, 4 table

Attitudes on the donation of human embryos for stem cell research among Chinese IVF patients and students

Bioethical debates on the use of human embryos and oocytes for stem cell research have often been criticized for the lack of empirical insights into the perceptions and experiences of the women and couples who are asked to donate these tissues in the IVF clinic. Empirical studies that have investigated the attitudes of IVF patients and citizens on the (potential) donation of their embryos and oocytes have been scarce and have focused predominantly on the situation in Europe and Australia. This article examines the viewpoints on the donation of embryos for stem cell research among IVF patients and students in China. Research into the perceptions of patients is based on in-depth interviews with IVF patients and IVF clinicians. Research into the attitudes of students is based on a quantitative survey study (n=427). The empirical findings in this paper indicate that perceptions of the donation of human embryos for stem cell research in China are far more diverse and complex than has commonly been suggested. Claims that ethical concerns regarding the donation and use of embryos and oocytes for stem cell research are typical for Western societies but absent in China cannot be upheld. The article shows that research into the situated perceptions and cultural specificities of human tissue donation can play a crucial role in the deconstruction of politicized bioethical argumentation and the (often ill-informed) assumptions about “others” that underlie socio-ethical debates on the moral dilemmas of technology developments in the life sciences

Reducing the error of sampled-data nonlinear observers with numerical approaches

Conference Name:2013 10th IEEE International Conference on Control and Automation, ICCA 2013. Conference Address: Hangzhou, China. Time:June 12, 2013 - June 14, 2013.The problem of how to reduce the error of sampled-data nonlinear observers is investigated. A two-step design approach is proposed, in which the feedback term of observer is designed in continuous-time while an accurate numerical approach is used to estimate the state at next sampling instance. Main theorem is proved to guarantee that the observer error decays to and remains in a bounded ball contains the origin and the bound can be controlled by the numerical approach. Compared with approximate discrete-time design method, the two-step approach can have the same observer precision while the design is relatively easy because the continuous-time design methods can be used directly. ? 2013 IEEE

Active Disturbance Rejection Control for Discrete Systems

A form of active disturbance rejection control (ADRC) is proposed for a kind of discrete systems in this paper. The state space form of the system is firstly formulated. Then the extended state observers (ESO) with and without model information are presented to estimate states and total disturbance. The control law is formulated to reject disturbance and to track a given trajectory. As a case study, the semiconductor manufacturing process is used to validate the proposed solution. Comparing with the exponentially weighted moving average controller, simulations indicate that the proposed discrete ADRC is effective in cancelling disturbance and in tracking the desired target

Inference Acceleration with Adaptive Distributed DNN Partition over Dynamic Video Stream

Deep neural network-based computer vision applications have exploded and are widely used in intelligent services for IoT devices. Due to the computationally intensive nature of DNNs, the deployment and execution of intelligent applications in smart scenarios face the challenge of limited device resources. Existing job scheduling strategies are single-focused and have limited support for large-scale end-device scenarios. In this paper, we present ADDP, an adaptive distributed DNN partition method that supports video analysis on large-scale smart cameras. ADDP applies to the commonly used DNN models for computer vision and contains a feature-map layer partition module (FLP) supporting edge-to-end collaborative model partition and a feature-map size partition (FSP) module supporting multidevice parallel inference. Based on the inference delay minimization objective, FLP and FSP achieve a tradeoff between the arithmetic and communication resources of different devices. We validate ADDP on heterogeneous devices and show that both the FLP module and the FSP module outperform existing approaches and reduce single-frame response latency by 10–25% compared to the pure on-device processing

New Framework for Dynamic Water Environmental Capacity Estimation Integrating the Hydro-Environmental Model and Load–Duration Curve Method—A Case Study in Data-Scarce Luanhe River Basin

A better understanding of river capacity for contaminants (i.e., water environmental capacity, WEC) is essential for the reasonable utilization of water resources, providing government’s with guidance about sewage discharge management, and allocating investments for pollutant reduction. This paper applied a new framework integrating a modified hydro-environmental model, Soil and Water Assessment Tool (SWAT) model, and load–duration curve (LDC) method for the dynamic estimation of the NH3-N WEC of the data-scarce Luanhe River basin in China. The impact mechanisms of hydrological and temperature conditions on WEC are discussed. We found that 77% of the WEC was concentrated in 40% hydrological guarantee flow rates. While the increasing flow velocity promoted the pollutant decay rate, it shortened its traveling time in streams, eventually reducing the river WEC. The results suggest that the integrated framework combined the merits of the traditional LDC method and the mechanism model. Thus, the integrated framework dynamically presents the WEC’s spatiotemporal distribution under different hydrological regimes with fewer data. It can also be applied in multi-segment rivers to help managers identify hot spots for fragile water environmental regions and periods at the basin scale

Deep Learning Based Brain Tumor Segmentation: A Survey

Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we use this survey to provide a comprehensive study of recently developed deep learning based brain tumor segmentation techniques. More than 100 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality processes. We also provide insightful discussions for future development directions

Unfalsifying Pole Locations Using a Fading Memory Cost Function

Given a feedback control system with an unknown plant, the problem of choosing a stabilizing controller is considered. Working within the framework of unfalsified adaptive control, we consider a finite-dimensional linear time invariant system as a special case of the standard adaptive configuration. A fading memory cost function is presented in which the influence of older data is reduced exponentially. With this cost function, the location of the poles can be detected with only input-output data. Compared with existing results, the cost function can detect changes affecting stability sooner and be used in adaptive switching control to improve the performance of controller switching. ? 2013 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society