Speaker
Dr. Sunil Kumar Jha
Nanjing University of Information Science and Technology, China
Research Area: Data Mining, Artificial Intelligence Applications, Chemical Sensing, Nano-Informatics, Renewable Energy
Speech Title: Data Fusion Approaches in Human Body Odor Data Mining
Abstract: The odor is the characteristic and alarming aroma of the human body. It is a significant information source of an individual's unique characteristic and physical condition in biometric, forensic and medical applications. Due to a complex combination of VOCs, the identification of individuals on the basis of body odor by conventional instruments is a tough task. The objective of the present research talk is to introduce audience about the data fusion and human body odor and to demonstrate research results related to search for an optimal subset of VOCs in body odor, which can produce differentiation in an individual by using the combination of analytical methods and chemometric analysis. Specifically, the implementation of data fusion approaches to search discriminating biomarker volatile organic chemicals (VOCs) in body odor for individual differentiation will be demonstrated. Also, some novel approaches to decision level data fusion will be discussed in human body odor mining. Gas chromatography–mass spectrometry (GC– MS) characterized human body odor samples have been used in analysis and validation of all experiments.
Prof. Chunbo Xiu
Tiangong University, China
Research Area: Design and application of embedded system, Intelligent control and pattern recognition
Speech Title: Memristive Cellular Neural Network & Its Dynamic Characteristic Analysis
Abstract: In order to improve the engineering feasibility of the memristive cellular neural network, a new memristor model with the smooth characteristic curve is designed. Based on the new memristor model, a new four-dimensional chaotic memristive cellular neural network system is constructed, and its chaotic dynamic behaviors are analyzed. Furthermore, in order to enhance the chaotic degree of cellular neural network(CNN), a five-dimensional memristive CNN hyperchaotic system is designed. Complex dynamic behaviors of memristive cellular neural network can be shown, and the circuit schematic diagrams of the systems can be designed. Improved sliding mode control method can be used to accomplish the chaos synchronization of memristive cellular neural network systems. Thus, chaotic memristive CNN system can be used in the secure communication by the chaos synchronization based on sliding mode control.
Assoc.Prof. Pavel Loskot
Zhejiang University-University of Illinois at Urbana-Champaign Institute (ZJUI)
Research Area: models, methods and algorithms for probabilistic and statistical inference, Monte-Carlo simulations and related signal and data processing problems
Speech Title: Monte Carlo Simulations Revisited
Abstract: Monte Carlo methods are very attractive for numerically solving otherwise intractable mathematical problems such as computing integrals, solving differential equations, generating random processes, and analyzing and optimizing models of systems. In case of Monte Carlo simulations, the typical task is to find an empirical relationship between summary statistics of the model inputs and outputs. Whilst this approach is often deemed sufficient for validating engineering designs, its side effect is that even a complex model is then reduced into a mere transformation of model inputs to model outputs. This results in a substantial information loss as it hinders the internal system dynamics and workings. In order to overcome the information loss, the intermediate system outputs can be assumed to augment the global input-output summary statistics. In this talk, this problem will be discussed in the contexts of experiment design and sensitivity analysis for Monte Carlo simulations of engineering systems to allow extracting more knowledge about the model properties, and to improve the efficiency and statistical power of simulations by going beyond the input-output summary statistics.