Prof. Yilun Shang
Northumbria University, UK
Speech title: Consensus of Hybrid Multi-agent Systems with Malicious Nodes
Biography: Dr. Shang received the B.S. and Ph.D. degrees in mathematics from Shanghai Jiao Tong University in 2005 and 2010, respectively. His research interests include complex networks, cyber epidemic spreading, probabilistic combinatorics, system control theory and data science. Prior to joining Northumbria University as an associate professor, he has various appointments with Tongji University, University of Texas at San Antonio, Singapore University of Technology and Design, Hebrew University of Jerusalem, and University of Essex. Dr. Shang is on the Editorial Boards of some international journals including Scientific Reports, European Journal of Pure and Applied Mathematics, and IEEE Access. He received the 2016 Dimitrie Pompeiu Prize.
Speech Abstract: In this talk, we will take a loot at resilient consensus problems of hybrid multi-agent systems containing both continuous-time dynamical agents and discrete-time dynamical agents. A hybrid censoring strategy is introduced to reach resilient consensus for cooperative agents in the directed networks in which some Byzantine agents are present. The number, location, and dynamics of Byzantine agents are assumed to be unavailable to the cooperative agents. Sufficient conditions based on network robustness are established when the number of Byzantine agents is locally bounded. They are further extended to cope with resilient scaled hybrid consensus where dictated ratios instead of a common value can be reached. Simulation and realistic examples in computer intelligence are presented.
Prof. Ching-Biau Tzeng
Kun Shan University, Taiwan
Biography: Ching-Biau Tzeng is now working at the same university in two different positions, include Chairperson and Associate Professor in Department of Electronic Engineering, Kun Shan University, Tainan, Taiwan. He completed his bachelor's degree at Electronic Engineering, National Taiwan University of Technology, Taipei, Taiwan in 1989; Then he received his master degree at Electrical Engineering, National Cheng Kung University, Tainan, Taiwan in 1991; Then he got his doctor's degree at Electrical Engineering, National Cheng Kung University, Tainan, Taiwan in 1998.
Prof. Mohd. Hudzari Bin Haji Razali
University Technology MARA (UiTM), Malaysia
Speech title: Digital Image Processing for Predicting the Maturity Stage of Oil Palm (Elaeis guineensis JACQ.) Fresh Fruit Bunch
Biography: Mohd Hudzari bin Haji Razali was born on 07rd February 1974 in Muar, a small village in North of Johor, boundary neighbour with Jasin, Melaka Malaysia. He did his elementary education in his village school. He passed his secondary school certificate from High School Muar, Johor, Malaysia. He started his university education at the Universiti Pertanian Malaysia, Serdang, and Selangor, Malaysia, where he obtained a Diploma of Agriculture Engineering in 1996. He obtained his Bachelor of Mechanical Engineering in 1999 and Master of Science degree on Agriculture Automation and Robotic from Universiti Putra Malaysia at the same University on program of structure A (research basis) in 2003. He worked as an Assistant Researcher for the national research grant during 2003 till present under his supervisor; Professor Wan Ishak Wan Ismail. In 2005, he was awarded a partial scholarship from Universiti Putra Malaysia, Serdang, Selangor, Malaysia when appointed as a Graduate Research Assistant to pursue Ph.D program at Institute of Advanced Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia and received Ph.D on Smart Technology and Robotic Engineering in October 2010. Previously worked as an Associate Professor in Department of Agriculture Science and Biotechnology, Universiti Sultan Zainal Abidin (UniSZA); and currently worked as at same gred of Associate Professor in UiTM; the position as academician took up on 6 February 2011, a day to his 37th anniversary. Mohd Hudzari also hold other posts, including Research Assistant at Faculty of Engineering, Universiti Putra Malaysia (1999 -2000), then offered as an Automation Engineer at private sector in STMicroelectronic Sdn Bhd, 84000, Muar, Johor, Malaysia (2000-2002), Program Manager and Graduate Research Assistant, Institute of Advanced Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia (2003-2010). He is registered graduate engineer, Board of Engineer (BEM) Malaysia (2011-now) and also currently appointed as Deputy Dean (Student Affairs and Alumni) at same faculty from 1 September 2015.
Speech Abstract: Harvesting of the oil palm fresh fruit bunches (FFB) at correct stage of ripening is important in maximizing crop yield. The quality of oil produced depends to a great extent on the correct date of harvesting. To satisfy the concept of machine intelligence for the smart technology, non-destructive and real time simulation method are necessary to predict the FFB maturity stage. Digital value of image known as Hue was used to measure the oil palm fruit colour skin in actual plantation condition. Using Analysis of Variance (ANOVA) method, the Hue was found the best digital value for determine the color surface of FFB among another digital value component of Red, Green and Blue. The relationship of the oil content for Mesocarp oil palm fruits with the digital value of Hue was analysed. The procedure starts from image capturing of the FFB during unripe (black color surface until overripe stage (orange color surface). The ages of oil palm trees chosen in this experiment were of 5, 16 and 20 years old located at Malaysian Palm Oil Board (MPOB) and Universiti Kebangsaan Malaysia (UKM) Research Station, Bangi Lama, Malaysia. The variety of oil palm is Tenera: Elaeis Guineensis. Nikon Coolpix 4500 digital camera with tele-converter zooming and the Keyence machine vision were used to capture the FFB images in actual oil palm plantation. The images from the Nikon digital camera were analysed for optical properties and then compared with the value obtained from Keyence machine vision. The images of oil palm FFB in plantation were captured with setting cameras parameter namely shutterspeed which set to 0.125 seconds, image sensor’s sensitivity (ISO) was set to Normal and white balance were calibrated using the standard white calibration CR-A74. The lighting intensity under oil palm canopy was simultaneously recorded and monitored using Extech Light Meter Datalogger. On the same day, the fruitlets were plucked from FFB and analysed for its oil Mesocarp content using the Soxhlet Extractor apparatus. The calculations to determine the Mesocarp oil content was developed based on the ratio of oil to dry Mesocarp. The MPOB colorimeter was used to validate and compare for the ripeness level. Regression analysis of polynomial 2nd order model shows that the optical property of oil palm fruit was significant in determining the oil from the Mesocarp fruit with respect to the degree of maturity. The formulated predicted equation was Y = -0.0116X2 + 5.2376X – 514.88 (R2 = 0.884) with Y as the Mesocarp oil content and X as the Hue optical property. High correlation of Hue digital value was found between the developed systems using the Nikon digital camera and the Keyence machine vision with correlation close to 0.929 and accuracy of 97%. Simulation model was developed for estimation the harvesting days of FFB on the basis of its oil content. The verification on calculating the harvesting day of FFB was based on previous research of destructive method. The graph to determine the day of harvesting the FFB was contributed in this research. The oil was found to start developing in Mesocarp fruit at 65 days before fruit at ripe maturity stage of 75% oil to dry Mesocarp weight. The audience aims is to investigate innovation applications and last researches in the areas of applied signal processing and digital image for agriculture application.
Keywords: Image processing, Palm oil, Maturity recognition system, Non-destructive technique
Prof. Samir Ladaci
National Polytechnic School of Constantine, Algeria
Speech title: Fractional Order Cruise Control Strategies for an Electric Vehicle
Biography: Samir Ladaci was born in Constantine (Algeria) on July 17, 1971. He now worked as the full professor in National Polytechnic School of Constantine, Department of E.E.A. Nouvelle ville Ali Mendjli, Constantine, 25000, Algeria. And he was a member of Laboratory of Sinal Processin SP-Lab, Department of electronics, University of Mentouri Constantine 1. His research interests mainly include Fractional order Systems and Control, Fractional Adaptive Control, Robust Control, Optimal control, Nonlinear systems and control, Identification, etc. He has 17 years of teaching with many skills and different courses and supervised more than 25 Engineering project (End of studies).
Speech Abstract: Fractional order controllers are gathering more and more interests from the control community for their ability to enhance the system control quality performances and robustness.
In this work we are investigating different fractional order control strategies for the cruise control of an electrical vehicle. We will use a Fractional Order Model Reference Adaptive Control (FOMRAC) algorithm, an optimized fractional order PID controller (FOPID) and a fractional order High gain controller to improve the vehicle behavior in presence of disturbances and uncertainties. We introduce new tuning parameters for the closed-loop system performance improvement. A numerical simulation of an application study for cruise control of an electric car is proposed.
Electric vehicles (EVs) are becoming more popular these days and automobile manufacturers are introducing various types of EVs in the market. The main advantages of EVs are the emission elimination, low operating cost, high efficiency, simplicity and superior controllability over the powertrain. The EV powertrain consists of an electric motor, single or double speed transmission and the final drive unit.
Our fractional adaptive control algorithm is applied to the cruise control of a DC motor-driven electric vehicle. This system is developed for driving with constant speed on long stretched roads. We show through computer simulations that it is able to compensate the disturbances from the road grade and changes in the vehicle weight. The results illustrate the effectiveness and robustness of the proposed algorithm
Keywords: Fractional adaptive control, Fractional order High gain control, Fractional order PID controller (FOPID), Fractional order integration, Model reference adaptive control, Electrical vehicle, Cruise control, Robustness