Management of Knowledge Imperfection in Building Intelligent Systems

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Several studies have shown that the combination of different brain signal acquisition methods can lead to higher performance.

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While these studies provided proof-of-concept of this approach as a highly promising avenue in the field of HRI, a systematic understanding of the dyadic interacting system human and machine remained unexplored. This research aims to address this gap by proposing a computational model of the human counterpart and simulating the integrated dyadic system.

The model can be employed for the systematic study of both human behavioral and technical factors influencing co-adaptation as exemplarily demonstrated in this paper for hypothetical variations of ErrP-decoder performance. The obtained findings have practical implications for future steps along this line of research, for instance to what extent and how improvements of ErrP-decoder performance can benefit co-adaptation in ErrP-based HRI. The proposed computational model enables the prediction of human behavior in the context of ErrP-based HRI.

As such it allows the simulation of future empirical studies prior to their conductance and thereby providing a means for accelerating progress along this line of research in a resource-saving manner. Groups typically have increased sensing and cognition capabilities that allow them to make better decisions than individuals. In the last decade, researchers have started evaluating the possibilities to exploit, by means of collaborative BCI, the neurophysiological signals of a group of observers to improve the decision-making process.

Following this stream of research, in this work we build a collaborative BCI and focus on the role of each subject in the group, trying to answer the question whether there are some subjects that would be better to remove or that are fundamental to enhance group performance.

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Management of Knowledge Imperfection in Building Intelligent Systems (Studies in Fuzziness and Soft Computing) [Eugene Roventa, Tiberiu Spircu] on. This book is among the few books to be entirely dedicated to the treatment of. Management of Knowledge Imperfection in Building Intelligent Systems. Authors .

Integrating brain-machine interface BMI neurotechnology with vehicle control systems VCS provides a novel solution to this problem. In this proof-of-concept study, we show that an intracortical BMI developed to restore voluntary grasp can be repurposed to decode motor intention for vehicle velocity and steering control.

Machine Learning in Games of Perfect Information

Methods: The BMI-VCS consists of four components: 1 implanted motor cortex microelectrode array and NeuroPort data acquisition system, 2 machine learning workstation, 3 Python interface to generate control signals, and 4 vehicle control system. These decoder outputs could also be used offline for shared control of a scale model car. Significance: High-level, shared vehicle control with BMI-VCS offers an innovative way to return independent driving abilities to those with disability. BMI systems that can control multiple end-effectors may be particularly useful to those with paralysis.

However, most intrusion detection, anomaly detection, monitoring, and mining algorithms and frameworks assume that data provided is of perfect quality. Therefore, these algorithms tend to perform extremely well in controlled lab environments but fail in the real-world.

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We propose a method for accurately restoring discrete temporal or sequential system traces affected by data loss, using Word2vec's Continuous Bag of Words CBOW model. The model works by learning to predict the next event in a sequence of events, the model feeds its output back into it for subsequent future predictions.

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Such a method can reconstruct even long sequence of missing events, and help validate and improve data quality for noisy data. The restored traces are very close to the real-data and can be used by algorithms depending on real-data for system analysis. We demonstrate our method by reconstructing traces from QNX real-time operating system consisting of long sequences of discrete events. In this paper, we study the joint decision problem on vegetable supplier selection and facility location for Winter Olympics held in Beijing considering supply risk.

We model this problem as a two-stage stochastic mixed-integer programming. Then we collect extensively data regarding to the game and estimate the parameters, based on which we conduct extensive numerical experiments.

Introduction: Paola Antonelli

For the Winter Olympics, we should select multiple centres. These behaviors have brought negative influence on subway operation and management. The paper will analyze the abnormal data of "In-out" in the same subway station IOSSS to explore the abnormal behavior of subway riders with Beijing' riding data. IOSSS refers to passengers' entering and leaving at the same subway station. The study collected half one year' passenger data, analyzed the characteristics of IOSSS, and carried out passenger cluster analysis and portrait analysis. The research can deepen the understanding of IOSSS, analyzed the purpose and characteristics of the abnormal riders, which is conducive to the safety of subway passengers.

In this model, delivery vehicles and UAVs serve customers, respectively, and trucks serve as a mobile warehouse and a landing platform for UAVs. In this paper, a mathematical model is established to minimize the task completion time of the system, and a two-stage heuristic algorithm is designed to solve the model. Finally, numerical experiments are carried out with the data of Beijing.

Through big data mining technology, we collected GPS location of express service points, residential areas, grocery stores and transportation stations in Beijing. Then, using Multiple linear regression, SVM and random forest machine learning model the quantitative relationship between the number of express service points and other data in urban area, as well as the approach of model stacking get an integration machine learning model.

The controller parameters are tuned by using a particle swarm optimization PSO algorithm.

Management of Knowledge Imperfection in Building Intelligent Systems

Its performance is compared with an integer order proportional-derivative IOPD controller. This provides an optimal global path by considering the physical system dimension and constraints of acceleration and velocity of the UAV. The simulation tests using the virtual environment demonstrate that the proposed approach outperforms the IOPD controller.

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A low cost setup is described along with a proposed methodology protocol. The paper introduces the problem of moving from a linear framework of fluid properties towards a nonlinear one and motivates the choice for lumped nonlinear parameter model structures. It follows identification using nonlinear least squares in various liquids. The results obtained suggest that parameters of the proposed fractional order impedance model are susceptible to changes in density as being one important feature of non-Newtonian fluids.

Further use of these findings across disciplines is given in the conclusion section of the paper. To this aim forced oscillations technique FOT has been used to non-invasively measure the lung tissue mechanics. FOT does not require any special effort from the patient in contrast with standardized tests where maneuvers are necessary. Hence, FOT is an ideal lung function test for extreme ages, more specifically children and elderly, given the simpleness of measurement technique.

Automation doesn’t replace process

Hitherto, measurements at low frequencies i. Here we measure in the frequency interval 0. To evaluate the non-linear contributions of the respiratory tissue, a novel T-index has been introduced. The results obtained indicate that the proposed index can differentiate between the two analyzed groups and that there is a dependence to age, height and weight.

A medical information system may use this information to update predictions of respiratory function and provide aid in decision-making process of drug therapy. Ultra-fast insulins are reputed to allow better tailoring of blood-glucose reduction than currently available fast insulins, particularly during and after meals. In this work, we propose a strategy for the control of glycemia in the presence of perturbations meals , using injectable insulin preparations with fast and very fast absorption kinetics, particularly in the context of MDI diabetes therapy.

The control method is validated in-silico, also with a comparison between the use of fast and ultra-fast insulin, while robustness is tested with respect to non-idealities including meal glucose content uncertainties, and quantized glycemia measurements and insulin doses. Preliminary results show the effectiveness of the approach taken and are promising in view of the application of the proposed techniques in more realistic experimental conditions. However, to successfully implement needle insertion, surgeons require prolonged training processes and long-term experience to develop essential handling skills.

This paper presents a new path planning approach with Deep Reinforcement Learning DRL to implement automatic needle insertion using a surgical robot. In this paper, Deep Q-Network DQN algorithm is utilized to learn the control policy for flexible needle steering with needle-tissue interaction.

As the human body is composed of a complex environment such as tissues, blood vessels, bones, and muscles, the uncertainty of the needle-tissue interaction should be considered during insertion. To model this complex interaction in path planning, utilizing a neural network to approximate the action-value function is more efficient than using traditional array methods in terms of time and accuracy.

In our simulation, the agent needle can be controlled with 2 degrees of freedom bevel direction rotation and insertion and received negative rewards when it collides with obstacles, goes out of range, or exceeds a predefined number of rotations. During the training, the agent demonstrates the accuracy and efficiency of the learned policy through feedback scores in every episode.

In addition, this system incorporates the uncertainty within flexible needle-tissue interaction using a stochastic environment.

AI, Games, and the Future of Intelligence

Compared with other traditional methods for flexible needle path planning, we demonstrated that motion planning of bevel-tip flexible needles in complex human bodies using DRL has better efficiency and accuracy. To apply this procedure, a suitable identification method for the patient model parameters, based on the model structure, is used.

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The topics include distributed system architectures, distributed programming, message passing, remote procedure calls, group communication, naming and membership problems, logical time, consistency, fault-tolerance, and recovery. In this paper, we propose to assist the physician with a haptic device that holds the needle and generates mechanical guides during the phase of manual needle pre-positioning. Finally, numerical experiments are carried out with the data of Beijing. Due to the simplicity of the structure and calculation of the internal model control based on the inverted decoupling ID-IMC , an improved Butterworth filter is introduced into the ID-IMC system design, so that the performance of the closed-loop control system is improved. However, for vision system it is difficult to continuously collect the image information due to occlusion, feature matching failure, etc. Today, teams of researchers are working on—or have already succeeded in—creating AIs that can defeat humans in increasingly complex games. Business Administration.

Nevertheless, the adoption of such technology in real home environments is still far to be reached since it presents several challenges mainly related to its acceptance in the everyday life. In this domain, a high degree of personalization is required usually obtained by a customization of a robotic system with respect to specific assistive tasks. On the contrary, socially assistive robots are usually general-purpose platforms requiring a considerable effort for customization.

In this work, to limit static and costly customization of robotic systems, a service-oriented approach is adopted to represent and manage assistive tasks to be performed by a social robotic system, allowing to decouple a given functionality from its concrete implementation that can be provided by different devices and with different execution modalities.

The service-oriented approach for robotics applications, known as Robot-as-a-Service, is becoming attractive for decoupling the robot hardware from the functionalities it provides.

Among the information required for the interaction with a human, the capability of analysing the emotion is surely among the most important ones. Another relevant feature of social robots is the possibility to interact with the human in real time, without any latency that could introduce a delay in the talk between the human and the robot.

It means that all the processing of the information for instance the analysis of the sequence of images for the detection of the persons and the consequent emotion analysis needs to be performed directly on board of the robot, without any possibility to use high performance servers for instance services on the cloud but only small devices that can be installed directly on board of the robotic platform.

In this paper we propose MIVIAbot, a robotic platform based on the Pepper humanoid robot and equipped with a small, low cost and low energy consumption embedded device installed directly on board of Pepper, without any requirements for a Wi-Fi connection that could introduce any latency for the transmission of the data. Furthermore, we also propose a method for the analysis of the emotion of the persons by face analysis, able to run on the considered hardware platform in real time without paying in terms of accuracy.

The experimentation conducted over various widely adopted datasets of images and videos for emotion analysis confirms both the efficiency and the effectiveness of the proposed approach. Quick to Complain? Would an architecture that learns very quickly outperform a slower but steadier learning profile? To further explore this, we propose a cognitive architecture for the humanoid robot iCub supporting adaptability and we attempt to validate its functionality and test different robot profiles.

This study proposes an architecture that adds extended representation of sentiment information to questions and answers, and reports on to what extent a prediction of the best answer be improved by the proposed architecture. The change in sensing volume coverage by increasing the number of sensor per ToF sensor array ring and also increasing the number of rings mounted on robot link is also studied.