2019 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第2页

Red-FLC: an Adaptive Fuzzy Logic Controller with Reduced Learning Parameters Red-FLC:一种减少学习参数的自适应模糊逻辑控制器

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003080

Md Meftahul Ferdaus, S. Anavatti, M. Garratt, Mahardhika Pratama

{"title":"Red-FLC: an Adaptive Fuzzy Logic Controller with Reduced Learning Parameters","authors":"Md Meftahul Ferdaus, S. Anavatti, M. Garratt, Mahardhika Pratama","doi":"10.1109/SSCI44817.2019.9003080","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9003080","url":null,"abstract":"In this paper, an adaptive Takagi-Sugeno (TS)-fuzzy controller is developed for nonlinear dynamical systems, where a new structure of the controller with reduced learning parameters is proposed. The proposed controller is named as a reduced learning parameter based fuzzy logic controller (Red-FLC). Being a model-free controller, the classical TS-fuzzy one performs well in slow-process control-based complex applications. However, the controller’s structure is associated with several antecedent and consequent parameters, which need to be adapted during control operation. Adaptation of a high number of parameters is computationally expensive, especially in controlling a system where a fast response is expected. From this research gap, in our developed adaptive fuzzy controller, the tuning parameters have reduced significantly since it has no antecedent parameters. The closed-loop stability of the controller has been proved using a new adaptation law. To evaluate the proposed controller’s performance, it has been utilized to stabilize an inverted pendulum’s simulated plant on a cart by considering an impulse disturbance. The performance of Red-FLC has been compared with a classical TS-fuzzy controller and a Proportional Integral Derivative (PID) controller, where better tracking of the cart’s position and better disturbance rejection is observed from the proposed TS-fuzzy controller.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"35 1","pages":"513-518"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77746022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Virtual Reality System for Accurate Cardiac Modeling and Multiple Virtual Operations 用于心脏精确建模和多重虚拟手术的虚拟现实系统

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003110

Xiumei Cai, Puze Cheng, Hao-yang Shi, Cong Guo, Shao-jie Tang, Mingle Qiu

引用次数: 0

Feature Selection Based on Twin Support Vector Regression 基于双支持向量回归的特征选择

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003001

Qing Wu, Haoyi Zhang, Rongrong Jing, Yiran Li

引用次数: 7

Membrane System-based Optimization Algorithm for Numeric Optimization Problem 基于膜系统的数值优化问题优化算法

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002810

Chuang Liu, Wanghui Shen, Yingkui Du, Jiahao Lei, Ao Li

引用次数: 0

Faster R-CNN Based Indoor Flame Detection for Firefighting Robot 基于R-CNN的室内火焰检测机器人

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002843

Jiadong Guo, Zengguang Hou, Xiaoliang Xie, Shuncai Yao, Qiaoli Wang, Xuechen Jin

{"title":"Faster R-CNN Based Indoor Flame Detection for Firefighting Robot","authors":"Jiadong Guo, Zengguang Hou, Xiaoliang Xie, Shuncai Yao, Qiaoli Wang, Xuechen Jin","doi":"10.1109/SSCI44817.2019.9002843","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002843","url":null,"abstract":"Firefighters are primarily tasked to handle fire incidents, but they are often exposed to high risks when extinguishing fire. Firefighting robots are actively being researched to reduce fire fighters injuries and deaths as well as increase their effectiveness on performing tasks. However, the major concern is how to make the flame detection methods to satisfy the high precision requirement of firefighting robot. Therefore, the flame detection of firefighting robot has become a hot topic in this area. In this paper, a Faster R-CNN model is proposed to detect flame in noisy images of fire ground. Firstly, the region generation network is used to extract the candidate flame regions. Secondly, the candidate flame regions are convoluted and pooled to extract the flame characteristics. Thirdly, the output features of Region Proposal Network (RPN) are fed into two fully connected layers: a box-regression layer which recognizes the locations of objects and a box-classification layer which classifies the objects. The dataset used in the experiment was obtained by video capture. The network is pre-trained based on Google platform Tensorflow, and the obtained precision and frame rate of the proposed method are up to 99.8% and 1.4 FPS, respectively. The experimental results demonstrate that the method equipped merits such as automatically extract the flame characteristics, effectively improve the precision of flame detection, and has excellent generalization ability and robustness.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"39 1","pages":"1390-1395"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85033793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Automatic Composite Action Discovery for Hierarchical Reinforcement Learning 分层强化学习的自动复合动作发现

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003053

Josiah Laivins, Minwoo Lee

引用次数: 1

Design of Industrial Field Intelligent Temperature Acquisition System Based on Timestamped Anti-Interference Algorithm 基于时间戳抗干扰算法的工业现场智能温度采集系统设计

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002928

Chunjie Yang, Bingchun Jiao, Hongbo Kang, Yanwei Li, Yan Liu, Yifan Wu, Shujie Ma

{"title":"Design of Industrial Field Intelligent Temperature Acquisition System Based on Timestamped Anti-Interference Algorithm","authors":"Chunjie Yang, Bingchun Jiao, Hongbo Kang, Yanwei Li, Yan Liu, Yifan Wu, Shujie Ma","doi":"10.1109/SSCI44817.2019.9002928","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002928","url":null,"abstract":"At present, wired method is used widely in the acquisition and transmission of field signals in industrial field acquisition system. There are some problems such as long cables, difficult wiring, analog signals that are susceptible to interference and so on. A new design of industrial field intelligent temperature acquisition system based on timestamped anti-interference algorithm is presented in the paper. The system can collect analog signals of PT100 and convert that to digital signals which are transmitted to the control center by Data Transceiver. When the signals reach the control center, the system will process digital signals, and set up T-R relation table based on least square algorithm, and at last, the signals are restored to resistance signals of PT100 by using multichannel digital potentiometers. The system uses wireless transmission instead of wired transmission, to realize the collection, transmission and recovery of thermal resistance information, and solve the interference problem of long-distance wireless communication. Experimental test verify that the system communication is normal, stability and accurate, which provides a reasonable design scheme for the application of data transmission technology in industrial field.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"31 1","pages":"2884-2889"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85916987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Multipitch Estimation of Piano Sounds Using Deep Spiking Neural Networks 基于深度尖峰神经网络的钢琴声音鲁棒多音高估计

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003037

Hanxiao Qian, Pengjie Gu, Rui Yan, Huajin Tang

引用次数: 1

Dynamic Insider Threat Detection Based on Adaptable Genetic Programming 基于自适应遗传规划的动态内部威胁检测

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003134

Duc C. Le, A. N. Zincir-Heywood, M. Heywood

引用次数: 4

Siamese-Hashing Network for Few-Shot Palmprint Recognition 小样本掌纹识别的连体哈希网络

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002978

Chengcheng Liu, Huikai Shao, Dexing Zhong, Jun Du

引用次数: 3