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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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Learning to Count with Cell Assemblies: A Neuro-Symbolic Approach

Learning to Count with Cell Assemblies: A Neuro-Symbolic App...

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

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

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Recent advancements in artificial intelligence have led to significant results in various domains, including image classification, natural language processing, and mastering complex games. However, current deep neural networks seem to process information differently from humans. Neuro-symbolic methods may offer a promising solution to address this concern. This paper proposes a preliminary cognitive architecture focused on neural cell assemblies, which can combine the adaptability of neural approaches with the explicit reasoning capabilities of symbolic systems. It presents a case study on learning to count, and highlights mechanisms for learning, generalization, and adaptation based on predictive errors.

关键词： Training Neuroscience Microprocessors Transfer learning Symbols Psychology computer architecture Biology Planning Assembly

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Predicting the Time from Diagnosis to Treatment in Breast Cancer

Predicting the Time from Diagnosis to Treatment in Breast Ca...

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E-Health and Bioengineering Conference (EHB)

作者： Sergiu Ceică Ştefan-Daniel Achirei Dept. of Computer Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iasi Iasi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Recent advancements in machine learning (ML) have significantly impacted the medical field, particularly in diagnosing and treating breast cancer. This study models the time from diagnosis to treatment for breast cancer patients, focusing on delay factors and ML-based solutions. The goal is to develop precise ML models that predict treatment delays, thereby improving the quality and efficiency of medical services. We investigated socio-economic disparities affecting treatment access and developed models to predict these delays. The predictive models used include K-Nearest Neighbors, Decision Trees, Linear Regression, and Boosting Algorithms such as AdaBoost and Gradient Boosting Regressor. By accurately predicting the interval from diagnosis to the first treatment, our work aims to promote equity in healthcare, ensuring timely treatment for all patients. Our findings highlight the potential of ML in optimizing treatment timelines and resource allocation, contributing to improved patient outcomes and advancing the medical system.

关键词： Medical services Predictive models Boosting Prediction algorithms Breast cancer Delays Resource management Socioeconomics Medical diagnostic imaging Regression tree analysis

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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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Analog, Programmable Switched Capacitor FIR Filter Based on Rotator Architecture Implemented in CMOS Technology 26

Analog, Programmable Switched Capacitor FIR Filter Based on ...

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26th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Pawlowski, Pawel Dlugosz, Rafal Radkowski, Marek Watly, Mateusz Dabrowski, Adam Poznań University of Technology Division of Signal Processing and Electronic Systems Faculty of Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Poznań60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350304985

The paper presents project and its verification of a prototype integrated circuit containing an analog, programmable finite impulse response (FIR) filter, implemented in CMOS 350 nm technology. The structure of the filter is based on the switched capacitor technique. In circuits of this type, one of main challenges is an efficient implementation of filter coefficients, which result from several factors described in this work. When implementing such filters as programmable circuits, the values of their coefficients have to be limited to a selected range, i.e. a given resolution in bits. In the implemented prototype filter, the filter coefficients are represented by 6 bits in sign-magnitude notation, so they can take 63 different values only. In such filters, it is not possible to directly implement any frequency response of the filter. Each time, it is necessary to properly round the theoretical values of the coefficients so that they fit into the available range of discrete values resulting from the implementation. The authors of the work designed an algorithm that allows such matching. The paper also presents results of measurements of the prototype chip. © 2023 Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT).

关键词： FIR filters

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Integration of Industrial Automation and IoT Devices on the Example of the Assembly Station with the Astorino Educational Robot 4th

Integration of Industrial Automation and IoT Devices on the ...

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4th International Conference on Intelligent Systems in Production engineering and Maintenance, ISPEM 2023

作者： Ćwikla, Grzegorz Kalinowski, Krzysztof Kciuk, Marek Gold, Michal Waluśkiewicz, Marek Sladek, Michal Tlolka, Jaroslaw Woźnica, Robert Wlazlo, Aleksander Faculty of Mechanical Engineering Silesian University of Technology Konarskiego 18A Gliwice44-100 Poland Faculty of Electrical Engineering Silesian University of Technology Akademicka 10A Gliwice44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9783031442810

The article presents an automated assembly station designed by students as part of Project Based Learning. The task of the project was to familiarize with the issues of integration of automation and control elements operating in various standards in order to implement the idea of Industry 4.0. The main elements of the station are the Astorino educational robot, gripper, servo drive and other professional elements of industrial automation and IoT systems. Raspberry PI, microcontrollers, etc. are also used. The assembly process of the product in the configuration in accordance with the user’s order is carried out at the station, quality control is carried out during the process using a vision system and various sensors. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data acquisition

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Pneumonia Detection from X-ray Images Using Convolutional Neural Networks

Pneumonia Detection from X-ray Images Using Convolutional Ne...

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International Conference on control Systems and computer Science (CSCS)

作者： Viorel Dediu Andreea Udrea Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science Univesity Politehnica of Bucharest Bucharest Romania

Pneumonia is one of the top causes of death in Romania and early detection of this disease improves the recovery chances and shortens the length of hospitalization. In this work, we develop a solution for automatic pneumonia detection based on convolutional neural networks. Four network models are investigated. They are trained on 4.163 images from a public dataset and tested on 530 images. The best results are obtained by one of the proposed models conducting to a sensitivity of 98.72%, an accuracy of 89.81%, and ROC 93.46%. Thus, this research proposes a lightweight screening tool that can help triaging the patients with pneumonia.

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Hierarchical Classification of Transversal Skills in Job Ads Based on Sentence Embeddings

arXiv

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arXiv 2024年

作者： Leon, Florin Gavrilescu, Marius Floria, Sabina-Adriana Minea, Alina-Adriana Faculty of Automatic Control and Computer Engineering Romania Faculty of Materials Science and Engineering "Gheorghe Asachi" Technical University of Iași Romania

This paper proposes a classification framework aimed at identifying correlations between job ad requirements and transversal skill sets, with a focus on predicting the necessary skills for individual job descriptions using a deep learning model. The approach involves data collection, preprocessing, and labeling using ESCO (European Skills, Competences, and Occupations) taxonomy. Hierarchical classification and multi-label strategies are used for skill identification, while augmentation techniques address data imbalance, enhancing model robustness. A comparison between results obtained with English-specific and multi-language sentence embedding models reveals close accuracy. The experimental case studies detail neural network configurations, hyperparameters, and cross-validation results, highlighting the efficacy of the hierarchical approach and the suitability of the multi-language model for the diverse European job market. Thus, a new approach is proposed for the hierarchical classification of transversal skills from job ads. © 2024, CC BY-NC-ND.

关键词： Embeddings

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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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Towards backward compatibility of ADRC: revisiting classical state-feedback control with integral compensator

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Bulletin of the Polish Academy of Sciences: Technical Sciences 2025年第2期73卷

作者： Mrotek, Mikolaj Michalski, Jacek Madonski, Rafal Pazderski, Dariusz Retinger, Marek Faculty of Control Robotics and Electrical Engineering Institute of Robotics and Machine Intelligence Poznan University of Technology Piotrowo 3a Poznan60-965 Poland Faculty of Automatic Control Electronics and Computer Science Department of Automatic Control and Robotics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Poznan University of Technology Piotrowo 3a Poznan60-965 Poland

In this paper, the problem of backward compatibility of active disturbance rejection control (ADRC) is investigated. The goal is to contextualize ADRC to deliver its interpretations from the established field of linear control systems. For this study, a control algorithm, denoted here as integral disturbance rejection control (IDRC), is considered that combines classical state-feedback control with an integral compensator. At first, an interpretation of ADRC is involved in terms of existing state-space control approaches. Next, a transition to the frequency domain is performed, which is justified as a significant part of practical control engineering is conducted in that domain. For assumed specific plant structures, both ADRC and IDRC are then holistically compared in terms of transfer function representation and frequency characteristics, as well as steady-state convergence conditions. Such a juxtaposition helps to highlight the similarities and differences of both approaches, whereas the utilized bandwidth parameterization is shown to bring the control system to the same form, thus indicating some interesting practical aspects. Finally, the theoretical results concerning both considered control structures are validated in a set of numerical simulations and experiments conducted on a laboratory hardware testbed. © 2025 The Author(s).

关键词： Disturbance rejection

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