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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Ayham Alharbat Gabriele Ruscelli Roberto Diversi Abeje Mersha Smart Mechatronics and Robotics research group Saxion University of Applied Science Enschede The Netherlands Robotics and Mechatronics research group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede The Netherlands Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This paper presents an external wrench estimator that uses a hybrid dynamics model consisting of a firstprinciples model and a neural network. This framework addresses one of the limitations of the state-of-the-art modelbased wrench observers: the wrench estimation of these observers comprises the external wrench (e.g. collision, physical interaction, wind); in addition to residual wrench (e.g. model parameters uncertainty or unmodeled dynamics). This is a problem if these wrench estimations are to be used as wrench feedback to a force controller, for example. In the proposed framework, a neural network is combined with a first-principles model to estimate the residual dynamics arising from unmodeled dynamics and parameters uncertainties, then, the hybrid trained model is used to estimate the external wrench, leading to a wrench estimation that has smaller contributions from the residual dynamics, and affected more by the external wrench. This method is validated with numerical simulations of an aerial robot in different flying scenarios and different types of residual dynamics, and the statistical analysis of the results shows that the wrench estimation error has improved significantly compared to a model-based wrench observer using only a firstprinciples model.

关键词： Uncertain systems Analytical models Statistical analysis Atmospheric modeling Dynamics Neural networks Observers Autonomous aerial vehicles Numerical simulation Numerical models

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A New Fixed Point Noise Cancellation Method for Suppressing Power Line Interference in Electrocardiogram Signals 10

A New Fixed Point Noise Cancellation Method for Suppressing ...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Faiz, Mohammed Mujahid Ulla Reni, Saumya Kareem Kale, Izzet ECE Department Presidency University Bengaluru560064 India Applied DSP and VLSI Research Group Department of Computer Science and Engineering University of Westminster LondonW1W 6UW United Kingdom

ISBN: (纸本)9781665485579

In this article, a new fixed point Leaky Sign Regressor Least Mean Mixed Norm (LSRLMMN) powered adaptive noise cancellation technique is being used for eliminating the Power Line Interference (PLI) noise embedded in the ElectroCardioGram (ECG) signal. The fixed point LSRLMMN powered noise cancellation technique used in this article has been completely quantized. The intention for the extensive quantization study and modeling approach was with a view to the physical integrated circuit implementation. All the modeling and simulation studies were carried out at the bit-level with various loss of precision schemes to ensure compliance with the set specification. The filter coefficients and all the data paths are quantized in order to establish at a high-level behavioral level of the parameters for a decreased complexity in integrated circuit implementation. © 2022 IEEE.

关键词： Electrocardiograms

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MKGL: Mastery of a Three-Word Language

arXiv

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arXiv 2024年

作者： Guo, Lingbing Bo, Zhongpu Chen, Zhuo Zhang, Yichi Chen, Jiaoyan Lan, Yarong Sun, Mengshu Zhang, Zhiqiang Luo, Yangyifei Li, Qian Zhang, Qiang Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China Zhejiang University Ant Group Joint Laboratory of Knowledge Graph China Ant Group China Department of Computer Science The University of Manchester United Kingdom School of Computer Science and Engineering Beihang University China School of Computer Science Beijing University of Posts and Telecommunications China School of Software Technology Zhejiang University China

Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs), which describe facts in the form of triplets and allow minimal hallucinations, remains an underexplored frontier. In this paper, we investigate the integration of LLMs with KGs by introducing a specialized KG Language (KGL), where a sentence precisely consists of an entity noun, a relation verb, and ends with another entity noun. Despite KGL's unfamiliar vocabulary to the LLM, we facilitate its learning through a tailored dictionary and illustrative sentences, and enhance context understanding via real-time KG context retrieval and KGL token embedding augmentation. Our results reveal that LLMs can achieve fluency in KGL, drastically reducing errors compared to conventional KG embedding methods on KG completion. Furthermore, our enhanced LLM shows exceptional competence in generating accurate three-word sentences from an initial entity and interpreting new unseen terms out of KGs. © 2024, CC BY.

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Wind speed prediction and insight for generalized predictive modeling framework: a comparative study for different artificial intelligence models

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Neural Computing and Applications 2024年第23期36卷 14119-14150页

作者： Bhagat, Suraj Kumar Tiyasha, Tiyasha Shather, A.H. Jamei, Mehdi Kumar, Adarsh Al-Khafaji, Zainab Goliatt, Leonardo Shafik, Shafik S. Alawi, Omer A. Yaseen, Zaher Mundher Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Engineering Technology Al Kitab University Altun Kopru Kirkuk00964 Iraq Faculty of Civil Engineering and Architecture Shahid Chamran University of Ahvaz Ahvaz Iran Canadian Centre for Climate Change and Adaptation University of Prince Edward Island St PetersPE Canada Building and Construction Techniques Engineering Department College of Engineering and Engineering Techniques Al-Mustaqbal University Hillah Babylon51001 Iraq Department of Computational and Applied Mechanics Federal University of Juiz de Fora Juiz de Fora Brazil Experimental Nuclear Radiation Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq Johor Bahru Skudai81310 Malaysia Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India Dept of Environmental Science Gitam School of Science GITAM University Visakhapatnam India

Wind speed (WS) has played a vital role in local urban and sub-urban weather, agriculture, and ecosystem. Several meteorological parameters are influencing WS such as relative humidity (at 2 m, %), surface pressure (kPa), maximum temperature (at 2 m, °C), minimum temperature (at 2 m, °C), average temperature (at 2 m, °C), and all sky insolation incident on a horizontal surface (kW-h/m2/day). The current research was conducted to predict WS at different locations at Vietnam using the feasibility of computer aid models (i.e., multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost) and random forest generator (Ranger)). Pearson correlation (PC) was investigated to select the high significant predictors to predict the WS at 10 m high. All inputs (maximum number, 6) are chosen by the PC approach for PhuongNinh, DaNang, and HaNoi;and for minimum number of inputs i.e four, are selected for PhuongHung, CanTho, and SaPa city;that exhibit the relationship with WS, citywise. The sequence selection of input parameters differed in each station as per the PC analysis. Based on the statistical evaluation and graphical presentation, MARS model attained the best prediction results, followed by XGBoost and Ranger. MARS predictive model remains at the top performance among others based on 95% confidence interval. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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A novel fixed-point leaky sign regressor algorithm based adaptive noise canceller for PLI cancellation in ECG signals 7

A novel fixed-point leaky sign regressor algorithm based ada...

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7th IEEE Forum on Research and Technologies for Society and Industry Innovation, RTSI 2022

作者： Faiz, Mohammed Mujahid Ulla Kale, Izzet University of Westminster Applied DSP and VLSI Research Group Department of Computer Science and Engineering LondonW1W 6UW United Kingdom Presidency University ECE Department Bengaluru560064 India

ISBN: (数字)9781665497398

ISBN: (纸本)9781665497398

In this paper, a novel fixed-point Leaky Sign Regressor Algorithm (LSRA) based adaptive noise canceller has been employed for the cancellation of 60 Hz Power Line Interference (PLI) from the ElectroCardioGram (ECG) signal. A sufficient condition for the convergence in the mean of the LSRA algorithm is also derived. The fixed-point LSRA-based adaptive noise canceller employed in this work is fully quantized using an in-house quantize function. The most effective number of quantization bits required for the various parameters are found to be 6-bits and are determined through rigorous simulations. The filtered ECG signal free from 60 Hz PLI is successfully recovered using a novel 6-bit fixed-point LSRA-based adaptive noise canceller. © 2022 IEEE.

关键词： Electrocardiograms

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on computer Aided Systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Single Loop Control of a Common DC-Bus-Configured Traction Motor Emulator Using State Feedback Linearization Method

Single Loop Control of a Common DC-Bus-Configured Traction M...

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2021 IEEE Southern Power Electronics Conference, SPEC 2021

作者： Kadam, Arvind H. Williamson, Sheldon S. Group Faculty of Engineering and Applied Science Department of Electrical Computer and Software Engineeirng 2000 Simcoe Street North OshawaONL1G 0C5 Canada

ISBN: (纸本)9781665436236

Nowadays, the LCL (inductor-capacitor-inductor) filter is an integral part of every power electronic converter system operating on current control. Though higher order, the LCL filter due to its high harmonic suppression capability are widely adopted in back-to-back converter systems or grid connected converter systems. Normally, with LCL filter, the control system follows double closed loop with outer loop controlling DC voltage and inner loop controlling AC current. However, for some special applications like common DC-bus-configured motor emulator (ME) system, the DC bus voltage is not required to be controlled. In addition, the presence of LCL filter introduces cross coupling of flux and torque component in case of AC motor emulation. The double closed loop control is generally adopted to decouple torque and flux components, however the controller design is more complex process. Therefore, this paper presents a single loop control of a common DC-bus-configured ME system with LCL filter based on state feedback linearization method. The decoupling equations in direct and quadrature axis are derived here. The simulation results presented for a 2.0 kW PMSM motor validate the stability as well as closed loop operation of the system with single loop control. © 2021 IEEE.

关键词： Synchronous motors

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CosineRecom: A KNN-Based Movie Recommendation System Using Cosine Similarity

CosineRecom: A KNN-Based Movie Recommendation System Using C...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Mandeep Singh Prafull Mishra Nitin Aggarwal Vinay Kumar Pramod Kumar Sharma Ritesh Yadav School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad UP India Supervisor Oracle Development Database Admin Data Science MFPS PA USA Department of CSIT KIET Group of Institutions Ghaziabad UP India Department of Applied Science KIET Group of Institute Ghaziabad UP India Department of Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Information overload is a major problem for many people who use the internet. This is because there is so much information available that it can be hard to know where to start. Filtering tools, like the Recommender System, are needed to solve this problem. These tools give users access to relevant information and let them customise their search results based on their liking. The Collaborative Filtering Recommendation System finds the group of active users who are your closest neighbours. This study shows many ways to use the supervised machine learning algorithm (KNN) method for the Movie recommendation System with different similarity measures, such as the cosine metric, Squared Error Similarity, Linear Correlation Coefficient, and Baseline Predictor Correlation. For the movie recommender system, these many changes to the KNN algorithm were tested on real data from the given dataset and compared based on accuracy measures such as the number of matching pairs and different types of error like mean absolute error, squared mean error, and squared root mean error. By making changes to the web browser, this recommendation system processing tool could be used as a substitute in the real world.

关键词： Machine learning algorithms Filtering Collaborative filtering Measurement uncertainty Machine learning Nearest neighbor methods Motion pictures Prediction algorithms Matrices Recommender systems

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A Novel Framework for Learning and Classifying the Imbalanced Multi-Label Data

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computer Systems science & engineering 2024年第5期48卷 1367-1385页

作者： P.K.A.Chitra S.Appavu alias Balamurugan S.Geetha Seifedine Kadry Jungeun Kim Keejun Han Department of Computer Science and Engineering SRM Institute of Science and TechnologyTiruchirappalliTamil Nadu603203India Department of Computer Science and Engineering Periyar Maniammai Institute of Science&Technology(Deemed to be University)ThanjavurTamil Nadu613403India School of Computer Science and Engineering ChennaiTamil Nadu600048India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346AjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos10150Lebanon Department of Software Kongju National UniversityCheonan31080Republic of Korea Division of Computer Engineering Hansung UniversitySeoul02876Republic of Korea

A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label *** main objective of this work is to create a novel framework for learning and classifying imbalancedmulti-label *** work proposes a framework of two *** imbalanced distribution of themulti-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase ***,an adaptive weighted l21 norm regularized(Elastic-net)multilabel logistic regression is used to predict unseen samples in phase *** proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional *** minority labels in these samples are called difficult minority labels and are more prone to penalize classification *** concurrentmeasure is considered borderline,and labels associated with samples are regarded as borderline labels in the decision *** phase II,a novel adaptive l21 norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic *** on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods.

关键词： Multi-label imbalanced data multi-label learning Borderline MLSMOTE concurrent multi-label adaptive weighted multi-label elastic net difficult minority label

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Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait

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PeerJ computer science 2025年 11卷 1-42页

作者： Naz, Anam Khan, Hikmat Ullah Alsahfi, Tariq Alhajlah, Mousa Alshemaimri, Bader Daud, Ali Department of Information Technology University of Sargodha Punjab Sargodha Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Applied of Computer Science College King Saud University Riyadh Saudi Arabia Software Engineering Department College of Computing and Information Sciences King Saud University Riyadh Saudi Arabia Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates

Understanding human personality traits is significant as it helps in decision making related to consumers’ behavior, career counselling, team building and top candidates’ selection for recruitment. Among various traits, openness is essential as it shows both diverse aspects of sensitive nature or intuitive nature. The individuals having a sensing nature tends to be more practical and prefer to focus on concrete information whereas the users having intuitive trait type is characterized by a focus on abstract ideas, creative thinking and future-oriented perspectives. In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type Indicator (MBTI) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. For comparison, we explore textual features of Frequency-Inverse Document (TF-IDF) and parts of speech (POS) tagging with machine learning models and deep features of word2vec and global vectors for word representation (GloVe) with deep learning models. The comprehensive empirical analysis reveals that TF-IDF used with gradient boosting achieves high accuracy of 90% whereas, the deep feature of sentence embeddings when used and with deep model bidirectional long short-term memory (Bi-LSTM) achieves 90.5% accuracy. The best results have been achieved using the latest Transformer-based DistilBERT, which achieves the highest accuracy of 92% outperforming the existing studies in relevant literature. Subjects Human-computer Interaction, Artificial Intelligence, Data Mining and Machine Learning, Natural Language and Speech © 2025 xxxx

关键词： Deep learning

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