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Artificial Neural Networks in Export and Import Forecasting: An Analysis of Opportunities 11

Artificial Neural Networks in Export and Import Forecasting:...

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11th IEEE international conference on Intelligent Data Acquisition and Advanced computing systems - Technology and applications (IDAACS)

作者： Luchko, Mykhailo R. Dziubanovska, Nataliia Arzamasova, Oksana West Ukrainian Natl Univ Fac Finance & Accounting 11 Lvivska St UA-46009 Ternopol Ukraine West Ukrainian Natl Univ Fac Comp Informat Technol 11 Lvivska St UA-46009 Ternopol Ukraine West Ukrainian Natl Univ SSU Vocat Coll Econ Law & Informat Technol 11 Lvivska St UA-46009 Ternopol Ukraine

ISBN: (纸本)9781665426053

the paper concerns the issue of forecasting trends in trade relations between Malaysia and Ukraine using artificial neural networks. the current state of trade relations between the countries in the context of the COVID-19 pandemic has been analyzed. Considering the advantages and disadvantages of the types of neural networks built into the software product STATISTICA 10, MLP network has been chosen to build a predictive model of imports and exports of goods. the forecast values for the volumes of exports and imports of goods for the period from January 2021 to December 2022 have been calculated. Comparing the results, the researchers concluded that the artificial neural network is the most successful model for forecasting imports and exports. Suggestions for effective evaluation and forecasting of international trade indicators using the theory of time series and neural network technologies are given and the directions of further scientific research arising from this paper are formed.

关键词： export import forecasting neural network Malaysia Ukraine

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Comprehensive Framework for Facilitating the Deployment of Distributed On-Premise Analytics applications in Resource-Constraint Environments

Comprehensive Framework for Facilitating the Deployment of D...

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IEEE international conference on Automation Science and Engineering (CASE)

作者： Nicolai Schoch Pascal Becker Virendra Ashiwal Andrew Habib ABB Corporate Research Center Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Data Analytics (DA) and Machine Learning (ML) algorithms today are inside many applications and systems. they provide state-of-the-art anomaly detection, pattern recognition, health monitoring, predictive maintenance, or help to optimize and calibrate parameter combinations. However, they usually require strong compute resources. It is therefore a challenge to deploy the corresponding algorithms on edge devices or in embedded systems, such as on cloud-decoupled drives and drive systems, as these usually have strict resource constraints. In this work, we look at the deployment of DA/ML algorithms on combinations of resource-constraint edge and device environments. We present our framework for the structured and guided definition, setup, distribution, and deployment of such DA/ML algorithms. A guided workflow leads a user through algorithm specification and configuration. While doing so, it extracts information, which is necessary for the distribution and deployment of the DA/ML algorithm to a suitable device-edge combination. With this solution, a DA/ML algorithm can be executed for sensor data processing and, in the ML context, for inference, on device-edge combinations without permanent cloud connection, e.g., on a drive/gateway combination on a cruise ship, so that it can be used for real-time anomaly detection or pattern analysis.

关键词： Cloud computing Machine learning algorithms Prediction algorithms Inference algorithms real-time systems Pattern analysis Marine vehicles Anomaly detection Monitoring Predictive maintenance

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A Computational Analysis of Drug-Expanse Predictions from Biomaterials Using Ensemble Model for Healthcare applications

A Computational Analysis of Drug-Expanse Predictions from Bi...

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Intelligent systems and embedded Design (ISED), international conference on

作者： G. Sasipriya B. Lavanya Department of Computer Science University of Madras Chennai India

the results of materials research laboratories' acquisition and evaluation of X-ray diffraction (XRD) data take a significant amount of time and include a lot of laborious and monotonous engineering through humans. To put this into perspective, a synopsis of the process of material synthesis that culminates in an investigation into the composition of the material can take several days to complete. this article explores an alternative approach that employs supervised learning algorithms based on ensemble-stacked neural network techniques. the method involves predicting the behavior of biomaterial granules incorporated with calcium phosphates (CaPs) as a matrix for carrying the anticancer drug carboplatin. the high-vacuum method is utilized for loading, and a set of five compositions is examined: hydroxyapatite (HA), $\beta$ - tricalciumphosphate( $\beta$ - TCP), biphasicHAp(60% $\beta$ - TCP, 40%HA), - $\beta$ TC- $P$ /MgOnanocomposite, and $\beta$ TCP/SiO2. Additionally, the study investigates the deposition of the carboplatin drug on the CaPs' surface at concentrations of 50, 60, and 70 mg/g, applying various machine-learning techniques for analysis. the deep neural network model successfully predicted the delivery release of different NPs with different amounts of phosphorous and calcium in the drug, and it did better than the stacked neural network (SNN) model ways. the determination of the results with the dataset for the following drug release with different compositions was around 97%. this study presents a quantitative approach that can improve the prediction of the drug release efficacy of nanomedicine in a manner that is both accurate and efficient.

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On the Efficiency of AdapTTA: An Adaptive Test-time Augmentation Strategy for Reliable embedded ConvNets 1

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29th IFIP WG 10.5/IEEE international conference on Very Large Scale Integration (VLSI-SoC)

作者： Mocerino, Luca Rizzo, Roberto G. Peluso, Valentino Calimera, Andrea Macii, Enrico Politecn Torino Dept Control & Comp Engn I-10129 Turin Italy Politecn Torino Interuniv Dept Reg & Urban Studies & Planning I-10129 Turin Italy

ISBN: (数字)9783031168185

ISBN: (纸本)9783031168185;9783031168178

Test-time Augmentation (TTA) is a popular technique that aims to improve the accuracy of Convolutional Neural Networks (ConvNets) at inference-time. TTA addresses a limitation inherent to any deep learning pipeline, that is, training datasets cover only a tiny portion of the possible inputs. For this reason, when ported to real-life scenarios, ConvNets may suffer from substantial accuracy loss due to unseen input patterns received under unpredictable external conditions that can mislead the model. TTA tackles this problem directly on the field, first running multiple inferences on a set of altered versions of the same input sample and then computing the final outcome through a consensus of the aggregated predictions. TTA has been conceived to run on cloud systems powered with high-performance GPUs, where the altered inputs get processed in parallel with no (or negligible) performance overhead. Unfortunately, when shifted on embedded CPUs, TTA introduces latency penalties that limit its adoption for edge applications. For a more efficient resource usage, we can rely on an adaptive implementation of TTA, AdapTTA, that adjusts the number of inferences dynamically, depending on the input complexity. In this work, we assess the figures of merit of the AdapTTA framework, exploring different configurations of its basic blocks, i.e., the augmentation policy, the predictions aggregation function, and the model confidence score estimator, suitable for the integration with the proposed adaptive system. We conducted an extensive experimental evaluation, considering state-of-the-art ConvNets for image classification, MobileNets and EfficientNets, deployed onto a commercial embedded device, the ARM Cortex-A CPU. the collected results reveal that thanks to optimal design choices, AdapTTA ensures substantial acceleration compared to a static TTA, with up to 2.21x faster processing preserving the same accuracy level. this comprehensive analysis helps designers identify the most effi

关键词： Test-time augmentation Deep learning embedded systems

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Modeling of Multi-Core CPU Power Consumption during Online Video conference 11

Modeling of Multi-Core CPU Power Consumption during Online V...

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11th IEEE international conference on Intelligent Data Acquisition and Advanced computing systems - Technology and applications (IDAACS)

作者： Zhuravska, Iryna Obukhova, Kateryna Petro Mohyla Black Sea Natl Univ Dept Comp Engn 68 Desantnyk10 UA-54003 Mykolaiv Ukraine

ISBN: (纸本)9781665426053

Recently, more and more services - industrial, educational, medical, etc. - are provided remotely, via the Internet. this has become especially relevant in the field of health care, for example, during the rehabilitation period for post-stroke and post-infarction patients. In this case, several video conferencing sessions are opened simultaneously on the doctor's gadget. this leads to overload of the gadget's processor ( CPU), a significant increase in power consumption, overheating of the cores, and as a result to a violation of the stability of the connection. the proposed Colored Petri Net model and developed software for each cores' monitoring the processes in the CPU, built on the *** architecture, allow decision- making on CPU load management with increasing the share of use of LITTLE cores. this approach will increase the time of use of the user device without recharging to 6.8%.

关键词： video conference remote rehabilitation post-stroke post-infarction patients multi-core CPU big.LITTLE architecture power consumption monitoring Colored Petri Net model

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real-time Flexible Accelerator for Fisheye Image Correction Based on Zynq SoC

Real-Time Flexible Accelerator for Fisheye Image Correction ...

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Mechatronics and Robotics Engineering (ICMRE), international conference on

作者： HoBin Oh Gyu Hyeon Hwang Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Fisheye cameras' broad field of view makes them ideal for a variety of applications, such as robotic navigation, drone vision, and car surround-view systems. Fisheye eyeglasses' intrinsic nonlinear distortion, however, makes it difficult to accurately interpret images and analyze them later. this research proposes a Zynq System on Chip (SoC)-based accelerator to dynamically restore fisheye lens image distortion in real time. the proposed system improves the limitations of conventional field programmable gate array (FPGA) designs that require fixed parameters and leverages the Python Productivity for Zynq (PYNQ) framework to enable dynamic parameter modification without reprogramming. Experimental results show that the proposed system processes images with a resolution of 960x640 at a speed of 135.7 frames per second (fps), achieving more than 20 times faster performance compared to software-based solutions. Additionally, it can maintain real-time performance even at Full HD resolution. We also evaluated the accuracy by comparing the fisheye distortion correction of four interpolation techniques provided by OpenCV with the quality indicators Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). It is also flexible enough to allow the system to dynamically adapt to environmental changes and process images efficiently. this research provides a highly flexible, real-time fisheye distortion correction system for various industries such as robotics, automotive, and drones.

关键词： Image resolution PSNR Navigation Nonlinear distortion real-time systems System-on-chip Automobiles Vehicle dynamics Field programmable gate arrays Drones

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Optimizing Algorithms for Human Behavior Recognition: Single-Modality Spatiotemporal Convolutional Networks

Optimizing Algorithms for Human Behavior Recognition: Single...

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international conference on Behavior, Economic and Social computing (BESC)

作者： Rui Zhao Jutao Hao Zhikun Feng Xianzhi Wang Mukesh Prasad Huan Huo Faculty of Engineering and IT School of Computer Science University of Technology Sydney Sydney Australia School of Electric Information Engineering Shanghai Dianji University Shanghai China School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (数字)9798331531904

ISBN: (纸本)9798331531911

Behavior recognition constitutes a pivotal area of research within computer vision and serves as an integral technology in video surveillance and related domains, thereby possessing substantial research value. Convolutional neural networks (CNNs) have been proven to possess robust learning capabilities. However, due to the high complexity and variability of human behavior, recognizing human behaviors in videos remains challenging due to the difficulty of capturing useful features over long time series and the redundancy of multi-frame information. To address these issues and improve the utilization of continuous frame information, we have designed a cross-frame multiscale information fusion module. this module allows the backbone network to acquire multiscale temporal features from different frames, thereby reducing information redundancy across multiple frames while minimizing additional computational costs. Furthermore, to mitigate the problem of video temporal feature loss, we introduce an easily integrable attention mechanism module, the Easy-Attention Block. this module facilitates rapid capture of long-range dependencies, enabling the model to focus on the most relative parts of continuous video sequences and suppress irrelevant temporal information. Consequently, the model can extract more discriminative spatial-temporal features, thereby enhancing its efficiency in handling long sequence data. Experimental results express that this model can achieve high recognition accuracy and effectively extract the temporal features of video, suitable for real-time applications in critical infrastructure surveillance, which enhancing safety and reducing potential risks in energy power systems.

关键词： Computational modeling Redundancy Video sequences Feature extraction real-time systems Data models Behavioral sciences Spatiotemporal phenomena Safety Power systems

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Dynamics Modeling of Continuum Robots Using Screw theory for real-time Control

Dynamics Modeling of Continuum Robots Using Screw Theory for...

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RSI/ISM international conference on Robotics and Mechatronics (ICROM)

作者： Saeedeh Shekari S. Ali A. Moosavian Dept of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) Lab K. N. Toosi University of Technology Tehran Iran

Modeling the dynamics of continuum robotic arms is challenging due to the highly nonlinear nature and uncertain and complicated inherent structure. this affects the application of continuum arms in various aspects, such as inverse kinematics, path generation, optimization, and control. this paper deals with the dynamics modeling of continuum robotic arms using screw theory to develop a suitable model with high accuracy, speed, and low computational load to achieve real-time control. By using the representation of the Lie group of screws in the screw theory, which simplifies the mathematics of the problem, it is possible to achieve a real-time integrated representation of the dynamics of the deformation of the body of continuum robotic arms. this provides the possibility of accurately predicting the system behavior even in the presence of external forces acting on the end-effector. the experimentally validated results for circular and infinite motion trajectories show the model effectiveness in enhancing the trajectory tracking performance and ability to estimate configuration shape changes under external loads. the integration of this model in soft robotic arms has the potential to revolutionize their application in a wide range of industries and medical applications.

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CyberShell: Meeting the Need for Both Fashion and Smart Functionality Using Electronic Wearables

CyberShell: Meeting the Need for Both Fashion and Smart Func...

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Sayed Abdullah Al Qallaf Rola Nour Eldine Jasim Al Meer Bader Al Tayyar Seyed Esmaeili Amal Alateyah College of Engineering and Applied Sciences American Unviersity of Kuwait Salmiya Kuwait

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

the purpose of this project is to merge the two worlds of fashion and technology to propel the industry of fashion-forward as a futuristic entity. CycberShell aims to seamlessly integrate aesthetic appeal with technical innovation, enhancing the quality of life for users. With the current existence of wearables, it is not strange that the technical school of thought would head towards this line of wearable applications. Wearing a piece of the future along with sustainability in mind will lead to a new era of self-expression and modern wear. the presented product offers a range of customization options and integrated technology, blending application-based experiences with embedded hardware to provide real-time feedback and adjustments. An application-based interface that utilizes code and mini-computing translates the implemented customization options into tangible changes, ensuring a seamless user experience. After all, investing in a single piece of customizable clothing can significantly reduce textile waste.

关键词： Industries Technological innovation Mechatronics Propulsion User experience real-time systems Hardware Wearable devices Sustainable development Textiles

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An eBPF/XDP-Based Architecture for Efficient SOME/IP Service Discovery

An eBPF/XDP-Based Architecture for Efficient SOME/IP Service...

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Mechatronics and Robotics Engineering (ICMRE), international conference on

作者： Seong Hyeon Lim Sung Bhin Oh Syng Keun Cha Young Soo Do Se Jeong Lim Hyeok Jun Choi Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

the rapid evolution of Software-Defined Vehicles (SDVs) necessitates efficient and scalable communication mechanisms between in-vehicle Electronic Control Units (ECUs). SOME/IP, particularly its Service Discovery component (SOME/IP-SD), has emerged as a critical protocol for enabling dynamic service-oriented communication in this context. However, traditional user-space implementations of SOME/IP-SD suffer from performance limitations, primarily due to excessive context switching overhead and CPU resource consumption, which can hinder real-time performance in automotive applications. this paper introduces a novel kernel-based architecture for SOME/IP-SD processing that leverages the power of eBPF/XDP. the proposed architecture offloads SOME/IP-SD packet processing to the kernel, significantly reducing context switching and optimizing system resource utilization. By processing SOME/IP service discovery at the kernel level with eBPF/XDP, the architecture minimizes latency and improves overall performance through early packet filtering and efficient data sharing between kernel and user space via eBPF maps. the proposed architecture aims to enhance the responsiveness of service discovery, a critical requirement for real-time applications in SDVs. this kernel-level approach demonstrates the potential of eBPF/XDP in optimizing automotive network protocols and is expected to contribute to the advancement of real-time communication systems in next-generation vehicles.

关键词： Protocols Runtime Filtering Switches real-time systems Security Kernel Low latency communication Vehicle dynamics Next generation networking

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