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A Multi-label Classification Approach for ICT Fault Text Analysis 12

A Multi-label Classification Approach for ICT Fault Text Ana...

引用

12th International symposium on computational intelligence and Design (ISCID)

作者： Zhang, Qiang Chen, Xiaona Global Energy Interconnect Res Inst Co Ltd Artificial Intelligence Elect Power Syst State Gr WeiLaiKeJiCheng Pk Beijing 102209 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

ISBN: (纸本)9781728146522

The ICT customer service from State Grid has accumulated a large number of electric power fault reports and texts, which are difficult to classify and analyze because an occurrence of power fault may be triggered possibly by multiple different causes. In order to track different causes of electric power faults from historical fault texts, a multi-label based text classification approach is proposed for fault-assisted decision. Firstly, Chinese fault texts are sequentially preprocessed by word segmentation, stop words removal and feature representation. Then, we present a multi-label text classification by combining Binary Relevance and Gradient Boosting algorithm. The experimental results show that our method is better than BR+LR and ML-KNN for fault text classification.

关键词： electric power fault diagnosis multi-label text classification Binary Relevance Gradient Boosting

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Warning Solution for Medication Administration

Warning Solution for Medication Administration

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Monica-Lavinia NEDELCEA Adriana ALBU Faculty of Automation and Computers Politehnica University Timisoara Timisoara Romania Department of Automation and Applied Informatics Politehnica University Timisoara Timisoara Romania

ISBN: (数字)9781728173771

ISBN: (纸本)9781728173788

Nowadays more and more people of different ages with health problems face the compliance with the medicine administration program. Besides the diagnosis and the proper treatment received from the physician, one of the most important steps for healing is to follow the schedule of medication administration. This paper describes a warning system which is composed by a pill box and an electronic kit with two functions (acoustic and visual). The first function, based on mini speaker, ensures sound alertness. The second one, involves two elements: a Light-Emitting Diode, which indicates the day of the week, and a Liquid Crystal Display, which informs the patient by a message, regarding the period of the day, that it is time to take the medication. The purpose of this paper is to present the warning mechanism, which is designed for the medical system, having the possibility to be used both at the hospital and at the domicile of the sick person.

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SACI 2019 - ieee 13th International symposium on Applied computational intelligence and Informatics, Proceedings

SACI 2019 - IEEE 13th International Symposium on Applied Com...

引用

13th ieee International symposium on Applied computational intelligence and Informatics, SACI 2019

ISBN: (纸本)9781728106854

The proceedings contain 64 papers. The topics discussed include: distributed formation control: a generalized framework for multi-agent and network systems;lunar crater detection based on grid partition using deep learning;study on a road surface estimation method based on big data analysis;relevance of PMBOK v6 processes for tailored agile project categories;wardriving in Eger;next-level robotic intelligence;intelligent lighting system platform architecture and linear process models;advanced control system for the rotary hearth furnace servicing system;virtual research laboratory for smart engineering in the cloud;and emotional profiling of robots in health-care.

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Visual Image Classification Based on Auto-Associative Memory

Visual Image Classification Based on Auto-Associative Memory

引用

ieee symposium Series on computational intelligence (SSCI)

作者： He, Daoliang Wang, Yanjiang Gao, Mingyue Song, Xinjing Xiao, Linxia China Univ Petr East China Coll Control Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9781728124858

Numerous methods have been proposed to solve the problem of visual image classification in the past few years, but few of them are associated with human brain memory mechanism. In this paper, we study the mechanism of human brain auto-associative memory and apply it to the storage, retrieval, and classification of visual images. The learning process of the memory model is based on Hebb-like associative local learning rule and the recall rests on a normalized dot product operation. The proposed model is capable of storing and retrieving visual images as human brain, and it can achieve distributed storage and incremental learning. The experimental results on some benchmark image databases indicate that the proposed model outperforms previously proposed Pattern Association Memory Model (PAMM) and Self-Organizing Incremental Neural Network (SOINN) on recognition accuracy.

关键词： image classification auto-associative memory memory modeling Hebb rules incremental learning

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Two Objective Matching Pursuit for Seismic Data Decomposition

Two Objective Matching Pursuit for Seismic Data Decompositio...

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ieee symposium Series on computational intelligence (SSCI)

作者： Li, Yongqing Wang, Jun Li, Hui Ren, Peng China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China

ISBN: (纸本)9781728124858

Matching pursuit is an effective method for seismic data decomposition. The traditional matching pursuit has one single objective that successively maximizes the inner products between signal residuals and atoms at different approximating levels. However, such inner product strategy does not guarantee the consistency of the peak of an atom and that of its corresponding signal residual. To address this limitation, we propose a two objective matching pursuit strategy that not only optimizes the traditional matching pursuit but also minimizes peak distance. Compared with the traditional matching pursuit with one single objective, the two objective matching pursuit has higher search ability. In this scenario, the genetic algorithm (GA) used for solving the traditional matching pursuit is not suitable for the two objective matching pursuit. Therefore, we propose to solve the two objective optimization problem by exploiting a nondominated sorting genetic algorithm (NSGA-II) algorithm, which not only increases the diversity of searching optimization and but also reduces the reconstruction error over the GA. Experiments for seismic data validate the advantages of the proposed two objective matching pursuit.

关键词： matching pursuit nondominated sorting genetic algorithm multiobjective optimization

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Red-FLC: an Adaptive Fuzzy Logic controller with Reduced Learning Parameters

Red-FLC: an Adaptive Fuzzy Logic Controller with Reduced Lea...

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ieee symposium Series on computational intelligence (SSCI)

作者： Ferdaus, Md Meftahul Anavatti, Sreenatha G. Garratt, Matthew A. Pratama, Mahardhika UNSW Canberra Sch Engn & Informat Technol Canberra ACT 2612 Australia Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

ISBN: (纸本)9781728124858

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.

关键词： adaptive control fuzzy learning reduced

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Road detection using lidar data based on plane assumption and graph model

Road detection using lidar data based on plane assumption an...

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ieee symposium Series on computational intelligence (SSCI)

作者： Yan, Min Wang, Junzheng Li, Jing Zhang, Ke Yang, Zimu Beijing Inst Technol Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China

ISBN: (纸本)9781728124858

Detection of traversable road area is one of the key tasks for unmanned platforms to achieve autonomous navigation. We propose a road detection method using lidar data based on plane assumption and graph model in this paper. Two detection algorithms for different speeds are provided to ensure the detection accuracy and to meet the real-time requirements, respectively. First, the point cloud is projected into a real image or an imaginary image for triangulation. The road points are then extended outward from the near platform according to the plane assumption. Then the dual Voronoi diagram of the triangulation is filled according to the attribute of the central element node, and the upper part of the horizon is removed. Finally, image post-processing yields the final detection results. This method perfectly combines the classification of points and the determination of road areas and uses the mature image algorithms in the processing of point cloud data. Experimental tests have been carried out on the public KITTI-Road benchmark, obtaining positive results.

关键词： road detection triangulation plane fitting lidar

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Object Tracking Based on Deep CNN Features Together with Color Features and Sparse Representation

Object Tracking Based on Deep CNN Features Together with Col...

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ieee symposium Series on computational intelligence (SSCI)

作者： Liu, Yuchi Qi, Yujuan Wang, Yanjiang China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China

ISBN: (纸本)9781728124858

To acquire the better object tracking performance in this paper, the object of interest is modeled by its RGB-color histogram feature together with deep convolutional neural network feature (deep CNN feature) and sparse representation. Because the information of the objects of interest cannot be obtained ahead in practical use, the deep CNN features should be abstracted through the pre-trained VGG network. Then the deep feature and RGB-color histogram are combined to model the object in adaptive mode. And then sparse representation is used to express the model. Above all, an adaptive particle filter algorithm based on deep CNN feature together with RGB-color feature and sparse representation is proposed to track the object of interest. Extensive experiment results demonstrate the effectiveness of our proposed method under the serious object occlusions and appearance changes.

关键词： deep convolutional neural network feature RGB-color histogram sparse representation object tracking

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Damping control Based Speed Adjustment Strategy for a Lower Limb Rehabilitation Robot

Damping Control Based Speed Adjustment Strategy for a Lower ...

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ieee symposium Series on computational intelligence (SSCI)

作者： Liang, Xu Hou, Zeng-Guang Ren, Shixin Shi, Weiguo Wang, Weiqun Wang, Jiaxing Su, Tingting Chinese Acad Sci Inst Automat Beijing Peoples R China Chinese Acad Sci Beijing Peoples R China North China Univ Technol Shanghai Peoples R China

ISBN: (纸本)9781728124858

Active rehabilitation training based on recognition of motion intention can effectively improve the patient's engagement in rehabilitation training, and thus improve the training effect. In this paper, a speed adjustment strategy for active training based on multi-joint damping control is proposed for bicycle training. The active force applied by patient can be calculated by the dynamic model of human-robot system, and then converted into the tangential force at the pedal of robot along the forward direction of bicycle. The tangential force is converted into the adjustment term of joint angular speed by damping control to dynamically adjust the riding speed. When the tangential force is larger than the threshold value, the pedal will deviate from the reference circular trajectory. Therefor, a speed vector pointing to the circular center is added to pull the end-effector back to the reference trajectory. Moreover, a fuzzy impedance parameter regulator is designed to adjust the training intensity, by which the impedance parameters can be regulated according to the magnitude of the patient's active force and the deviation from the reference trajectory. Finally, in order to increase the patient's engagement, Unity3D software is used to design the virtual scene of cycling on the road. The experimental results show that the active compliant rehabilitation training can be realized by the proposed method.

关键词： speed adjustment strategy active training compliant control rehabilitation robot

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A Novel Assist-As-Needed controller Based on Fuzzy-Logic Inference and Human Impedance Identification for Upper-Limb Rehabilitation

A Novel Assist-As-Needed Controller Based on Fuzzy-Logic Inf...

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ieee symposium Series on computational intelligence (SSCI)

作者： Wang, Chen Peng, Liang Hou, Zeng-Guang Wang, Weiqun Su, Tingting Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China North China Univ Technol Dept Mech & Elect Engn Beijing 100144 Peoples R China

ISBN: (纸本)9781728124858

Upper-limb rehabilitation robots are being increasingly included in the rehabilitation after stroke. Clinical studies suggest that the patient's active participation can maximize effectiveness of robot-assisted rehabilitation and result in fastest possible recovery. In order to promote active involvement of the patient, we develop a subject-adaptive controller within the assist-as-needed (AAN) paradigm. The controller employs Recursive Least Square (RLS) algorithm to identify human arm impedance parameters, with aims to quantify the residual motor capability of the patient. According to the upper-limb impedance of the patient in the direction along movements, the reference trajectory can be generated based on the motion patterns in healthy humans. In addition, the fuzzy logic control strategy is implemented in the direction perpendicular to movements, which is capable of adjusting the robotic assistance level by considering both upper-limb impedance of the patient and the variations of deviation between the reference trajectory and actual trajectory. Furthermore, the proposed subject-adaptive AAN controller was validated in the simulation study, and the results demonstrated the capability of the controller to modulate the assistance level in accordance with the subject's instantaneous requirements. Future works will focus on the implementation of the control scheme with post-stroke patients.

关键词： Assist-as-needed fuzzy logic control rehabilitation robotics identification of human arm impedance

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