王龙，现为北京科技大学副教授，香港政府博士奖学金（Hong Kong PhD Fellowship）获得者，国际电气和电子工程师协会（IEEE）会员，IEEE Power and Energy Society （IEEE PES）会员，香港城市大学系统工程博士，英国伦敦大学学院（University College London）计算机科学硕士。主要研究方向为数据发掘、机器学习、计算机视觉和计算智能方法在新能源、电力市场和轨道交通领域的应用。研究成果主要发表在IEEE Transactions on Smart Grid, IEEE Transactions on Industrial Informatics 和 IEEE Transactions on Industrial Electronics等SCI期刊，现为SCI期刊Journal of Intelligent Manufacturing, Electronics Letters和IEEE Transactions on Cybernetics审稿人。

个人主页：http://longwang.me/

Integration of Machine Learning and Computational Intelligence 2016-Present 

The goal of this project is to develop a regression model guided swarm intelligence algorithm. We are working on integrating the Gaussian process model into a swarm intelligence algorithm and thus the Gaussian process model is utilized for estimating the fitness function values. This algorithm has been applied to track the maximum power point of PV systems. 

GPU-based Parallel Jaya Algorithm 2016-Present 

This project aims to develop a parallel Jaya algorithm implemented on GPU. Compared with the conventional Jaya algorithm, the parallel Jaya algorithm is also free of any algorithm-specific parameters and the three main procedures, solution update, fitness value computation, and the best/worst solution selection are all computed in parallel on GPU. We have applied this algorithm to estimate parameters of a li-ion battery model.

Object Detection Using UAVs 2015-Present

Objection detection algorithms are highly desired in emerging applications of UAVs for remote inspection tasks. We are working on designing an improved cascading classifier for objection detection based on UAV-taken images. In order to reduce the number of features utilized, decision trees and support vector machines are utilized as substitutions of boosting algorithms. This algorithm has been applied for detecting surface cracks on wind turbine blades.

Anomaly Detection of Complex Systems 2014-Present  

This project aims at developing data-driven anomaly detection approaches for complex systems. We are working on developing deep learning based frameworks to detection anomalies of complex systems. In these frameworks, deep learning algorithms, such as deep autoencoders and dropout deep neural networks, are employed to model complex systems while statistical control charts are utilized to monitor the abnormal statuses. We have applied these frameworks for wind turbine condition monitoring and fault diagnosis.

Short-term Electricity Price Forecasting 2014-2015 

Developed an extended stacked denoising autoencoders model, which incorporates both the stochastic neighbor embedding and the random sample consensus algorithms. This model has outperformed classical data-driven models and an industrial method in forecasting electricity prices of five hubs in the USA.

Postgraduate Level:

SEEM 6015 Supply Chain Management, Semester A 2015/16, Class Size: 85

Undergraduate Level:

SEEM 4025 Quality Systems & Management, Semester B 2015/16, Class Size: 30

SEEM 3040 Engineering Database \& Systems, Semester A 2016/17, Class Size: 18

"Data-driven Wind Turbine Condition Monitoring,'' 2016 East Lake International Forum for Outstanding Overseas Young Scholars, December, 2016, Wuhan, China

"Data Mining and its Application to Wind Energy,'' China Longyuan Power Group Corporation Ltd., November, 2016, Beijing, China

"Wind Turbine Gearbox Failure Monitoring Based on SCADA Data Analysis,'' 2016 IEEE Power and Energy Society General Meeting, July 2016, Boston, USA

"Wind Turbine Gearbox Failure Monitoring Based on SCADA Data Analysis,'' Seminar Series, Department of Systems Engineering and Engineering Management, City University of Hong Kong, August, 2016, Hong Kong

"Data-driven Wind Turbine Generation Performance Monitoring,'' China Longyuan Power Group Corporation Ltd., August, 2015, Beijing, China