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23rd Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB 2009

作者： Kupferberg, Aleksandra Glasauer, Stefan Huber, Markus Rickert, Markus Knoll, Alois Brandt, Thomas Center for Sensorimotor Research Clinical Neurosciences Ludwig-Maximilians-Univ. Munich Germany Robotics and Embedded Systems Lab. Dept. of Computer Science Technical Univ. München Germany Department of Clinical Neurosciences Ludwig-Maximilians-Univ. München Germany

ISBN: (纸本)1902956850

Anthropomorphic "humanoid" robots are suggested to be more competent in social communicative interactions than industrial robots, because humans interact more intuitively with them. It is, therefore, critical to evaluate the acceptance of an agent as possible partner for joint interaction. One possible method is to utilize the phenomenon of motor interference (MI). It claims, that observation of an incongruent movement of another individual leads to a higher variance in one's own movement trajectory. Although this effect has been demonstrated while observing a human agent, the researchers were unable to show increased variance in the subjects' movements if they observed an arm of an industrial robot moving with piecewise-constant velocity. In contrast, in other recent studies, MI was demonstrated when subjects watched a humanoid robot performing biological movements based on prerecording of a human experimenter, even if it was not the case when the same robot moved with constant (artificial) velocity. The purpose of the present study was two-fold: 1) we aimed to replicate these results using video-presentations of the agents, and 2) we asked whether quasi-biological movement trajectories are sufficient to elicit MI. We presented subjects, who were instructed to perform horizontal and vertical arm movements, with videos of a human agent or of a humanoid robot, who performed congruent or incongruent arm movements. Robotic movements were produced with a quasi-biological minimum-jerk velocity profile. We found MI both for the human agent and the robot, suggesting that an artificial human-like movement velocity profile (minimum-jerk) is sufficient to facilitate perception of humanoid robots as interaction partners, and that the measurement of MI using a face-to-face video setup can serve as a tool for objectively evaluating humanoid robots.

关键词： Anthropomorphic robots

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Adaptive admission control of multimedia traffic in high-speed networks

Adaptive admission control of multimedia traffic in high-spe...

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IEEE International Symposium on Intelligent Control (ISIC)

作者： S. Jagannathan A. Tohmaz A. Chronopoulos H.G. Cheung Embedded Systems and Networking Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA Parallel and Distributed Systems Lab Department of Computer Science University of Texas-San Antonio TX USA

This paper proposes a novel real-time adaptive admission control (AAC) scheme with a desired quality of service (QoS) guarantee and high network utilization in high speed networks. The QoS is given in terms of service delay, which is defined as the time it takes for a source to get admitted into the network after it initiates its intended request, packet/cell losses, and transmission delay (time taken to complete transmission from its initiation). AAC uses the following information - the available capacity from a novel adaptive bandwidth estimation scheme, a congestion indicator derived from a congestion controller, peak cell rate estimate from new sources, along with the desired QoS metrics, and outputs an 'admit' or 'reject' decision signal to the new sources while guaranteeing QoS and network utilization. Simulation results are presented by streaming ON/OFF and video data into the network. Results show that the proposed AAC admits significantly more traffic compared to other available admission control schemes thereby guaranteeing high network utilization while maintaining the desired QoS.

关键词： Programmable control Adaptive control Admission control Communication system traffic control High-speed networks Quality of service Delay effects Propagation losses Bandwidth Streaming media

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PID2018 Benchmark Challenge: Model-based Feedforward Compensator with A Conditional Integrator

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IFAC-PapersOnLine 2018年第4期51卷 888-893页

作者： Yuan, Jie Ates, Abdullah Dehghan, Sina Zhao, Yang Fei, Shumin Chen, YangQuan School of Automation Southeast University Nanjing210096 China Engineering Faculty Computer Engineering Department Inonu University Malatya44280 Turkey Mechatronics Embedded Systems and Automation Lab University of California MercedCA95340 United States School of Science and Engineering Shandong University Jinan250061 China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China

Since proportional-integral-derivative (PID) controllers absolutely dominate the control engineering, numbers of different control structures and theories have been developed to enhance the efficiency of PID controllers. Thus, it is essential and inspiring to operate different PID control strategies to the PID2018 Benchmark Challenge. In this paper, a novel control strategy is designed for this refrigeration system, where a feedforward compensator and a conditional integrator are utilized to compensate the disturbances and remove the steady-state error in the benchmark problem, respectively. The simulation results given in the benchmark problem show the straightforward effectiveness of the proposed control structure compared with the existing control methods. © 2018

关键词： Controllers Benchmarking Disturbance rejection Electric control equipment Proportional control systems Refrigeration Three term control systems Two term control systems Conditional integrators Feed forward compensation Model based method PID controllers Refrigeration system

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ReLU and Addition-based Gated RNN

arXiv

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arXiv 2023年

作者： Brännvall, Rickard Forsgren, Henrik Sandin, Fredrik Liwicki, Marcus Department of Computer Science RISE Research Institutes of Sweden Luleå Sweden Embedded Intelligent Systems LAB Luleå University of Technology Luleå Sweden

We replace the multiplication and sigmoid function of the conventional recurrent gate with addition and ReLU activation. This mechanism is designed to maintain long-term memory for sequence processing but at a reduced computational cost, thereby opening up for more efficient execution or larger models on restricted hardware. Recurrent Neural Networks (RNNs) with gating mechanisms such as LSTM and GRU have been widely successful in learning from sequential data due to their ability to capture long-term dependencies. Conventionally, the update based on current inputs and the previous state history is each multiplied with dynamic weights and combined to compute the next state. However, multiplication can be computationally expensive, especially for certain hardware architectures or alternative arithmetic systems such as homomorphic encryption. It is demonstrated that the novel gating mechanism can capture long-term dependencies for a standard synthetic sequence learning task while significantly reducing computational costs such that execution time is reduced by half on CPU and by one-third under encryption. Experimental results on handwritten text recognition tasks furthermore show that the proposed architecture can be trained to achieve comparable accuracy to conventional GRU and LSTM baselines. The gating mechanism introduced in this paper may enable privacy-preserving AI applications operating under homomorphic encryption by avoiding the multiplication of encrypted variables. It can also support quantization in (unencrypted) plaintext applications, with the potential for substantial performance gains since the addition-based formulation can avoid the expansion to double precision often required for multiplication. © 2023, CC BY.

关键词： Cryptography

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All for one or one for all? Combining heterogeneous features for activity spotting

All for one or one for all? Combining heterogeneous features...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Ulf Blanke Bernt Schiele Matthias Kreil Paul Lukowicz Bernhard Sick Thiemo Gruber Computer Science Department Technical University of Darmstadt Germany Embedded Systems Laboratory (ESL) University of Passau Germany Computationally Intelligent Systems Lab (CIS) University of Passau Germany

Choosing the right feature for motion based activity spotting is not a trivial task. Often, features derived by intuition or that proved to work well in previous work are used. While feature selection algorithms allow automatic decision, definition of features remains a manual task. We conduct a comparative study of features with very different origin. To this end, we propose a new type of features based on polynomial approximation of signals. The new feature type is compared to features used routinely for motion based activity recognition as well as to recently proposed body-model based features. Experiments were performed on three different, large datasets allowing a thorough, in-depth analysis. They not only show the respective strengths of the different feature types but also their complementarity resulting in improved performance through combination. It shows that each feature type with its individual and complementary strengths and weaknesses can improve results by combination.

关键词： Wearable sensors Polynomials Performance analysis Frequency Boosting computer science embedded system embedded computing Intelligent systems Computational intelligence

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Catalyzing EEG signal analysis: unveiling the potential of machine learning-enabled smart K nearest neighbor outlier detection

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International Journal of Information Technology (Singapore) 2024年 1-12页

作者： Aymen, Abid Khediri, Salim El Thaljaoui, Adel Miladi, Moahmed Kachouri, Abdennaceur Computer & amp Embedded Systems Lab National Engineering School of Sfax University of Sfax Sfax Tunisia Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science Faculty of Science of Gafsa University of Gafsa Gafsa Tunisia Preparatory Institute for Engineering Studies of Gafsa Gafsa Tunisia Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Sfax Tunisia Advanced Fluid Dynamics Energetics and Environment Lab National Engineering School of Sfax University of Sfax Sfax Tunisia

Electroencephalogram (EEG) data are susceptible to artifacts, such as lapses in concentration or poor imagination, which can significantly impact the accuracy of disease diagnosis in e-health applications. To mitigate this issue, the use of machine learning (ML) and potentially artificial intelligence (AI) solutions to accurately identify outliers becomes crucial. Unlike many AI methods that incorporate unnecessary or redundant input variables, our study focuses on detecting anomalous values in EEG data through the K nearest neighbor (KNN) process and Euclidean distance metric. Our proposed unsupervised non-parametric algorithm, known as the smart KNN outlier detector (SKOD), eliminates the need for initial parameter configurations such as the number of neighbors (K), while achieving high performance. Evaluation of SKOD using real EEG data from 140 trials demonstrated sensitivity and specificity exceeding 60%, with nearly perfect accuracy in detecting outliers reaching close to 100%. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Data analysis EEG signal Healthcare Knowledge-based systems Machine learning Outlier detection

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Regional detection of unknown sources for the sub-diffusion process

Regional detection of unknown sources for the sub-diffusion ...

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American Control Conference

作者： Fudong Ge Yang Quan Chen Chunhai Kou School of Computer Science China University of Geosciences Mechatronics Embedded Systems and Automation Lab University of California Merced Department of Applied Mathematics Donghua University

ISBN: (纸本)9781509045839

In this paper, we shall discuss the problem of regional detection of unknown source in a sub-diffusion process. The notions of sources, detection and regional strategic/spy sensors are introduced and the relationships between regional spy sensors and regional strategic sensors in a sub-diffusion process are explored. Moreover, we show, using an extension of the Hilbert Uniqueness Methods approach, how to reconstruct a regionally detectable source for the time fractional diffusion system based on the output functions. An example is given in the end to confirm our results.

关键词： Regional detection Sources Sensors Sub-diffusion process Time fractional diffusion systems Sensors Diffusion processes Regional Unknown

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Context-awareness for service oriented systems

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International Journal of computer Science Issues 2012年第5 5-2期9卷 95-104页

作者： Hafiddi, Hatim Baidouri, Hicham Nassar, Mahmoud Kriouile, Abdelaziz Mobile and Embedded Information Systems Lab. Models and Systems Engineering Team National Higher School for Computer Science and Systems Analysis Mohammed V Souissi University BP 713 Agdal Rabat Morocco

Today, service oriented systems need to be enhanced to sense and react to user's context in order to provide a better user experience. To meet this requirement, Context-Aware Services (CAS) have emerged as an underling design and development paradigm for the development of context-aware systems. The fundamental challenges for such systems development are context-awareness management and service adaptation to the user's context. To cope with such requirements, we propose a well designed architecture, named ACAS, to support the development of Context-Aware Service Oriented systems (CASOS). This architecture relies on a set of context-awareness and CAS specifications and metamodels to enhance a core service, in service oriented systems, to be context-aware. This enhancement is fulfilled by the Aspect Adaptations Weaver (A2W) which, based on the Aspect Paradigm (AP) concepts, considers the service's adaptations as aspects. © 2012 International Journal of computer Science Issues.

关键词： Information services

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TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning 31

TS-MoCo: Time-Series Momentum Contrast for Self-Supervised P...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Hallgarten, Philipp Bethge, David Özdenizci, Ozan Grosse-Puppendahl, Tobias Kasneci, Enkelejda Dr. Ing. h.c. F. Porsche AG Stuttgart Germany TU Munich Germany LMU Munich Germany Institute of Theoretical Computer Science TU Graz Austria TU Graz - SAL Dependable Embedded Systems Lab Silicon Austria Labs Graz Austria

ISBN: (纸本)9789464593600

Limited availability of labeled physiological data often prohibits the use of powerful supervised deep learning models in the biomedical machine intelligence domain. We approach this problem and propose a novel encoding framework that relies on self-supervised learning with momentum contrast to learn representations from multivariate time-series of various physiological domains without needing labels. Our model uses a transformer architecture that can be easily adapted to classification problems by optimizing a linear output classification layer. We experimentally evaluate our framework using two publicly available physiological datasets from different domains, i.e., human activity recognition from embedded inertial sensory and emotion recognition from electroencephalography. We show that our self-supervised learning approach can indeed learn discriminative features which can be exploited in downstream classification tasks. Our work enables the development of domain-agnostic intelligent systems that can effectively analyze multivariate time-series data from physiological domains. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Emotion Recognition

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Motion planner augmented reinforcement learning for robot manipulation in obstructed environments

arXiv

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arXiv 2020年

作者： Yamada, Jun Lee, Youngwoon Salhotra, Gautam Pertsch, Karl Pflueger, Max Sukhatme, Gaurav S. Lim, Joseph J. Englert, Peter Cognitive Learning for Vision and Robotics Lab United States Robotic Embedded Systems Laboratory Department of Computer Science University of Southern California United States

Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in environments with many obstacles that complicate exploration. In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment. To combine the benefits of both approaches, we propose motion planner augmented RL (MoPA-RL) which augments the action space of an RL agent with the long-horizon planning capabilities of motion planners. Based on the magnitude of the action, our approach smoothly transitions between directly executing the action and invoking a motion planner. We evaluate our approach on various simulated manipulation tasks and compare it to alternative action spaces in terms of learning efficiency and safety. The experiments demonstrate that MoPA-RL increases learning efficiency, leads to a faster exploration, and results in safer policies that avoid collisions with the environment. Videos and code are available at https://***/mopa-rl. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion planning

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