6^th World Convention on Robots, Autonomous Vehicles and Deep Learning September 10-11, 2018 Singapore

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Biography:

Yuji Iwahori has completed his BSc degree from Department of Computer Science, Nagoya Institute of Technology, MS and PhD degree from the Department of Electrical and Electronics, Tokyo Institute of Technology in 1985 and 1988, respectively. He had joined Nagoya Institute of Technology in 1988 and then became a Professor in 2002. He has joined Chubu University as a Professor since 2004 with experience of the Department Head of Computer Science. In the meanwhile, he has been a Visiting Researcher of the University of British Columbia Computer Science, Canada. He has also been a Research Collaborator with Indian Institute of Technology, Guwahati. His research interests include computer vision and application of machine learning. He has published over 220 scientific papers of journals and international conferences.

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In the computer vision and machine learning fields, image recognition and its application technologies are more and more becoming popular in recent years. In this talk, application of computer vision to medical endoscope and SEM images using neural network is introduced for the approaches we have developed in recent years. Endoscope images are used for the supporting system of the medical diagnosis including 3D shape recovery and pattern classifications, where automatic polyp detection and classification of benign or malignant are investigated based on the recent machine learning approaches including deep learning, while SEM images are used to recover 3D shape for the industrial applications using neural network.

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Emdad Khan is Chairman of InternetSpeech which he founded with the vision to develop innovative technology for accessing information on the internet anytime, anywhere, using just an ordinary telephone and the human voice. He is a Faculty at Maharishi University of Management, Iowa, USA and a Research Professor at Southern University, Louisiana, USA. He holds 23 patents and published over 75 journal and conference papers on intelligent internet, natural language processing/understanding, machine learning, big data, bioinformatics, software engineering, neural nets, fuzzy logic, intelligent systems and more. He has developed the prototype of voice internet and semantic engine using brain-like approach.

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Automation had started in the late 18^th century with mechanization of textile industry and initiated the first industrial revolution. It then continued and started the second industrial revolution in early 20^th century when Henry Ford mastered the moving assembly line and ushered in the age of mass production. The first two industrial revolutions made people richer and more urban. The biggest benefit of automation is that it saves labor. However, it is also used to save energy and materials and to improve quality, accuracy and precision. Now a third revolution is under way. Manufacturing is going digital. And what would be the next? We believe it will be intelligent agent based robots (soft-bots) that will take industrial automation digital manufacturing to the next level. Such robots will also communicate more naturally with human and machine. The dominant mechanism for natural communication is Natural Language Understanding (NLU) and processing. This study focuses on the key issues of robots to drive industrial/manufacturing automation and discusses specifically the NLP (Natural Language Processing) algorithms and Intelligent Agent (IA); the two core components of future automation. NLP is very important for the best HCI (Human Computer Interaction): Natural language based interaction, in general, is the most preferred communication with man as well as machine. Clearly understanding user’s input by IA is also the key to take necessary actions. And NLP can also make the search space significantly smaller in taking necessary actions. The core to NLP is a semantic engine that can understand the semantics and is critical for any complex NLP based applications. Semantic Engine is also the key for cognitive computing. We will discuss a Semantic Engine using Brain-Like Approach (SEBLA) and associated NLP & NLU to address the key problems of intelligent robot based automation. SEBLA based NLU (SEBLA-NLU) resembles human Brain-Like and Brain-Inspired algorithms and hence is good at dealing with natural language based interactions. In fact, SEBLA and IA are also very critical to solve most Big Data problems, especially when data is dominated by text. Our proposed SEBLA and IA based solution would make it much easier to effectively use robots (and softbots) by non-technical, semiliterate, illiterate as well as by technical people.

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Zhou Xing has completed his PhD degree in Particle Physics with his thesis focusing on large scale statistical data analysis, utilizing neural network methodologies on various analysis projects at LHCb experiment at CERN. He has joined Stanford Linear Accelerator Center (SLAC) National Laboratory as Engineering Physicist/Faculty and Staff, working on data acquisition and analysis. He has also joined a leading China EV company, NIO, where he specializes on a few deep-learning driven fields related to applications of autonomous driving, including supervised learning for semantic segmentations/road segmentations, moving object detections, trajectory prediction using optical camera as well as LIDAR measurements, reinforcement learning of continuous control, policy-based semi-model controlled reinforcement learning methods, etc.

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A prediction of driver’s intentions and their behaviors using the road is of great importance for planning and decision making processes of autonomous driving vehicles. In particular, relatively short-term driving intentions are the fundamental units that constitute more sophisticated driving goals, behaviors, such as overtaking the slow vehicle in front, exit or merge onto a high way, etc. While it is not uncommon that most of the time human driver can rationalize, in advance, various on-road behaviors, intentions, as well as the associated risks, aggressiveness, reciprocity characteristics, etc., such reasoning skills can be challenging and difficult for an autonomous driving system to learn. In this article, we present a disciplined methodology to build and train a predictive drive system, which includes various components such as traffic data, traffic scene generator, simulation and experimentation platform, supervised learning framework for sequential data using Recurrent Neural Network (RNN) approach, validation of the modeling using both quantitative and qualitative methods, etc. In particular, the simulation environment in which we can parameterize and configure relatively challenging traffic scenes customize different vehicle physics and control for various types of vehicles such as cars, SUV, trucks, test and utilize high definition map of the road model in algorithms, generate sensor data out of Light Detection and Ranging (LIDAR), optical wavelength cameras for training deep neural networks is crucial for driving intention, behavior, collision risk modeling since collecting statistically significant amount of such data as well as experimentation processes in the real world can be extremely time and resource consuming.

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Sunan Huang has completed his PhD degree from Shanghai Jiao Tong University. He was a Postdoctoral Fellow in the Department of Electrical Engineering and Computer Sciences, University of California at Berkeley. He was also a Research Fellow in the Department of Electrical and Computer Engineering, National University of Singapore, a Visiting Professor in Hangzhou Dianzi University. He is currently a Senior Research Scientist in Temasek Laboratories, National University of Singapore. He has co-authored several patents, more than 120 journal papers and four books entitled Precision Motion Control, Modeling and Control of Precise Actuators, Applied Predictive Control and Neural Network Control: Theory and Applications. He is also a Member of the Editorial Advisory Board, Journal of Recent Patents on Engineering, a Reviewer Editor of Journal of Frontiers in Robotics and AI and an Associate Editor of The Open Electrical and Electronic Engineering Journal.

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Currently, cooperative control of a multiple Unmanned Aerial Vehicle (UAV) system is attracting growing interest. This is motivated by growing number of everyday civil and commercial UAV applications. One of the core problems in the multi UAV system is motion planning, where each UAV navigates path to the target by sharing other UAV information. This requires collision-free path during the UAV motion control. Thus, the topic of UAV collision avoidance has driven a development of various control technologies in this area. In this talk, we first review the development of multiple UAV systems and collision avoidance. Therefore, we focus on a distributed collision avoidance algorithm which is proposed in a multi-UAV system. The basic idea is to use the cooperative control concept to generate heartbeat message, where multi-UAV communication is used to exchange UAV information and the fusion technology is used to merge them. With the heartbeat message fused, the own UAV is to select the velocity command to avoid only those UAVs or obstacles which are within a certain range around the own UAV. The velocity obstacle algorithm is adopted for collision avoidance control. This control is in a distributed form and each UAV independently makes its own decision. Finally, in this talk, we will show the flight test of the proposed method implemented on several real UAVs.

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