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arXiv 2024年

作者： Leon, Florin Department of Computer Science and Engineering Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iași Romania

This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence. © 2024, CC BY-NC-SA.

关键词： Decision making

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Domain-specific data characteristics: A study on meaning of stylometric sub-concepts and in-class imbalance

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Procedia computer Science 2024年 246卷 4421-4430页

作者： Urszula Stańczyk Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

In the context of data imbalance probably the most investigated problem is imbalance of classes, as learning from the data with this characteristic makes detection of existing patterns for all classes more difficult. However, other problems related to imbalance also exists and the paper addresses such cases where classes are balanced, but there is in-class imbalance. Such imbalance can be caused by uneven representation of sub-concepts. When there is a noticeable difference between the numbers of samples belonging to sub-concepts, this can turn the under-represented sub-concepts into disjuncts. Data irregularities of this type can hinder recognition, therefore actions are typically taken to restore balance. In the investigations described, the issue was studied in the stylometric domain and various classifiers were applied to the data that was balanced, then imbalanced, and finally with restored balance. The experiments show that the specifics of the domain of application can put its own mark on the data which is difficult to overcome by standard processing such as under- or oversampling. Observed dependence on a learner and dataset makes the issue even more complex and layered, and shows the need for deeper studies.

关键词： Attribute domain Data irregularities Sub-concept Disjunct Pattern recognition Stylometry Authorship attribution

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Observations of data characteristics and irregularities through domain-oriented transformations of attributes

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Procedia computer Science 2024年 246卷 2694-2703页

作者： Urszula Stańczyk Grzegorz Baron Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The paper presents research dedicated to observations of relations between attribute properties and discretisation. In the investigations described, the gradually increasing sets of features were discretised by selected approaches, and several variants of data were constructed. The continuous, partially discrete, and completely translated datasets were explored by the chosen classifiers and their performance studied in the context of a number of discretised attributes, discretisation procedures, and the way of processing of features and datasets. The stylometric problem of authorship attribution was the machine learning task under study. The experimental results enable to observe closer the specificity of style-markers employed as characteristic features, and indicate conditions for efficient recognition of authorship. They can be extended to other application domains with similar characteristics.

关键词： Attribute domain Discretisation Data representation Data irregularities Pattern recognition Stylometry Authorship attribution

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Secure Synchronization of Heterogeneous Pulse-Coupled Oscillators

arXiv

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arXiv 2024年

作者： Yan, Jiaqi Ishii, Hideaki The Automatic Control Laboratory ETH Zurich Switzerland The Department of Computer Science Tokyo Institute of Technology Japan

In this paper, we consider the synchronization of heterogeneous pulse-coupled oscillators (PCOs), where some of the oscillators might be faulty or malicious. The oscillators interact through identical pulses at discrete instants and evolve continuously with different frequencies otherwise. Despite the presence of misbehaviors, benign oscillators aim to reach synchronization. To achieve this objective, two resilient synchronization protocols are developed in this paper by adapting the real-valued mean-subsequence reduced (MSR) algorithm to pulse-based interactions. The first protocol relies on packet-based communication to transmit absolute frequencies, while the second protocol operates purely with pulses to calculate relative frequencies. In both protocols, each normal oscillator periodically counts the received pulses to detect possible malicious behaviors. By disregarding suspicious pulses from its neighbors, the oscillator updates both its phases and frequencies. The paper establishes sufficient conditions on the initial states and graph structure under which resilient synchronization is achieved in the PCO network. Specifically, the normal oscillators can either detect the presence of malicious nodes or synchronize in both phases and frequencies. Additionally, a comparison between the two algorithms reveals a trade-off between relaxed initial conditions and reduced communication burden. © 2024, CC BY.

关键词： Synchronization

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RiscDaqExt – Digital Acquisition and control RISC-V Extension

RiscDaqExt – Digital Acquisition and Control RISC-V Extensi...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Cosmin-Andrei Popovici Andrei Stan Vasile-Ion Manta Department of Computer Engineering Faculty of Automatic Control and Computer Engineering Technical University "Gheorghe Asachi" Iași România

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

This paper presents a RISC-V extension, called RiscDaqExt designed for Digital Acquisition and control over 32 individually managed digital channels. Each channel can be configured as input or output, inputs can be negated or filtered before being recorded using specific masks and output channels source can be chosen between values given with a mask, result of a multi-signal operation on specified inputs or a PWM signal generated by the SigWavy extension. The extension is also capable of measuring PWMs frequencies and duty cycles. The extension is controlled using a dedicated ISA extension containing instructions for setting/getting configuration/values and for controlling the storing records of 32 signals values with 32-bit timestamps to an attached DDR3 memory or directly to hosting PC’s RAM via Ethernet. The proposed solution, embedded into the RISC-V CPU named RiscDaq manages to initiate an Ethernet transmission of digital records 9.78x times faster than a MicroBlaze microcontroller, although our SoC operates with a 4x lower frequency, and outputs the result of logical operation performed over maximum 31 digital channels, with a constant 20 ns propagation delay, 49.2x times faster than a Cortex-M7 clocked at a 6x times higher frequency.

关键词： Time-frequency analysis Digital systems Process control Random access memory Ethernet Pulse width modulation Frequency measurement Time factors Logic Propagation delay

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BigFeat: Scalable and Interpretable Automated Feature Engineering Framework

BigFeat: Scalable and Interpretable Automated Feature Engine...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Eldeeb, Hassan Amashukeli, Shota Elshawi, Radwa University of Tartu Institute of Computer Science Tartu Estonia Tanta University Faculty of Engineering Computers and Automatic Control Department Tanta Egypt

ISBN: (纸本)9781665480451

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features' quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat. © 2022 IEEE.

关键词： Scalability

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A MATHEMATICAL MODEL ILLUSTRATING THE INHIBITORY EFFECT OF THE MICRO RNA ON THE PROTEIN PRODUCTION

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2023年第4期85卷 101-106页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

The present paper introduces a mathematical model for the cross-talking between microRNA and Protein. Studying the qualitative properties of the proposed model, we infer that the microRNA is an inhibitor for the Prote... 详细信息

关键词： Proteins

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Rough inference system for automated failure prediction in cooling devices 1

Rough inference system for automated failure prediction in c...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Kokosinski, Zbigniew Szydlowski, Piotr Cracow University of Technology Department of Automatic Control and Computer Engineering Kraków31-155 Poland Efento Sp. Z O.o. Kraków31-123 Poland

ISBN: (纸本)9798350328042

This article presents a versatile model of failure prediction in control systems of objects with large time constants. The basic model of a control unit consists of deterministic finite automaton (DFA) which states correspond to states of the system where system variables satisfy a given set of constraints. System failures can be predicted on the basis of time series of the selected signals. Further diagnosis is performed on the basis of rough set theory and original algorithms for attributes' reduction and decision rule generation. A versatile failure prediction algorithm is constructed that integrates all involved techniques. Cooling systems are application examples of the proposed methodology. © 2023 IEEE.

关键词： Approximation algorithms

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Towards Empirical Transfer Function Estimation for Frequency Domain Closed-loop System Identification

Towards Empirical Transfer Function Estimation for Frequency...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Amirreza Zaman Dina Shona Laila Wolfgang Birk Department of Computer Science Electrical and Space Engineering Automatic Control Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This work introduces a method for closed-loop system identification using frequency analysis, employing Empirical Transfer Function Estimation (ETFE). By integrating optimization within a Monte Carlo framework, it enhances the precision of ETFE, resulting in minimized frequency response errors compared to actual system data. Leveraging controller information in an offline model fitting scheme, it achieves optimal realization of process dynamics. The method is evaluated on a data center rack-level cooling system, showing Bode magnitude plots of actual and estimated closed-loop and open-loop dynamics, with confidence intervals demonstrating algorithm consistency. Numerical evaluations confirm the feasibility and potential of the approach to improve offline closed-loop system identification performance in the frequency domain, beneficial for analysis and design. There will not be a comparative study for the introduced approach.

关键词： Monte Carlo methods Heuristic algorithms Fitting Transfer functions Estimation Process control Numerical models Closed loop systems Iterative methods Optimization

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Real-Time Multi-Object Tracking using YOLOv8 and SORT on a SoC FPGA

arXiv

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arXiv 2025年

作者： Danilowicz, Michal Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Krakow Poland

Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation on low-power and real-time embedded platforms is highly desirable. Modern MOT algorithms should be able to track objects of a given class (e.g. people or vehicles). In addition, the number of objects to be tracked is not known in advance, and they may appear and disappear at any time, as well as be obscured. For these reasons, the most popular and successful approaches have recently been based on the tracking paradigm. Therefore, the presence of a high quality object detector is essential, which in practice accounts for the vast majority of the computational and memory complexity of the whole MOT system. In this paper, we propose an FPGA (Field-Programmable Gate Array) implementation of an embedded MOT system based on a quantized YOLOv8 detector and the SORT (Simple Online Realtime Tracker) tracker. We use a modified version of the FINN framework to utilize external memory for model parameters and to support operations necessary required by YOLOv8. We discuss the evaluation of detection and tracking performance using the COCO and MOT15 datasets, where we achieve 0.21 mAP and 38.9 MOTA respectively. As the computational platform, we use an MPSoC system (Zynq UltraScale+ device from AMD/Xilinx) where the detector is deployed in reprogrammable logic and the tracking algorithm is implemented in the processor system. © 2025, CC BY.

关键词： System-on-chip

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