Shenyang Institute of Automation,Chinese Academy Of Sciences
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基于多智能体深度强化学习的工业无线网络端边协同资源分配
No full textEdge artificial intelligence will empower the ever simple industrial wireless networks (IWNs) supporting complex and dynamic tasks by collaboratively exploiting the computation and communication resources of both machine-type devices (MTDs) and edge servers. In this paper, we propose a multi-agent deep reinforcement learning based resource allocation (MADRL-RA) algorithm for end–edge orchestrated IWNs to support computation-intensive and delay-sensitive applications. First, we present the system model of IWNs, wherein each MTD is regarded as a self-learning agent. Then, we apply the Markov decision process to formulate a minimum system overhead problem with joint optimization of delay and energy consumption. Next, we employ MADRL to defeat the explosive state space and learn an effective resource allocation policy with respect to computing decision, computation capacity, and transmission power. To break the time correlation of training data while accelerating the learning process of MADRL-RA, we design a weighted experience replay to store and sample experiences categorically. Furthermore, we propose a step-by-step ε-greedy method to balance exploitation and exploration. Finally, we verify the effectiveness of MADRL-RA by comparing it with some benchmark algorithms in many experiments, showing that MADRL-RA converges quickly and learns an effective resource allocation policy achieving the minimum system overhead.</p
The Important Role of Global State for Multi-Agent Reinforcement Learning
No full textEnvironmental information plays an important role in deep reinforcement learning (DRL). However, many algorithms do not pay much attention to environmental information. In multi-agent reinforcement learning decision-making, because agents need to make decisions combined with the information of other agents in the environment, this makes the environmental information more important. To prove the importance of environmental information, we added environmental information to the algorithm. We evaluated many algorithms on a challenging set of StarCraft II micromanagement tasks. Compared with the original algorithm, the standard deviation (except for the VDN algorithm) was smaller than that of the original algorithm, which shows that our algorithm has better stability. The average score of our algorithm was higher than that of the original algorithm (except for VDN and COMA), which shows that our work significantly outperforms existing multi-agent RL methods.</p
GasHisSDB: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer
No full textBackground and objective: Gastric cancer is the fifth most common cancer globally, and early detection of gastric cancer is essential to save lives. Histopathological examination of gastric cancer is the gold standard for the diagnosis of gastric cancer. However, computer-aided diagnostic techniques are challenging to evaluate due to the scarcity of publicly available gastric histopathology image datasets. Methods: In this paper, a noble publicly available Gastric Histopathology Sub-size Image Database (GasHisSDB) is published to identify classifiers’ performance. Specifically, two types of data are included: normal and abnormal, with a total of 245,196 tissue case images. In order to prove that the methods of different periods in the field of image classification have discrepancies on GasHisSDB, we select a variety of classifiers for evaluation. Seven classical machine learning classifiers, three Convolutional Neural Network classifiers, and a novel transformer-based classifier are selected for testing on image classification tasks. Results: This study performed extensive experiments using traditional machine learning and deep learning methods to prove that the methods of different periods have discrepancies on GasHisSDB. Traditional machine learning achieved the best accuracy rate of 86.08% and a minimum of just 41.12%. The best accuracy of deep learning reached 96.47% and the lowest was 86.21%. Accuracy rates vary significantly across classifiers. Conclusions: To the best of our knowledge, it is the first publicly available gastric cancer histopathology dataset containing a large number of images for weakly supervised learning. We believe that GasHisSDB can attract researchers to explore new algorithms for the automated diagnosis of gastric cancer, which can help physicians and patients in the clinical setting.</p
APAN: Across-Scale Progressive Attention Network for Single Image Deraining
No full textRecent single image deraining works have achieved significant improvement using convolutional neural networks. However, the rain streaks in the rain image share similar patterns with its multi-scale versions, which are not fully exploited in recent works. In this paper, we propose an Across-scale Progressive Attention Network (i.e., APAN) to explore the multi-scale collaborative representation for single image deraining. Specifically, we represent each rainy image via a multi-scale module. An across-scale attention module is then used to capture long-range feature correspondences from multi-scale features, which can model the rain streaks at an enlarging feature dimension. Afterwards, we construct a pyramid structure and further predict the rain streak progressively, which also guides the across-scale attention module to refine the feature representation from coarse to fine. The proposed model exploits self-similarity of features via an across-scale attention between different scales, which can well model the rain streak with long-range information. Experiments on several datasets show that our model achieves significant improvement compared with most state-of-the-art deraining models.</p
A real-time scheduling algorithmbased on flexible frequency resources in 5G
No full text5G网络的出现使无线技术在工业控制系统中的全面应用成为可能。目前,工业控制系统中存在大量高实时数据需通过无线网络进行传输,但已有的传输调度方法无法适用于频率域可灵活拆分的5G网络。因此,文章针对5G时频使用规则,基于经典装箱算法,提出一种对5G时频资源分类细化处理的启发式调度方法。评估测试表明文章所提方法与传统调度方法相比数据传输延时减小了28%。</p
Context-aware scheduling and control architecture for cyber-physical production systems
No full textCyber-physical production systems provide a flexible and open mechanism for manufacturing process scheduling and control, and they also offer an opportunity to further improve the performance of systems by the joint optimization of scheduling and control (JSC). With given optimization objectives, the solution of JSC not only provides the schedule plan but also provides the optimal control parameters. However, due to the dynamic nature of the production system, it is not possible to consider all potential situations to make an ideal solution for the JSC at the beginning. Therefore, this paper formulates the problem of dynamic JSC and proposes an architecture for context-aware production scheduling and control systems, which utilizes ontology and reasoning technologies from knowledge engineering to enhance the adaptabilities of production systems. To illustrate the feasibility of the proposed architecture, we take an international competition platform as a case study and compare the performance with the champion team's system. The result shows that our system performs better than does the system of the champion team, and it also proves the feasibility of the proposed architecture.</p
Shape Sensing for Continuum Robots by Capturing Passive Tendon Displacements with Image Sensors
No full textContinuum robots and soft robots have shown great potential in industrial and medical applications. Sensing the shapes of continuum robots is a challenging but significant problem for enhancing their performance during various tasks. In this paper, we present a novel method to estimate the shapes of continuum robots by capturing passive tendon displacements with image sensors. This method is reliable but low-cost. To reconstruct the shape of the continuum robot, an intuitive way based on screw theory and the material frame is also presented. The displacements of passive tendons are related to the integral of the geometric parameters of the continuum robot. By using passive tendon displacements, not only bending but also twisting of the robot can be calculated. The experiments show that our method can estimate the shapes of the robot deformed in different conditions. The mean distal position error is 3.01% of the length and the mean shape error is 1.86% of the length. The mean distal direction error is 4.1 degrees. Besides, sensing the shapes of continuum robots in real-time is also achieved.</p
一种水下机器人用伸缩光纤浮包对接装置
No full text本发明属于水下机器人技术领域，特别涉及一种水下机器人用伸缩光纤浮包对接装置。包括上伸缩软管、浮包、下伸缩软管及光纤；浮包的上端通过上连接件与上伸缩软管可分离连接；浮包的下端通过下连接件与下伸缩软管固定连接；光纤依次穿过上伸缩软管、浮包及下伸缩软管。本发明通过在危险环境下将光纤放置在装置内而达到保护的目的，提高了光纤的安全性，减少了因光纤问题而影响潜次的次数，提高了水下机器人布放的成功率
一种适用于无人潜器的水下对接装置
No full text本发明涉及水下对接装置，具体地说是一种适用于无人潜器的水下对接装置，对接装置头部为对接头，对接头的上方开有对接通孔；对接装置的内部为空心式结构，在对接装置的对接头内部的空心结构中，存在一个保压密封装置；被对接装置的头部为导向罩，导向罩上设有灯源，导向罩后侧为对接筒，对接筒内设有可以夹紧和放松的电动夹爪；在被对接装置的内部也有一个保压密封装置。本发明为前头移动式对接，解决了水下设备移动性差、方向性差的笨重问题，符合了水下结构控制学的基本要求；同时，本发明可以使水下无人潜器有效地设置精密仪器的使用环境，并保证对接中的稳定性，具有方便进行水下无人潜器与其它装置的信息、能源传输的优势
一种基于少量正样本融合的模板匹配方法
No full text本发明涉及一种基于少量正样本融合的模板匹配方法。本发明面向图像目标识别定位领域，尤其涉及基于模板匹配的目标识别定位方法，通过对少量模板正样本的融合，当目标存在较大遮挡、形变、梯度方向突变时，能够对目标进行鲁棒的、快速的识别定位。本发明包括以下四个步骤：1、提取模板图像中的目标边缘点集；2、不同模板图像下目标边缘点集的配准；3、目标边缘点的融合；4、基于融合点集的相似度统计。本发明解决目标存在较大遮挡、形变、梯度方向突变时的模板匹配问题，提高目标识别定位鲁棒性；本发明应用于工业机器视觉识别与定位领域，为工业生产中零部件的识别与定位提供了解决方法，为实现工业自动化生产过程提供了感知功能