G.K.H. Pang

University of Hong Kong

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

N.H.C. Yung

University of Hong Kong

Henry Y. T. Ngan

Hong Kong Baptist University

K.L. Choy

Hong Kong Polytechnic University

Ngai Wong

University of Hong Kong

Ya-Li Hou

Guizhou Electromechanical Research and Design Institute

Dapeng Qiao

Beijing Institute of Technology

H.Y. Lam

Hang Seng University of Hong Kong

E. Kwong

Hong Kong Polytechnic University

V.H. Quintana

University of Waterloo

Ajay Kumar

JSS Academy of Higher Education and Research

Cooperative Institutions

University of Hong Kong

University of Waterloo

Hong Kong University of Science and Technology

Hong Kong Polytechnic University

Chinese University of Hong Kong

University of Toronto

Chinese Academy of Surveying and Mapping

Wuhan University

University of Chinese Academy of Sciences

Hang Seng University of Hong Kong

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

REAL-TIME INTELLIGENT PROCESS CONTROL USING CONTINUOUS FUZZY PETRI NETS

Elsevier eBooks (1994)

R. Tang G.K.H. Pang

Process architecture

Petri dish

10.1016/b978-0-08-042360-9.50016-6

Cite

Citations (0)

A Blackboard Control Architecture for Real-Time Control

American Control Conference (1988)

G.K.H. Pang

A flexible and powerful expert system framework, called blackboard architecture, is proposed for tackling real-time control problems. It is believed that this blackboard architecture can provide an environment for achieving intelligent control behaviours in AI systems. A brief description of the blackboard architecture is given. The potential and usefulness of this structured framework is examined. The use of the blackboard architecture for the real-time control of a mobile robot is discussed.

Blackboard system

Real-time Control System

10.23919/acc.1988.4789719

Cite

Citations (9)

Bio-medical Application on Predicting Systolic Blood Pressure Using Neural Networks

Tony Hao Wu E. Kwong G.K.H. Pang

This paper presents a new study based on artificial neural network, which is a typical technique for processing big data, for the prediction of systolic blood pressure by correlated factors (gender, serum cholesterol, fasting blood sugar and electrocardiography signal). Two neural network algorithms, back-propagation neural network and radial basis function network, are used to construct and validate the bio-medical prediction system. The database of raw data is divided into two parts: 80% for training the neural network and the remaining 20% for testing the performance. The experimental result shows that artificial neural networks are suitable for modeling and predicting systolic blood pressure. This novel method of predicting systolic blood pressure contributes to giving early warnings to adults who may not take regular blood pressure measurements. Also, as it is known that an isolated blood pressure measurement is sometimes not very accurate due to the daily fluctuation, our predictor can provide another reference value to the medical staff.

Backpropagation

10.1109/bigdataservice.2015.54

Cite

Citations (6)

Fabric defect detection by Fourier analysis

IEEE Transactions on Industry Applications (2000)

Chi-Ho Chan G.K.H. Pang

Many fabric defects are very small and undistinguishable, which makes them very difficult to detect by only monitoring the intensity change. Faultless fabric is a repetitive and regular global texture and Fourier transforms can be applied to monitor the spatial frequency spectrum of a fabric. When a defect occurs in fabric, its regular structure is changed so that the corresponding intensity at some specific positions of the frequency spectrum would change. However, the three-dimensional frequency spectrum is very difficult to analyze. In this paper, a simulated fabric model is used to understand the relationship between the fabric structure in the image space and in the frequency space. Based on the three-dimensional frequency spectrum, two significant spectral diagrams are defined and used for analyzing the fabric defect. These two diagrams are called the central spatial frequency spectrums. The defects are broadly classified into four classes: (1) double yarn; (2) missing yarn; (3) webs or broken fabric; and (4) yarn densities variation. After evaluating these four classes of defects using some simulated models and real samples, seven characteristic parameters for a central spatial frequency spectrum are extracted for defect classification.

Spatial frequency

Intensity

Texture (cosmology)

Frequency spectrum

10.1109/28.871274

Cite

Citations (406)

Intelligent control of an autonomous mobile robot in a hazardous material spill accident — a blackboard structure approach

Robotics and Autonomous Systems (1990)

G.K.H. Pang Helen C. Shen

Blackboard system

Accident (philosophy)

10.1016/s0921-8890(05)80016-8

Cite

Citations (15)

A pattern analysis approach for topology determination, bad data correction and missing measurement estimation in power systems

A.P. Alves da Silva V.H. Quintana G.K.H. Pang

The authors propose a novel methodology for the combined solution of the topological identification, observability analysis and bad data processing problems in power systems. The idea is to provide, in a very fast way, a reliable input database for the state estimator, without interacting with it. The solution is based on a pattern analysis approach. An efficient framework for solving data acquisition and processing problems, joining pattern analysis and analytical procedures, is suggested. Two different techniques of pattern analysis are combined to produce a classifier and an estimator with unique characteristics to deal with noisy environments. The patterns required for the training process can be acquired from the SCADA (supervisory control and data acquisition) system and/or from load-flow simulations. Test results have been obtained for the IEEE 24-bus reliability test system.< >

Observability

SCADA

10.1109/naps.1990.151390

Cite

Citations (11)

A framework for intelligent control

Journal of Intelligent & Robotic Systems (1991)

G.K.H. Pang

Blackboard system

Intelligent Control

10.1007/bf00440415

Cite