Day 2 :
Keynote Forum
Magnus S Magnusson
University of Iceland, Iceland
Keynote: T-Patterns, external memory and mass-societies in proteins and humans: in an eyeblink the naked ape became a string-controled citizen
Time : 9:30 - 10:00
Biography:
Magnus S Magnusson is a Research Professor, Founder and Director of the Human Behavior Laboratory, University of Iceland. He has completed his PhD in 1983 from University of Copenhagen. He is the Author of the T-pattern model and detection software THEMETM (PatternVision.com), focused on real-time organization of behavior. He has Co-directed DNA analysis. He has numerous papers (>1700 citations) and talks/keynotes in ethology, neuroscience, mathematics, religion, proteomics and mass spectrometry. He was the Deputy Director 1983-1988, in National Museum of Natural History, Paris. He has been repeatedly invited as a temporary Professor at the University of Paris, V, VIII and XIII.
Abstract:
This talk presents a self-similar pattern type called T-pattern, a kind of statistical peseudo fractal recurring with significant translation symmetry on a single discrete dimention. It now comes with a specialized detection (evolution) algorithm implemented as the software THEMETM for Windows (see patternvision.com), which has allowed the discovery of numerous and complex interaction patterns in many kinds of human and animal interactions as well as in neuronal interactions within living brains. T-patterns have also been detected in interactions between robots and humans and also seem characteristic for the structure of DNA and text. A definition of T-patterns is presented as well as the essentials of the current detection algorithms including examples of detected T-patterns using the specially developed T-pattern diagrams. The T-pattern is now a part of a larger set of pattern types and relations called T-system that will be shortly described including examples of patterning detected with specially developed algorithms also implemented in THEMETM. The potential importance of T-patterns is finally illustrated through a comparison of human mass societities and the mass societies of proteins within biological cells (sometimes called “Cell City”), where self-similarity of organization evolved over billions of years is striking from nano to human scales based on self-similar T-patterns, but appearing suddenly among large-brain animals in humans only and partly based on massively copied standardized T-patterned letter strings such as holy books and constitutions.
Keynote Forum
Richard Satava
University of Washington, USA
Keynote: Future of Healthcare: Surgical robotics and the fifth generation-non-invasive procedures
Time : 10:00-10:30
Biography:
Richard Satava is a Professor of Surgery at the University of Washington Medical Center, and Senior Science Advisor at the US Army Medical Research and Materiel Command in Ft. Detrick, MD. His prior positions include Professor of Surgery at Yale University and a military appointment as Professor of Surgery (USUHS) in the Army Medical Corps assigned to General Surgery at Walter Reed Army Medical Center and Program Manager of Advanced Biomedical Technology at the Defense Advanced Research Projects Agency (DARPA).
Abstract:
Non-healthcare industries have used a wide spectrum of robotic and energy-based systems for literally all different purposes, from manufacturing to artist creations, whereas only a small portion of these commercially available systems have been exploited by physicians. Although many of the technologies are large and sophisticated image-guided systems, numerous other technologies are small, hand-held portable systems. Thus, the operating room or procedure room of the future may well need to be reconfigured for some of the larger systems, enabling new forms of non-invasive therapies; on the other hand, many other time-honored procedures will be performed as outpatient or office procedures with small, hand-held devices. Although the military (DARPA) has demonstrated a prototype robotic surgical system for the battlefield, it has yet to complete development. An important area in robotics that has not been exploited by surgery is the area of automation. The great majority (of the few existing) surgical robots are actually single location-based tele-operated systems, with virtually no automation. Even the initial success in true remote tele-surgery has been discontinued. There are opportunities in the area of control motion and feedback that could revolutionize surgical robotics – such as developing ‘virtual fixtures’ (constraints) or automating simple tasks like suturing. Surgical robotics is in their infancy. Even as this fourth revolution in surgery in 25 years (robotic surgery) is gaining in popularity, a much more disruptive change is beginning with the next revolution: Directed Energy Surgery. While surgeons have been investigating a few different types of energy for decades, including success with some forms such as lithotripsy, photonics, high-intensity focused ultrasound (HIFU), etc., these pioneering techniques are but the tip of the iceberg that heralds the transition to non-invasive surgery. Such systems are based upon the premise which robotics and automation can bring – precision, speed and reliability, especially as surgery ‘descends’ into operating at the cellular and molecular level. Richard Feynman was right – this is “room at the bottom”!
Keynote Forum
Petra Perner
Institute of Computer Vision and applied Computer Sciences, Germany
Keynote: Multi time-series mining for medical, engineering, and smart maintenance purposes in order to figure out critical system statuses
Time : 10:50-11:20
Biography:
Petra Perner is the Director of the Institute of Computer Vision and Applied Computer Sciences IBaI. She has received her Diploma degree in Electrical Engineering and her PhD degree in Computer Science for the work on data reduction methods for industrial robots with direct teach-in-programing. Her habilitation thesis was about a methodology for the development of knowledge-based image-interpretation systems. She has been the Principal Investigator of various national and international research projects. She has received several research awards for her research work and has been awarded with three business awards for her work on bringing intelligent image interpretation methods and data mining methods into business. Her research interest includes image analysis and interpretation, machine learning, data mining, big data, machine learning, image mining and case-based reasoning.
Abstract:
In many applications multiple time series of measurement parameters are taken. The aim is not to forecast how the single time series will evolve. The aim of this study was to figure out when a biological system, an engineering system, or a system under observation will go into a critical status that requires immediately action to preserve the system. This task requires different intelligent observations from prediction to decision making over multiple time-parameters. Often the measurement data points are not equidistant. They are often on different time-intervals and they have to be brought into a common time interval by adequate interpolation methods. The status of the system in the past and how it will be behaving in the future will also play an important role. That does not bring it into a single point observation but rather into a more complex consideration that needs to take into an account the system status. We will show on different application how such an application can be solved. We will review the state of the art of single time-signal prediction. We will show how the system theory method has to be applied. We demonstrate that it is necessary to take the system theory quotation into account to solve the problem, it does not matter if it is a biological, engineering, or maintenance object; and finally, we will show on different application how we solved the applications with system-theory data mining methods.
Keynote Forum
David Moss
People Power, USA
Keynote: Building smart AI assistants to transform the competitive landscape of the IoT market
Time : 11:20-11:50
Biography:
David Moss is the President, CTO, and Co-founder of People Power Company. His talents span the full technology spectrum, including hardware design, embedded firmware, wireless sensor networks, cloud platforms, mobile apps, user experiences, product management, and business development. Prior to People Power, he worked with Bitfone Corporation as both as Java Developer and Firmware Engineer, where he integrated Bitfone’s revolutionary over-the-air firmware updates into tens of millions of Motorola phones. His open source contributions have been used by thousands of developers around the world.
Abstract:
We’re in an exciting era of computing beyond the boundaries of mobile phones. Let’s face it, app development has hit a wall, smart home adoption is slow and developers are hungry for the next big thing. There’s a huge market opportunity for developers in the IoT industry with AI assistants. We’re on the cusp of a new economy driven by AI assistants, which provide a 24/7 consciousness that learns from users’ personal data to identify patterns and anticipate problems. Today’s smart home products are too dumb to create actual value for mass consumer adoption. Consumers don’t need more devices to make their lives more convenient, they instead need personalized AI-enabled software services designed to meet their individual needs. AI assistants enable service providers the opportunity to deploy sophisticated machine learning-based services. With pattern behavior analysis from deep-learning algorithms that mimic the human brain, AI assistants track adjustments to smart home devices, like smart lighting or connected thermostats, and automatically adapt to consumer’s routines. They enable reoccurring micro-services, like monitoring energy usage, that don’t require a phone screen like apps do. AI assistants deliver smart home services beyond simple screen interactions and get deeper into people’s lives to better manage their personal and home preferences. The author will demonstrate how to create AI assistant services, leveraging open source tools and IoT platforms. He’ll focus on the architecture of AI assistants, forming a representative business model which can incorporate intelligent assistants and coordinating devices and data sources to make products work intelligently together.
Keynote Forum
Andy Pandharikar
COMMERCE.AI, USA
Keynote: How Is artificial intelligence disrupting commerce?
Time : 13:30-14:00
Biography:
Andy Pandharikar is the CEO of COMMERCE.AI, which develops AI for Commerce. Before that, he co-founded Fitiquette based in SF, which got acquired by Flipkart group, India’s largest online retailer. Fitiquette had developed machine-learning technology for fashion e-commerce and was selected as the top commerce-tech startup at Techcrunch Disrupt SF 2012. He held various product and engineering positions at Cisco. He is also a Member of SF Angels Group and has invested in over 12 startups. He attended MS in Management Science and Engineering at Stanford University and obtained an Executive Degree at Harvard Business School. He is an outdoor enthusiast, an ultra-marathoner, and a certified lead rock climber.
Abstract:
There are over five billion unique products sold worldwide with over 30,000 new products introduced every month. From product design to manufacturing, to merchandising, to supply chain, to delivery, all the blocks in commerce are getting dismantled and rebuilt with the help of machine learning and deep learning. How are brands and retailers leveraging algorithms and data to win in this changing landscape? Where will it all leads to? Which problems need to be solved in order to realize autonomous commerce? I will share our learning from building an applied AI startup with our vision for self-driving commerce.
Keynote Forum
Marzieh Jalal Abadi
data61|CSIRO, Australia
Keynote: MotionSpy-vibration energy harvesting sensor can track train passangers using machine learning techniques
Time : 14:00-14:30
Biography:
Marzieh Jalal Abadi has completed her PhD at University of New South Wales (UNSW) in 2016 and joined data61-CSIRO as Research Associate in 2017. Her research is in machine learning, sensory data, IoT and cybersecurity.
Abstract:
Today’s mobile devices are equipped with a range of embedded sensors. These sensors can be used to infer contextual information such as location, activity, health, etc. and thus enable a range of applications. Recent research has demonstrated that applications with access to data collected from GPS, accelerometer and even device battery profile can accurately track the location of users as they move about in urban spaces. In recent years, vibration energy harvesting (VEH) has emerged as a viable option for mobile devices to address the inadequacy of current battery technology. VEH harnesses power from human motions and ambient sources and it could be used as a motion sensor. This is due to the fact that different ambient vibrations and human motions produce a unique pattern of energy in the VEH circuit. In this paper, we reveal that VEH signal contains rich information and it is possible to precisely identify the trip using machine learning techniques. A typical train ride consists of episodes of continuous motion interspersed with brief stoppages at train stations. Our key hypothesis is that the train tracks between any two consecutive stations create a unique vibration characteristic that is reflected in the VEH data and we model it using machine learning techniques. Then, we leverage the sequential nature of a trip to correct the occasional segment misclassifications and ultimately infer the entire trip. To demonstrate our hypothesis, we collected real-world motion data from 4 distinct train routes in the Sydney metropolitan area. Our data set includes motion data from 36 trips. To exploit a thresholding-based segmentation algorithm and extract the individual segments, we employ different machine learning classifiers and ensemble classifier achieves accuracy of 60.9% for identifying individual segments. Finally, we use the sequential properties of a train trip and achieve a trip inference accuracy of 97.2% for a journey of 7 stations.
Keynote Forum
Francis X Govers
Bell Helicopter Textron, USA
Keynote: The risk of artificial intelligence: Evaluation of risk and safety in learning capable unmanned systems
Time : 14:30-15:00
Biography:
Francis X Govers III develops autonomous vehicles for Bell Helicopter. Previously, he has worked as Chief Robotics Officer for Gamma 2 Robotics; Chief Engineer of Elbit Systems Land Solutions; Special Missions Manager for Airship Ventures; Lead Engineer for Command and Control of International Space Station and Deputy Chief Engineer of US Army Future Combat Systems. He has also participated in DARPA Grand Challenge and DARPA EATR project. He has been author of over 40 articles on Robotics and Technology.
Abstract:
Unmanned vehicles, drones, self-driving cars and other sorts of advanced autonomous vehicles are being announced on an almost daily basis. Uber is working on flying taxis, every car company has a self-driving car in the works, and drones are the hottest Christmas toy for people of all ages. Inside these, autonomous vehicles are systems based on advanced artificial intelligence, including artificial neural networks (ANN), machine learning based systems, probabilistic reasoning, and monte-carlo models providing support for complex decision making. One of the common concerns about autonomous vehicles, be they flying or driving, is for safety. Safety testing is usually based on deterministic behavior, the aircraft or car or boat, which faced a similar situation, behaves the same way every time. But, what happens when the vehicle is learning from its environment, just as we humans do. Then it may behave differently each time based on experience. How then to predict and evaluate in advance? How safe an autonomous system might be? This paper will present two complementary approaches to this problem. One is a stochastic model for predicting how an autonomous system might behave as it learns over time, providing a range of behavioral responses, to be used as a risk assessment tool? The other is a set of methods and standards for writing test procedures for such vehicles.
Chair
Andrzej Buchacz
Silesian University of Technology, Poland
Co-Chair
Meenakshi Nadimpalli
Reliable Software Resources Inc, USA
Session Introduction
Farinaz Behrooz
Universiti Putra Malaysia, Malaysia
Title: FCM-Fuzzy cognitive map (fcm) method for designing intelligent control algorithm on mimo nonlinear systems
Time : 15:00-15:20
Biography:
Farinaz Behrooz has completed her PhD in Control and Automation Engineering from Universiti Putra Malaysia, her Master’s degree in Smart Technology and Robotics Engineering and Bachelor’s degree in Electronics Engineering. She has published four papers in reputed journals and has fi led a patent in Malaysia. She has more fi ve papers under revision and modifi cation in reputed journals.
Abstract:
This is a simple technique that can meet the needs of the system purposes like nonlinearity, MIMO, coupling eff ect, uncertainty and complexity. FCM method is the combination of fuzzy systems and neural networks methods which contains the robust properties of both methods. As the structure of FCM has the ability to design the controller based on the needs of the system and what is expected from the system to do, not how it works, it could be a suitable solution for nonlinear MIMO systems. The structure of FCM is a graph structure with simple mathematics based on tendencies or goals of the system. Th erefore, the designing of the controller is not involved with complexity of the system and complex mathematical analysis for deriving the control law. In this study, in control scenario by FCM, the inputs and outputs of the system with other eff ective parameters in the process like actuators could be considered as concepts (MIMO characteristic). Th e nodes are connected to each other based on the relationship or eff ect of them on each other (possible coupling eff ects). Finally, the control signals are applied to the nonlinear dynamic model of the system. Consequently, the nonlinear MIMO controller with simple mathematics and algorithm by considering coupling eff ect could be designed based on soft computing method of FCM in order to apply on nonlinear MIMO systems. Th e benefi ts of FCMs are fast convergence due to the less mathematical calculation and analysis and helping to omit the artifi cial decoupling of variables due to its construction, accordingly the accuracy and sensitivity of control can be increased.
Hassan Mukhtar
Concordia University, Canada
Title: Supplier quality management in global supply chain; risks associated, quality issues, outsourcing, and other issues related with global supply chain
Time : 15:20-15:40
Biography:
Hassan Mukhtar is a PhD student in Information and Systems Engineering at Concordia University. He has immense interest in global supply chain. His articles are published in reputed publications like Research gate, and Inderscience.
Abstract:
The paper is about the supply chain management with respect to quality at global level. Th e issues in supply chain management at global level have been detailed and outlined in the paper. Th e most notable issues have been risks associated, quality issues, outsourcing, and other issues related with global supply chain management. Th e concept has been divided into time phases that start from the time prior to 1990 to present. Th e paper states that integration and collaboration are very important in global Supply chain management issues. In order to be successful and effi cient, the paper proposes that global supply chain management must be integrated, and global perspective must be taken in order to be successful.
Rujeko Masike
Harare Institute of Technology, Zimbabwe
Title: Experimental analysis for optimization of a bernoulli principle based climbing robot adhesion system
Time : 15:40-16:00
Biography:
Rujeko Masike is a PhD Scholar at Amity University in Gurgaon, Haryana, India. She is the Chairperson of the Industrial and Manufacturing Engineering department at Harare Institute of Technology. She has published more than fi ve papers in reputed journals and has been conference chair for two international conferences of repute
Abstract:
Climbing capability is a characteristic that robotic researchers have been intensely pursuing in the last decade for climbing robots. Emphasis has been on minimizing energy expenditure, increasing payload and traversing diff erent wall materials. Using the Bernoulli principle as inspiration, important principles are revealed for reliable maneuvering on vertical structures. An experimental identifi cation of a model for a Bernoulli principle-based holding force is described. To quantitatively evaluate requirements of a Bernoulli pad to achieve attachment on a wall, this paper presents the force analysis and conducts experimental verifi cation for a commercially available Bernoulli pad. By designing and using a test bed, optimal holding force that ensures complete attachment of the pad is defi ned and experimentally verifi ed. Factors that infl uence the holding force such as fl uid media, frictional force, robot state, air speed, height of pad from surface and density variations are experimentally investigated and their causes and eff ects are established. Th e methods proposed in this study are valuable in guiding the design of pneumatics-based adhesion devices such as wall-climbing robots. Th e results from the experiments would then lead to the design of an adaptive force which would enable diff erent fl uid media to be used therefore increasing the versatility of the adhesion system. Th ey would also enable optimization of the Bernoulli principle therefore increasing holding force and payload. Th e cause and eff ect of these parameters were confi rmed through fi nite element analysis using ANSYS and simulation using Matlab.
Biography:
Mustafa Gelen has completed his Mechanical Engineering (2017) at Istanbul Technical University, Turkey. He worked as a Thesis Trainee at Arcelik group at Turkey. He worked as a Manufacturing Tool Design Engineer at Eczacibasi Yapi Urunleri Grubu, Turkey. Currently he is working as a Senior Quality Engineer at Ford Otosan, Turkey
Abstract:
Ford Otosan’s Yeniköy plant, which assembles the transit courier and tourneo courier, has implemented an adaptive image processing system to inspect the vehicle in terms of part presence/absence, vehicle complexity and specifi cation requirement automatically. In the assembly plant, when the vehicle is fi nished, the vehicle goes through customer acceptance line (CAL) for fi nal inspection list of checks to make sure all of the parts are in place, doors fi t properly, wipers work etc., to ensure the vehicle meets engineering specifi cations and customer expectations as a part of quality management system. Th is inspection workfl ow is crucial to eliminate customer complaints, warranty costs and decrease of customer satisfaction. All these checks were being done manually by operators aft er implementing adaptive image processing system, correct exterior trim part availability and specifi cation check have been started to be done automatically. With adaptive image processing system, labor effi ciency has been improved, non-value-added processes have been eliminated and human errors on manual processes have been completely reduced to zero.
Harish Kumar Sahoo
Veer Surendra Sai University of Technology, India
Title: Sparse adaptive fi ltering for wireless channel estimation and equalization
Time : 16:40-17:00
Biography:
Harish Kumar Sahoo is currently working as Associate Professor in the Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, India. Prior to his present assignment, he had worked for six years in International Institute of Technology, Bhubaneswar, India. He has more than 15 years of teaching and research experiences. He has received his MTech degree from National Institute of Technology, Rourkela, India and PhD from Sambalpur University, India. He is a Senior Member of IEEE and Life Member of ISTE. He has published several journal and conference papers in Elsevier and IEEE. He is an Active Reviewer of IEEE Transaction on instrumentation and measurement, IEEE Transaction on power delivery, Elsevier, Springer as well as Taylor and Francis journal publishing companies. His research interest includes adaptive fi ltering and soft computing applications in wireless communication
Abstract:
Rayleigh’s distribution is mainly used when fading wireless medium does not have proper line of sight (LOS) path and is dominated by a large number of non-line of sight (NLOS) paths due to refl ections of the received signal. Also because of relative motion of the base station and mobile station, a random frequency shift is generally introduced in the carrier, which can be realized in terms of Doppler spread. In case of Rayleigh’s fading channels, generally two critical problems arise in receiver design those are accurate estimations of channel coeffi cients followed by mitigation of channel impairments like inter symbol interference (ISI) and fading in presence of user mobility. Th e accuracy of estimated channel state information (CSI) is really crucial to design robust equalizer for reconstruction of bit sequence and the equalizer performance is aff ected by the fading rate and Doppler spread. Th e main research is oriented towards the exploitation of underlying sparseness of block adaptive fi lters through L0-norm penalty for accurate estimation with stable convergence which helps to design computationally effi cient adaptive models that can be eff ectively used for practical applications. Block and fast block processing can be quite eff ective techniques for outdoor fading wireless environment with proper choice of modulation formats.
Chair
Andrzej Buchacz
Silesian University of Technology, Poland
Co-Chair
Meenakshi Nadimpalli
Reliable Software Resources Inc, USA
Session Introduction
Randika K W Vithanage
Glasgow Caledonian University, UK
Title: A detailed kinematic analysis of a 6-articulated industrial robot
Time : 11:50-12:10
Biography:
Randika K W Vithanage has received his BSc degree in Mechanical Engineering from University of Moratuwa, Sri Lanka in 2010. He is currently a PhD Scholar at Glasgow Caledonian University, where he is awarded a Competitive Scholarship to fund his PhD, he has worked as a Senior Manufacturing Engineer at Toshiba TEC, Singapore where he was recognized as employee of the year and named Inventor on a patent. His research interests include industrial robots, robotic sensing, and manipulation of robots in unstructured environments.
Abstract:
Due largely to the growing emphasis in academic research on industrial robots and their applications, it is oft en required by researchers to understand and examine the kinematic aspect of such robots. Obtaining both forward and inverse kinematic models of a given industrial robot could be a tedious and intricate task. Th erefore, this paper presents a detailed kinematic analysis of a 6-axis industrial robot that commonly found in present-day industry and research laboratories. Th e proposed kinematic solutions have been validated against the simulation soft ware provided by robot’s supplier and an error analysis has been done to ensure the accuracy. Th ere are particular models of robots which are well discussed in the fi eld of robotic research and also within the domain of kinematic analysis. Th e Puma 560 by Unimation, also known as “white rat” of robotics is one of such robots which catalyzed robotic research for decades, and well examined in textbooks and research articles. However, the eminence of such robots is gradually being replaced by modern 6-axis industrial robots. Perhaps due to its size, prize, availability or consideration of health and safety aspects, the Fanuc LR Mate 200iD is becoming increasingly popular in the industry as well as in the research laboratories. Alternatively, there are a limited number of research articles which examine the kinematics of present day industrial robots. Further, the majority of those articles barely discusses the results and lacks the scientifi c validation of proposed solutions. Th erefore, the main focus of this article was to generate and validate the both forward and inverse kinematic models of a popular and modern industrial robot – the Fanuc LR Mate 200iD.
Mi-Ching Tsai
National Cheng Kung University, Taiwan
Title: Active vibration control of piezoelectric self-sensing actuators based on velocity observer
Time : 12:10-12:30
Biography:
Mi-Ching Tsai has completed his PhD in Engineering Science from the University of Oxford UK in 1990. He is currently a Chair Professor at the Department of Mechanical Engineering, National Cheng Kung University, Taiwan. He has authored or coauthored more than 117 journal papers and holds more than 100 patents. His research interests include robust control, servo control, motor design, and applications of advanced control technologies using DSPs. He is a Fellow of the Institution of Engineering and Technology, UK and previously served as an Associate Editor of the IEEE/ASME Transactions on Mechatronics from 2003 to 2007 and the Deputy Minister of the Ministry of Science and Technology, Taiwan from 2016 to 2017.
Abstract:
Piezoelectric self-sensing actuators (SSAs) have been extensively used in vibration control of fl exible structures over the last three decades. Compared to separated sensor/actuator systems, the SSA is simple, robust, and cost-eff ective. According to the literature, the specially designed electric circuit, referred to as a bridge circuit, is required to realize the concept. A method of achieving self-sensing capability without a bridge circuit is proposed by utilizing a velocity observer, and then the vibration velocity of a SSA can be estimated by the measured voltage and current signals. Th us, the SSA active vibration control can be implemented without using a physical velocity sensor to achieve the required vibration suppression based on feedback control design. Th e SSA vibration suppression performance is highly dependent on the equivalent mechanical admittance, which consists of equivalent mass, stiff ness, and damping. Furthermore, the equivalent stiff ness and damping will be directly influenced by the controller parameters. Th us, the SSA vibration suppression performance can be adjusted by the control design. The experimental results show that the proposed method can eff ectively reduce the structural resonance phenomenon when the controller parameters of the SSA are properly designed with a required mechanical admittance.
Michael Truell
Horace Mann School, USA
Title: A universal robotic control system using reinforcement learning with limited feedback
Biography:
Michael Truell is presently at the Horace Mann School in Bronx, NY.
Abstract:
A mobile robot deep reinforcement learning system is created that converges on common robotic tasks using four times less feedback than pre-existing solutions. Th e system achieves this leap in effi ciency through context-aware action selection and aggressive online hyper-parameter optimization while still maintaining performance on embedded hardware. A core algorithm of deep wire fi tted q-learning is supplemented with active measurement of robot uncertainty, defi ned as the derivative of error between expected and received reward. Th is uncertainty value directly scales the temperature of Boltzmann probabilistic exploration policy in addition to the learning rate of stochastic gradient descent. Furthermore, to provide generality across robots and tasks, neural network topology is effi ciently evolved throughout training and evaluation. Finally, experience replay is extended to changing environments and is integrated with our uncertainty value. Human operators successfully trained the system on multiple robots in a matter of minutes to perform tasks such as driving to a point with a diff erential drive system, following a line using holonomic Swedish wheels or playing ping pong with a robot arm. All are without any manual hyperparameter adjustment in both simulation and hardware.
Sergey Mikhailovich Afonin
National Research University of Electronic Technology, Russia
Title: Condition of absolute stability for automatic control system of deformation piezo actuator for nanotechology
Time : 15:00-15:20
Biography:
Sergey Mikhailovich Afonin is an Associate Professor of Department of Intellectual Technical Systems of National Research University of Electronic Technology (Moscow Institute of Electronic Technology MIET). He is a graduate of the National Research University of Electronic Technology MIET, Engineer in Electronic Technology 1976. He has completed his PhD in Electronic Technology Engineering and Control Systems from MIET 1982. He has received academic title of Senior Researcher from MIET 1991. He has received different positions such as: Aspirant MIET 1976–79, Junior Researcher MIET 1979–82, Senior Researcher MIET 1983–93, Associate Professor at MIET since 1993 to present time. He has more than 200 scientifi c papers to professional publication and 16 inventions. He is the Recipient of silver medal and two bronze in VDNKh Russia.
Abstract:
The piezo actuator is using in the automatic control system in the scanning tunneling microscopes, the scanning force microscopes and the atomic force microscopes for the nanotechnology. Th e piezo actuator is the piezo mechanical device intended for the actuation of the mechanisms, the systems or the management based on the piezo eff ect, converts the electrical signals into the mechanical movement and the force. Th e correcting devices are chosen the high quality of the automatic control systems of the deformation the piezo actuator. Th e analytical expressions for the suffi cient absolute stability conditions of the system with the hysteresis nonlinearity of the electro-magneto-elastic actuators are written using the Yakubovich absolute stability criterion with the use of the derivative for the characteristic deformation of the piezo actuator. Th e Yakubovich criterion is the development of the Popov absolute stability criterion. For the stable control system on Lyapunov, the Yalubovich absolute stability criterion for the systems with the single hysteresis nonlinearity provides the simplest and pictorial representation of results of the investigation of the stability and the possibility of the synthesis of the correcting devices of the system ensuring the stability of the strain control systems with the piezo actuator. In the condition of the absolute stability of the control system for the deformation the piezo actuator of the nanomanipulator is used the value of the tangent of the angle of the tangent line to the hysteresis nonlinearity for the piezo actuator. Th e stationary set of the automatic control systems of the deformation the piezo actuator is the segment of the straight line. Th e conditions of the absolute stability of the automatic control systems with the piezo actuator in the case of longitudinal, transverse and shift piezo eff ect for the hysteresis characteristic of deformation of the piezo actuator are obtained. Th e obtained absolute stability condition with the use of the derivative for the characteristic deformation of the piezo actuator for the automatic control system with the piezo actuator allow one to estimate and calculate the characteristics of the correcting devices of the control system of the deformation the piezo actuator.
Jagadeesh Shanmugam Hariharan Natarajan
North Carolina State University, USA
Title: Cognitive vision principle for conceptual learning of colors
Time : 15:20-15:40
Biography:
Jagadeesh Shanmugam Hariharan Natarajan is currently pursuing Master of Science in Industrial Engineering at North Carolina State University and has completed his Bachelor’s in Mechatronics Engineering from Anna University in India. He has spent his last semester of his under-graduate degree working as a Research Assistant at Nanyang Technological University in Singapore, during which he realized how insights from data can make important decision at various circumstances. Hence, he is currently focusing his career on Data Analytics. Also, he has actively participated in the Summer Research Fellowship at Indian Institute of Technology, Madras.
Abstract:
Color is a powerful form of communication among human beings. Sociable robots that live and coexist with humans must also learn colors from the society it lives. A lot of research has been performed to enable computer, as the brain of a robot, to learn colors. Most of them rely on modeling of human color perception and mathematical complexities. Diff erently, this work targets on developing the capability of the computer to use machine learning approaches to learn the colors through human interaction. Th e diff erent colors which is being detected in the camera is processed by using image processing tool OpenCV and the most dominant color of the picture is identifi ed and displayed in the system. Th e user can now teach the computer the difference between the appropriate colors using the RGB values. Th erefore, although at the beginning, the computer does not know any colors, eventually through interaction, it learns numerous colors which will indicate the shared color learning with humans in the society. Aft er teaching the computer a number of times, it is able to classify the colors by matching with RGB values for that particular color from the database. If the color does not exist, the computer identifi es the closest possible color using the unsupervised machine learning technique k-means clustering. Aft er learning colors from the society, the developed algorithm is implemented in the NTU Singaboat, which is an Unmanned Surface Vehicle (USV) built for competing in the Maritime RobotX Challenge.
- Video Presentations
Session Introduction
Bharat Bhargava
Purdue University, USA
Title: Intelligent autonomous systems
Time : 16:00-16:20
Biography:
Bharat Bhargava is a Professor of Computer Science at Purdue University. He is conducting research in security and privacy issues in Service Oriented Architecture (SoA) and Cloud Computing. He has won six best paper awards in addition to the technical achievement award and golden core award from IEEE, and is a fellow of IEEE. He received outstanding Instructor Awards from the Purdue chapter of the ACM in 1996 and 1998. In 2003, he was inducted in the Purdue's Book of Great Teachers. He is Editor-In-Chief of four journals and serves on over ten editorial boards of international journals. He is the Founder of the IEEE Symposium on reliable and distributed systems, IEEE conference on Digital Library, and the ACM Conference on information and knowledge management. He has worked extensively at research laboratories of Air Force and Navy. He has successfully completed several Darpa and Navy STTR and AFRL projects. His recent work on controlled data dissemination in untrusted environments under attacks received the fi rst place in Purdue’s CERIAS Security Center Symposium held in March 2015.
Abstract:
Intelligent Autonomous Systems (IAS) are highly cognitive, refl ective, multitask-able, and eff ective in knowledge discovery. Examples of IAS include soft ware that is capable of automatic reconfi guration, autonomous vehicles, network of sensors with reconfi gurable sensory platforms, and an unmanned aerial vehicle respecting privacy by deciding to turn off its camera when pointing inside a private residence. Research is needed to build systems that can monitor their environment and interactions, learn their capability, and adapt to meet the mission objectives with limited or no human intervention. Th e systems should be fail-safe and should allow for graceful degradations while continuing to meet the mission objectives. Th is presentation will advance the science of autonomy in smart systems through enhancement in real-time control, auto-confi gurability, monitoring, adaptability, trust. I will present research ideas in smart autonomy, Multi-intelligence (MINT) Enterprise Analytics, and Rapid Autonomy prototype among others. Th e main objective is to realize a vision based on the following approaches: Employ machine learning techniques on sensor and provenance data to learn and understand the underlying patterns of interaction, conduct forensics to detect anomalies, and provide assistance in decision making by on-the-fl y semantic and probabilistic reasoning; Apply advanced data analytics techniques to incomplete and hidden raw system data (provenance data, error logs, etc.,) to discover new knowledge that contributes to the success of the IAS mission; Enhance the autonomous system’s self-awareness, self-protection, self-healing, and self-optimization by learning from the knowledge discovered through dataanalytics and Utilizing blockchain technology for storing provenance data for providing monitoring, trust, and verifi cation, using the WaxedPrune system developed for Northrup Grumman.
Fionn Murtagh
Department of Computing and Engineering, University of Huddersfi eld, UK
Title: “The Sciences of the Artificialâ€: Ultrametric topology of complex systems
Biography:
Fionn Murtagh is Professor of Data Science and was Professor of Computer Science, including Department Head, in many universities. Fionn was Editor-in-Chief of the Computer Journal (British Computer Society) for more than 10 years, and is an Editorial Board member of many journals. With over 300 refereed articles and 30 books authored or edited, his fellowships and scholarly academies include: Fellow of: British Computer Society (FBCS), Institute of Mathematics and Its Applications (FIMA), International Association for Pattern Recognition (FIAPR), Royal Statistical Society (FRSS), Royal Society of Arts (FRSA). He has been an Elected Member - Royal Irish Academy (MRIA), Academia - Europaea (MAE), Senior Member - IEEE.
Abstract:
The book with the title, “Th e Sciences of the Artifi cial”, is by Nobel Prize winner in 1978, Simon Herbert. At issue is cognitive processes and analytics from inherent hierarchical system complexity. We may be determining the extent of hierarchical nature and properties, perhaps including or determining evolution along the lines of geneology. First we address how inherently hierarchical various sources of data can be. Considered are time series that are fi nancial, environmental, biomedical, and texts that are from literature, from accident reports, and psychogically related dream reports. Th e use and benefi t of taking hierarchical structure fully into account includes the following: how high dimensional or sparse data become hierarchical, and application can be for proximity and related searching, leading to nearest neighbour regression. But far more than that is ultrametric regression, taking into account the ultrametric topology associated with hierarchical structure. For our cognitive processes and analytics, ultrametric regression is how cognitive and analytical processing determines such system properties for regression purposes. By using contiguity-constrained, i.e. here, chronological, hierarchical clustering, then through multivariate time series, and changepoint analysis, hierarchy expresses changes at varying scales. At issue, quite generally, are multivariate time series. Furthermore, our partitioning of the chronologically constrained hierarchical clustering, so as to segment the multivariate time series, and determine changepoints, this is carried out using a wavelet transform in the ultrametric topological space. Th e case study here, of ultrametric wavelet regression of multivariate time series, is through application to Colombian confl ict analysis.
- Poster Presentations
Session Introduction
Andrzej Wróbel
Silesian University of Technology, Poland
Title: Comparative tests of steering gear made of composite and aluminum alloy
Time : 16:40-17:00
Biography:
Andrzej Wróbel is a Lecturer in the Institute of Engineering Processes Automation and Integrated Manufacturing Systems, Silesian University of Technology. He is a specialist in the design, analysis of mechatronic systems and industrial automation. He is the Head of studies in the fi eld of Automation and Robotics Engineering Processes. He is a Member of the Professional Association in Modern Manufacturing Technologies ModTech Iasi-Romania and International Union of Machine Builders (Donetsk, Ukraine). He is the Manager of Laboratory of Visualization of Mechatronic Systems in the Center of the New Technology of the Silesian Technical University. He is the Editor in Chief of Journal “Selected Engineering Problems”. He is an author or a co-author of more than fi ve monographs and chapters in books and more than 70 articles.
Abstract:
The industry has played an important role for the development of the polish economy for centuries. Th e location of a given industry in a given part of the country depends on such factors as natural resources area, location of sale or a qualifi ed staff . Th e Upper Silesian Industrial District is the largest industrial district of Poland includes industrial companies in the centraleastern part of the Silesian Voivodship. In this area, the automotive industry and companies closely cooperating with this industry are a very strong branch of industry. An example of such cooperation is Nexteer-the leader in the innovative motion control delivery of electric and hydraulic steering systems, steering columns and driveline systems. Th e paper presented in this article attempts to replace standard materials of steering columns such as aluminum with new composite materials. Th e prototype of such a steering column has been done as a part of the research project PBS3/B6/37/2015 (PST-41/RMT2/2015)in cooperation of Nexteer and Institute of Engineering. Processes Automation and Integrated Manufacturing Systems. Faculty of Mechanical Engineering, Silesian University of Technology. Th e main objective of the research was to compare the noise generated during the work of previously manufactured gears and the new innovative gear housing made of composite. Aft er analyzing the fi rst two prototypes of the transmission, we managed to obtain results comparable to the results of the production version. Subsequent research that will be carried out will be related to thermographic studies of transmission subassemblies and assemblies as well as examination of moments and forces generated during transmission operation.
Igor Gorlach
Development of a low-cost automatic guided vehicle (AGV)
Title: Development of a low-cost automatic guided vehicle (AGV)
Time : 17:00-17:20
Biography:
Igor Gorlach has completed his PhD from North-West University in South Africa. He is the Chairperson at General Motors South Africa (GMSA) and Professor of Mechatronics at Nelson Mandela University.
Abstract:
Modern production systems utilize robotic and automation systems including automatic guided vehicles (AGVs) for a variety of material handling tasks. A low-cost AGV was initially designed for an assembly line at General Motors South Africa (GMSA). Th is paper presents the latest development and modifi cations of the AGV design. Th e main research focuses were to improve the AGV performance, simplify the operation and reduce the cost. Th e AGV is used as a tugger, which tows trolley between assembly stations in a pre-designed loop. However, it could also be employed to deliver unique or unusual parts between production lines in a more complex production environment. Th e improved AGV controller is based on a BeagleBone Black, which uses an ARM cortex-A8 processor for navigation, obstacle detection and logic processing. Th e navigation is achieved with a magnetic sensor that follows magnetic tape. Th e ultrasonic sensors are used to develop a safety zone ahead of the AGV to avoid obstacles. Th e proposed AGV design meets the criteria for an effi cient and low-cost autonomous material handling system. Th e developed AGV is capable of transporting tasks in various industrial environments and it can be easily reprogrammed to cater for very specifi c scenarios.
Rosemonica Bezerra De Jesus
University Federal of Bahia, Brazil
Title: Development of a phenomenological model for a battle reactor
Time : 17:20-17:40
Biography:
Rosemonica Bezerra De Jesus is currently in a Master’s degree program for Industrial Engineering and Chemical Engineer at the Federal University of Bahia (UFBA) Brazil. She has experience with projects, research and innovation. She has ability to use process simulators (ASPEN, HYSYS and UNISIM) and other engineering softwares such as MATLAB, AutoCAD and MsProject. In addition, she has knowledge in modeling, simulation, control and optimization of chemical and petrochemical processes.
Abstract:
This article refers to a phenomenological model of a batch reactor. Using an energy balance model it was possible to predict the heating and cooling behavior inside the reactor and how the temperature varies with the applied voltage. Th is work will be used for later emphasis on polymer syntheses, especially the hydrolytic synthesis of caprolactam, in order to obtain a product with a higher value-added market, nylon 6. Given the diffi culty of temperature control when operating with nylon 6, silicone oil (nylon-like characteristics) was used for system testing and data collection. In this way, the system boils down to a heating tank, with heating via electrical resistance. Th us, the focus of the present study will be on modeling the reactor using silicone oil. Th e study will be done obtaining dynamic measurements of temperature in order to be able to present a phenomenological model of the reactor. For the validation of the model, we used data collected in the plant from a steptype test. In this way, this text aims to develop the phenomenological model of the system in order to better understand the dynamics of the heating and thus enable future control studies. Th e model represented a satisfactory behavior of the reactor in question, presenting an average relative error of 4.3%.