Prof. Jiping Li, South China Agricultural University, China
Title: Procedural Representation Based Product Data Exchange
Abstract: The problem of exchanging CAD models together with construction history is a crucial issue to realize a complete Product Lifecycle Management (PLM) strategy in current highly distributed product development environments. This talk will discuss some theoretic and practical issues that, at present, hinder the exchange of procedurally defined shape models between different CAD systems. Some approaches proposed in literatures are summarized and critically discussed.

Prof. Qijin Wang, Electronic Communication Engineering College, Anhui Xinhua University, China(Dean of the College)
Title: A Large-Scale Very Small Object Data Set of Agricultural Pests for Multi-target Detection
Abstract: Precision agriculture poses new challenges for real-time monitoring pest population in field based on new-generation AI technology. In order to provide a big data resource for training pest detection deep learning models, this paper establishes a large-scale multi-target standardized data set of agricultural pests, named Pest24. Specifically, the data set currently consists of 25,378 field pest annotated images collected from our automatic pest trap & imaging device. Totally, 24 categories of typical pests are involved in Pest24, which dominantly destroy field crops in China every year. We apply several state-of-the-art deep learning detection methods, Faster RCNN, SSD, YOLOv3, Cascade R-CNN to detect the pests in the data set, and obtained encouraging results for real-time monitoring field crop pests. To explore the factors that affect the detection accuracy of pests, we analyze the data set in a variety of aspects, finding three factors, i.e. relative scales, number of instances and object adhesion, mainly influence the pest detection performance. Overall, Pest24 is featured typically with large scale of multi-pest image data, very small object scales, high object similarity and dense pest distribution. We hope that Pest24 promote accurate multi-pest monitoring for precision agriculture and alsobenefitthe machine vision community by providing a new specialized object detection benchmark.

Prof. Juan Fang, Beijing University of Technology, China
Title: IoT Application Modules Placement and Dynamic Task Processing in Edge-Cloud Computing
Abstract: In today's era of Internet of Things (IoT), efficient and real-time processing of massive data generated by IoT device has become the primary issue for traditional cloud computing network architectures.  As a supplement of cloud computing, edge computing enhances the real-time performance of service completion by offloading services to edge servers closer to the terminal device for execution, while reducing power consumption and computing load in the cloud. In this paper, we propose the following solutions to resolve the different requests of the IoT device: In an “edge-cloud” heterogeneous network environment, create a mapping scheme between application modules and basic resource equipment, considering the two factors of tolerant task latency and system power consumption; In the application step-by-step execution process, heuristic dynamic task processing algorithm is used to reduce the task latency time. Experiments with the “iFogSim” simulator show that, application service quality is significantly improved and system power consumption is greatly reduced, which comparing with the stable application module placement strategy and the static task scheduling strategy.