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Web based decision support system for management of defence activities: Process automation in Strategic Command - Decision Support System (PASC-DSS)

Web based decision support system for management of defence ...

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ieee international Conference on Communication Control and Computing Technologies (ICCCCT)

作者： Suman Patil Lalita Madanbhavi Electronics and Communication Department B. V. Bhoomaraddi College of Engineering and Technology Hubli Karnataka India Computer Science Department B. V. Bhoomaraddi College of Engineering and Technology Hubli Karnataka India

"Process automation in Strategic Command Decision Support System" (PASC DSS) is a program being executed by Center for Artificial intelligence and robotics (CAIR) to provide Indian Defence with a system built on leading edge technology. PASC DSS helps in collecting, collating, filtering, processing, synthesizing and displaying the information gathered from various sources and provide support while decision making. The system also helps to provide support for gathering information for various resources of Indian Defence and utilizing this information at the time of executing various operations.

关键词： Decision support systems automation Humans Decision making Spread spectrum communication computational modeling

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Attention-Guided Lightweight Network for Real-Time Segmentation of Robotic Surgical Instruments

Attention-Guided Lightweight Network for Real-Time Segmentat...

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ieee international Conference on robotics and automation (ICRA)

作者： Zhen-Liang Ni Gui-Bin Bian Zeng-Guang Hou Xiao-Hu Zhou Xiao-Liang Xie Zhen Li State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China school of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

The real-time segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, it is still a challenging task to implement deep learning models to do real-time segmentation for surgical instruments due to their high computational costs and slow inference speed. In this paper, we propose an attention-guided lightweight network (LWANet), which can segment surgical instruments in real-time. LWANet adopts encoder-decoder architecture, where the encoder is the lightweight network MobileNetV2, and the decoder consists of depthwise separable convolution, attention fusion block, and transposed convolution. Depthwise separable convolution is used as the basic unit to construct the decoder, which can reduce the model size and computational costs. Attention fusion block captures global contexts and encodes semantic dependencies between channels to emphasize target regions, contributing to locating the surgical instrument. Transposed convolution is performed to upsample feature maps for acquiring refined edges. LWANet can segment surgical instruments in real-time while takes little computational costs. Based on 960x544 inputs, its inference speed can reach 39 fps with only 3.39 GFLOPs. Also, it has a small model size and the number of parameters is only 2.06 M. The proposed network is evaluated on two datasets. It achieves state-of-the- art performance 94.10% mean IOU on Cata7 and obtains a new record on EndoVis 2017 with a 4.10% increase on mean IOU.

关键词： Instruments Convolution Semantics computational efficiency Decoding Surgery Real-time systems

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Reset-free guided policy search: Efficient deep reinforcement learning with stochastic initial states

Reset-free guided policy search: Efficient deep reinforcemen...

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ieee international Conference on robotics and automation (ICRA)

作者： William Montgomery Anurag Ajay Chelsea Finn Pieter Abbeel Sergey Levine Department of Computer Science and Engineering University of Washington Seattle WA Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA

Autonomous learning of robotic skills can allow general-purpose robots to learn wide behavioral repertoires without extensive manual engineering. However, robotic skill learning must typically make trade-offs to enable practical real-world learning, such as requiring manually designed policy or value function representations, initialization from human demonstrations, instrumentation of the training environment, or extremely long training times. We propose a new reinforcement learning algorithm that can train general-purpose neural network policies with minimal human engineering, while still allowing for fast, efficient learning in stochastic environments. We build on the guided policy search (GPS) algorithm, which transforms the reinforcement learning problem into supervised learning from a computational teacher (without human demonstrations). In contrast to prior GPS methods, which require a consistent set of initial states to which the system must be reset after each episode, our approach can handle random initial states, allowing it to be used even when deterministic resets are impossible. We compare our method to existing policy search algorithms in simulation, showing that it can train high-dimensional neural network policies with the same sample efficiency as prior GPS methods, and can learn policies directly from image pixels. We also present real-world robot results that show that our method can learn manipulation policies with visual features and random initial states.

关键词： Robots Global Positioning System Neural networks Learning (artificial intelligence) Supervised learning Training Optimization

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Novel Contradiction Resolution in Fixed Point Transformation-based Adaptive Control

Novel Contradiction Resolution in Fixed Point Transformation...

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international symposium on computational intelligence and Informatics

作者： Hamza Khan József K. Tar Doctoral School of Applied Informatics and textitApplied Mathematics Óbuda University Budapest Hungary Research and Innovation Center Antal Bejczy Center for Intelligent Robotics Óbuda University Budapest Hungary

In the control practice the problem of “underactu-ation” often arises: for controlling the state of a multivariable system the number of the independently applicable control signals is smaller than that of the components of the state variables. Then either certain state components can propagate in time without direct control while the others are precisely controlled, or some compromise has to be elaborated on the basis of which the control forces are “distributed” to keep each variable under partial and imprecise control. Normally, only imprecise and/or incomplete system models are available. Traditionally a compromise is achieved by the use of optimal controllers in which the often contradictory requirements are represented as nonnegative contributions to a cost function that is minimized under the constraints representing the available approximate dynamic model. The heuristic “Receding Horizon Controller” (RHC) uses “Nonlinear Programming” (NP): the optimum is found over a discrete grid by the use of Lagrange's Reduced Gradient Method. For dealing with imprecise modeling and unknown external disturbances the control forces are calculated for and are exerted only over a “short” horizon, then, after observing the actual state by measurements, a new horizon is designed, etc. The adaptive controllers, that normally are designed on the basis of Lyapunov's 2nd method, do not allow simple integration with the optimal controllers. The design of optimal and adaptive controllers is a challenge. To take up this challenge in Adaptive Control Lyapunov's 2nd method was replaced by a “Fixed Point Transformation” (FPT)-based approach. At first the control problem is transformed into finding the fixed point of a contractive map over a Banach Space, then it is solved via iteration. With this approach the contradiction resolution was tackled by time-sharing instead of minimizing a cost function. Alternatively, the minimization of a cost function was tackled by FPT simply by driving

关键词： Brain modeling Adaptation models computational modeling Task analysis Biological system modeling Lyapunov methods Asymptotic stability

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A theoretical mathematical modeling of Parkinson's disease using Fuzzy Cognitive Maps

A theoretical mathematical modeling of Parkinson's disease u...

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ieee symposium on Bioinformatics and Bioengineering (BIBE)

作者： Peter P. Groumpos Antigoni P. Anninou Laboratory for Automation and Robotics University of Patras Rio Greece

The mathematical model of medical problems is considered. The aim of this paper is to present a new approach in modeling the disease of Parkinson using Fuzzy Cognitive Maps (FCM). Basic theories of FCMs are reviewed and presented. Decision Support Systems (DSS) for Medical problems are considered. The disease of Parkinson is mathematically modeled using Fuzzy Cognitive Maps and three (3) experts. Linguistic variables are proposed and used to describe the correlation among concepts of the FCM. Simulations are performed and very interesting results are obtained and discussed.

关键词： Diseases Pragmatics Mathematical model Decision support systems Decision making computational modeling

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Integration of Heterogeneous computational Platform-Based, Ai-Capable Planetary Rover Using ROS 2

Integration of Heterogeneous Computational Platform-Based, A...

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ieee international symposium on Geoscience and Remote Sensing (IGARSS)

作者： Marek Kraft Krzysztof Walas Bartosz Ptak Michał Bidziński Krzysztof Stężała Dominik Pieczyński Institute of Robotics and Machine Intelligence Poznań University of Technology Poznań Poland

Space exploration has experienced a surge in interest and accessibility, with an increasing number of spacecraft launches. However, the scaling of space technology faces challenges as it heavily relies on human supervision and intervention. To overcome these limitations and enable greater autonomy, recent advancements in software and hardware, particularly in commercial off-the-shelf (COTS) components, have provided new opportunities. This paper introduces a prototype of a tightly-coupled hardware-software system that leverages a standard COTS computational platform and deep learning coprocessors to enable the efficient execution of deep learning workloads for space rovers. Integrated within the Robot Operating System 2 (ROS 2) framework, the system incorporates onboard sensors and offers rapid prototyping capabilities. By harnessing the benefits of COTS components and advanced software frameworks, this system represents a step towards achieving increased autonomy in space rovers, while also reducing development time. The presented system showcases the potential for future advancements in autonomous space exploration. The project documentation is publicly available: https://***/PUTvision/ros2_fpga_inference_node

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Consideration about the stability and performance of a minimum variance control system

Consideration about the stability and performance of a minim...

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international symposium on Applied computational intelligence and Informatics ( SACI)

作者： Ioan Filip Cristian Vasar Iosif Szeidert Octavian Prostean Department of Automation and Applied Informatics Politehnica University of Timisoara Timisoara Romania

ISBN: (数字)9781728173771

ISBN: (纸本)9781728173788

This paper presents a stability and performance analysis of a self-tuning minimum variance control system. Designed through a cost function minimization, the control law is described by a linear difference model with time varying parameters. Based on a linearized process model around an operating point and by using a parameter estimator, the control system automatically adapts itself when process parameters change (as effect of a disturbance). However, the performance of the control system is strongly conditioned by an a priori setting of a factor that weights the control variance term of the cost function. The goal of this stability analysis is to provide a strategy regarding how to tune this control penalty factor, which significantly influences the stability and performance of the control system. Two approaches were considered: one based on the control system response in relation to the disturbance, validated by a second one based on frequency response analysis.

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Preliminary Investigation on the Possible Adaptive Control of an Inverted Pendulum-type Electric Cart

Preliminary Investigation on the Possible Adaptive Control o...

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international symposium on computational intelligence and Informatics

作者： Hamza Khan Tamás Faitli Tamás Szili József K. Tar Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Donát Bánki Faculty of Mechanical and Safety Engineering Institute of Mechatronics and Autotechnics Óbuda University Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Budapest Hungary Research and Innovation Center Antal Bejczy Center for Intelligent Robotics Óbuda University Budapest Hungary

The model-based control of Classical Mechanical systems often has to cope with the problem of the lack of precise dynamic models. In the case of underactuated systems this problem is further burdened by the existence of strong nonlinearities, even in the form of dynamic singularities. The simultaneous existence of these problems does not promise successful application of the traditional robust control approaches that are based on a Linear Time-invariant (LTI) approximation of the model in the vicinity of some “working point” and the intensive use of the frequency domain. The relatively simple, strongly nonlinear Robust Variable Structure/Sliding Mode (VS/SM) controllers may produce undesirable chattering while the more sophisticated, Lyapunov function-based adaptive techniques may require complicated computations because the dynamic model of a relatively simple mechanical structure computationally may be quite complicated, especially when the system is driven by a Pulse Width Modulated (PWM), brushless electric DC motor that together with the mechanical components determines a relative order 3 control task yielding terms of order 0, 1, 2, and 3 time-derivatives of the state variables. The aim of the present paper is to investigate whether to what extent the calculation of these terms can be eliminated by the use of a simple, Fixed Point-iteration (FPI)-based adaptive controller that could be implemented by the use of an embedded micro-controller. The simulation results that belong to a certain solution are promising.

关键词： Adaptation models Task analysis Mathematical model Adaptive control computational modeling Informatics

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Comparison of different radio communication-based technologies for indoor localization using trilateration

Comparison of different radio communication-based technologi...

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international symposium on Applied computational intelligence and Informatics ( SACI)

作者： Dominik Csík Peter Sarcevic Richard Pesti Ákos Odry Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Department of Mechatronics and Automation University of Szeged Szeged Hungary

With the spread of Internet of Things (IoT) different radio communication-based technologies are gaining more attention, and their application in indoor localization problems where GPS signal is not available, becomes more and more relevant. This paper addresses the trilateration algorithm-based position estimation approach in indoor localization; this approach uses Received Signal Strength Indication (RSSI) value with the Free Space Path Loss (FSPL) model. Comprehensive laboratory measurements were performed with different radio communication-based technologies namely 433 MHz RSSI, 2.4 GHz WiFi RSSI, ultra-wideband (UWB) RSSI, and UWB Time of Flight (TOF). Then, numerical optimization using the Particle Swarm optimization (PSO) algorithm to determine the parameters of the FSPL model for each anchor and each technology. This method enabled the utilization of trilateration to calculate the position of the measurement node. The obtained results show that the 433 MHz RSSI and the UWB TOF outperform the other two technologies. Using UWB TOF technology, the achieved accuracy was 165.97 cm. 433 MHz RSSI technology provided the second-best solution with 166.60 cm. WiFi RSSI provided 227.89 cm accuracy, while the worst case was obtained by UWB RSSI with 252.08 cm. The study experimentally validates that the most appropriate measurements are provided by UWB TOF and 433 MHz RSSI, which enables the implementation of these technologies in sensor fusion algorithms.

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Real time implementation of a selective attention model for the intelligent robot with autonomous mental development

Real time implementation of a selective attention model for ...

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ieee international symposium on Industrial Electronics (ISIE)

作者： Woong-Jae Won Sang-Woo Ban Minho Lee School of Electronic and Electrical Engineering Kyungpook National University Taegu South Korea

We propose a biologically motivated selective attention model to find an object based on context free search for an intelligent robot with an autonomous mental development (AMD) mechanism. For real-time operation of the selective attention model in the robot system, we have considered a way to reduce the computational load of the selective attention model, which uses a simplified symmetry operation with retina-topic sampling and look-up table in the localized candidate attention region. As a result, our model can perform within 270 ms at Pentium-4 2.8Ghz, and obtain a plausible human-like visual scan path in order to pay attention to an object preferentially. Then, we implemented an intelligent mobile robot with selective attention for an AMD mechanism.

关键词： Intelligent robots Autonomous mental development Context modeling Brain modeling Biological system modeling Medical robotics Artificial intelligence Humanoid robots Humans Intelligent sensors

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