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15th IEEE International Conference on Industrial and Information systems, ICIIS 2020

作者： Mithal, Aditya Baser, Manit Dhiraj Birla Institute of Technology and Science Department of Computer Science Engineering Pilani India CSIR-Central Electronics Engineering Research Institute Intelligent Systems Group Pilani India

ISBN: (纸本)9781728185248

Automating object detection for surveillance purpose and threat detection is beneficial as it may compensate for the human error and will save time, which is of significant economic value. For the end-to-end classification process and feature extraction, the CNN approach requires large amounts of data. To overcome this limited availability of data, we have presented a transfer learning approach with various object detection models for single and multiple detections on two types of the dataset: Single-channelled (GDXray dataset) and Multichanneled(SIXray dataset). We have presented comparisons between the various models(Faster R-CNN with ResNet50, SSD with VGG16, YOLOv3 with ResNet50, and RetinaNet with ResNet50). The best results were achieved on Faster-RCNN(ResNet50) with 0.966 mAP for the four-class object detection problem(GDXray Dataset) and 0.845 mAP for the two-class object detection problem(SIXray Dataset). © 2020 IEEE.

关键词： Object detection

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Describing Brachiopods Shape Based on Fusion of Curvature and Fourier Descriptors 12th

Describing Brachiopods Shape Based on Fusion of Curvature an...

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12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020

作者： Khouya, Youssef Ait Oussous, Mohammed Ait Ghorbel, Faouzi Software Engineering and Information Systems Engineering Team Faculty of Sciences and Techniques Errachidia Moulay Ismail University Meknes Morocco Qualifying High School Ibn Albana Ministry of National Education Rabat Morocco GRIFT Research Group CRISTAL Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia

ISBN: (纸本)9783030736880

The fossils classification has a great importance in palaeontological studies. They make it possible to understand the biodiversity in its morphological dimension and they show the morphological transformations suffered during the biological evolution. In this paper, we propose a descriptor for fossils classification and particularly Brachiopods. The proposed descriptor is a fusion between the curvature and Fourier descriptors. The curvature properties provide an apparently powerful cue to the underlying structure of the curve and captures completely the structure of planar curve. In addition, it is stable and complete. Fourier descriptors are powerful features for the recognition of two-dimensional connected shapes and is supported by the well-developed and well-understood Fourier theory. We used the Fourier descriptors proposed by Ghorbel, this set of invariants is also stable and complete. Experimental results are performed to illustrate the performance of the proposed descriptor. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Biodiversity

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Color Models for Skin Lesion Classification from Dermatoscopic Images 8th

Color Models for Skin Lesion Classification from Dermatoscop...

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8th International Workshop on Combinations of Intelligent Methods and Applications, CIMA 2018 held in conjunction with the 30th IEEE International Conference on Tools with Artificial Intelligence, IEEE ICTAI 2018

作者： Mporas, Iosif Perikos, Isidoros Paraskevas, Michael Communications and Intelligent Systems Group School of Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom Computer Engineering and Informatics Department University of Patras Patras Greece Computer Technology Institute and Press ‘Diophantus’ Patras Greece Electrical and Computer Engineering Department University of Peloponnese Patras Greece

ISBN: (纸本)9789811519178

In this paper, we present an architecture for classification of pigmented skin lesions from dermatoscopic images. The architecture is using image preprocessing for natural hair removal and image segmentation for extraction of the skin lesion area followed by computation of statistical values of colors as features. The color-based features were extracted from several well-known and widely used color models. Several classification algorithms were evaluated with the best performing classification algorithm being the AdaBoost with random forest classifier with classification accuracy equal to 73.08% when using RGB-based features only and 74.26% when combining RGB, HSV, and YIQ color model-based features. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Fisheye Camera and Ultrasonic Sensor Fusion For Near-Field Obstacle Perception in Bird’s-Eye-View

arXiv

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arXiv 2024年

作者： Das, Arindam Paul, Sudarshan Scholz, Niko Malviya, Akhilesh Kumar Sistu, Ganesh Bhattacharya, Ujjwal Eising, Ciarán The Department of Electronic and Computer Engineering University of Limerick LimerickV94 T9PX Ireland The Data Driven Computer Engineering Research Group University of Limerick LimerickV94 T9PX Ireland The Department of Driving Software and Systems Valeo India Chennai600130 India Valeo Driving Assistance Research Hummendorfer Str. 74 Kronach96317 Germany Valeo Vision Systems Dunmore Road Co. Galway TuamH54 Y276 Ireland Unit Indian Statistical Institute Kolkata700108 India

Accurate obstacle identification represents a fundamental challenge within the scope of near-field perception for autonomous driving. Conventionally, fisheye cameras are frequently employed for comprehensive surround-view perception, including rear-view obstacle localization. However, the performance of such cameras can significantly deteriorate in low-light conditions, during nighttime, or when subjected to intense sun glare. Conversely, cost-effective sensors like ultrasonic sensors remain largely unaffected under these conditions. Therefore, we present, to our knowledge, the first end-to-end multimodal fusion model tailored for efficient obstacle perception in a bird’s-eye-view (BEV) perspective, utilizing fisheye cameras and ultrasonic sensors. Initially, ResNeXt-50 is employed as a set of unimodal encoders to extract features specific to each modality. Subsequently, the feature space associated with the visible spectrum undergoes transformation into BEV. The fusion of these two modalities is facilitated via concatenation. At the same time, the ultrasonic spectrum-based unimodal feature maps pass through content-aware dilated convolution, applied to mitigate the sensor misalignment between two sensors in the fused feature space. Finally, the fused features are utilized by a two-stage semantic occupancy decoder to generate grid-wise predictions for precise obstacle perception. We conduct a systematic investigation to determine the optimal strategy for multimodal fusion of both sensors. We provide insights into our dataset creation procedures, annotation guidelines, and perform a thorough data analysis to ensure adequate coverage of all scenarios. When applied to our dataset, the experimental results underscore the robustness and effectiveness of our proposed multimodal fusion approach. A short video highlighting an overview of this work along with its more qualitative results can be seen at https://***/JmSLBBL9Ruo. © 2024, CC BY.

关键词： Birds

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Thermal resistance prediction model for IC packaging optimization and design cycle reduction

Thermal resistance prediction model for IC packaging optimiz...

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Electronic Components and Technology Conference (ECTC)

作者： Guan-Wei Chen Yan-Cheng Lin Chung-Hsiang Hsu Tang-Yuan Chen Chen-Chao Wang Chin-Pin Hung Hung-Kai Wang Institute of Innovative Semiconductor and Sustainable Manufacturing Nantinal Cheng – Kung University Tainan Taiwan Institute of Manufacturing Inforamtion and Systems Nantinal Cheng – Kung University Tainan Taiwan Department of Manufacturing Inforamtion and Systems Nantinal Cheng – Kung University Tainan Taiwan Group Corporate Research and Development Division Advanced Semiconductor Engineering Inc. Kaohsiung Taiwan Corporate R&D Center Advanced Semiconductor Engineering Inc. Kaohsiung Taiwan Advanced Semiconductor Engineering Inc. Kaohsiung Taiwan Department of Computer Science and Information Engineering Nantinal Cheng - Kung University Tainan Taiwan

ISBN: (数字)9798350375985

ISBN: (纸本)9798350375992

The development of highly reliable packaging products is paramount, as accurately predicting the thermal resistance values (Theta JA, JB, JC) of IC packages is crucial for calculating maximum power dissipation and mitigating self-heating effects. In this paper, we focus on the prediction and analysis of thermal resistance in packaged products. Traditionally, finite element methods (FEM) are used for simulations to predict thermal resistance. However, these simulations often employ idealized assumptions that may not align with real-world conditions. Furthermore, traditional simulation methods require substantial memory space and high-performance hardware, leading to significantly increased computation times as the complexity of the simulation increases, thereby extending the product verification timeline. Machine learning methods can effectively improve operational speed and reduce costs. This research utilizes datasets generated from past FEM simulations, testing various machine learning models, among which the Artificial Neural Network (ANN) models, a deep learning approach, yielded the best results. These models were then primarily employed for predicting the characteristics of packaged components. The findings indicate that the predicted results closely align with the FEM simulation data, with the percentage error in thermal resistance for QFN packages being less than 5%, thereby achieving structural optimization of packaged products and shortening the design cycle. Additionally, a user interface platform was developed to enhance research efficiency significantly. Researchers simply need to select the desired packaging technology product and enter the structural parameters to generate predictive results and visual charts, making it accessible even for operators lacking programming skills. Ultimately, the goal is to achieve visual structural optimization and shorten the design cycle for IC packaged products, serving as a basis for future structural studies of prod

关键词： Visualization Thermal resistance Computational modeling Artificial neural networks Predictive models Packaging Electronic packaging thermal management

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Accounting for the Special Role of Infrastructure-assisted Collective Perception

Accounting for the Special Role of Infrastructure-assisted C...

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International Conference on Intelligent Transportation

作者： Florian Alexander Schiegg Anna–Lisa Rueeck Jörg Gamerdinger Hugues Tchouankem Edmir Xhoxhi Georg Volk Distributed Intelligence and Systems Corporate Research Robert Bosch GmbH Hildesheim Germany Function Development 3D Surround View Engineering Parking Functions Robert Bosch GmbH Feuerbach Germany Department of Computer Science Embedded Systems Group University of Tübingen Tübingen Germany Institute of Communication Technology Leibniz University Hanover Hanover Germany

The number of traffic participants has increased exponentially over the past century, making a traffic accident the most likely cause of death for children and young adults today. Vehicle-to-Everything (V2X) communication is regarded as one of the most promising emerging technologies to enhance road safety. It allows traffic participants to exchange relevant information to increase their environmental perception. Cooperative Awareness, a V2X service that is already being deployed in Europe, enables road users to share their dynamic states. Collective Perception (CP), currently in the final stage of standardization in the different regions of the world, enables the exchange of sensor-detected objects. The latter is thus not only relevant for connected vehicles but can further be used by sensor-equipped infrastructure to support vehicular perception, opening a whole new range of possibilities. The objective of this work is to investigate mechanisms to account for the special role of Infrastructure-assisted Collective Perception (ICP). Packet duplication is introduced for ICP to enhance the communication reliability. Additionally, different message generation rules prioritizing ICP are proposed and compared based on the enabled environmental perception. Simulations show significant improvements of the average precision, reaching an average increase of about 60% for ICP in comparison to V2V -based CP in scenarios with low connected vehicle densities.

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A stabilizer based predictive control scheme for smart inverters in weak grid

A stabilizer based predictive control scheme for smart inver...

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2020 IEEE Texas Power and Energy Conference, TPEC 2020

作者： Umar, Muhammad Farooq Khan, Ahmad Shadmand, Mohammad B. Power Electronics and Autonomous Systems Research Group Kansas State University Dept. of Electrical and Computer Engineering ManhattanKS United States

ISBN: (纸本)9781728144368

This paper presents a self-stabilization mechanism based on finite-set model predictive control (FCS-MPC) framework for smart inverters operating in weak grid conditions. As weak grid's large parasitic impedance and low short-circuit-ratio (SCR) challenge the stable operation of grid-connected inverters. Specifically, the inverter may experience frequencies that might excite the LCL filter resonance phenomenon. The inverter stability collapses if this LCL resonance is triggered. To address this issue, a robust predictive controller is proposed that features a self-stabilization mechanism for smart inverters interacting with a weak grid. The proposed methodology utilizes the idea that in stiff grid conditions the grid current feedback (GCF) is stable and in weak grid conditions the inverter current feedback (ICF) is stable. Therefore, the proposed FCS-MPC toggles between GCF and ICF to achieve inherent LCL filter resonance damping. The toggling action between GCF and ICF is leveraged by comparing the moving RMS grid current with threshold current as a constrained in the proposed FCS-MPC cost function. The theoretical analyses are verified by several case studies for a single-phase grid-connected inverter. The analysis and results demonstrated that the proposed FCS-MPC operates well under weak, ultra-weak and stiff grid conditions. © 2020 IEEE.

关键词： Model predictive control

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Direct data-driven LPV control of nonlinear systems: An experimental result

arXiv

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arXiv 2022年

作者： Verhoek, Chris Abbas, Hossam S. Tóth, Roland Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven5600MB Netherlands Institute for Electrical Engineering in Medicine Universität zu Lübeck Lübeck23558 Germany Systems and Control Lab Institute for Computer Science and Control Budapest1111 Hungary

We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) system concept. An LPV data-driven representation is used as a surrogate LPV form of the data-driven representation of the original nonlinear system. The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior. Efficiency of the proposed approach is demonstrated experimentally on a nonlinear unbalanced disc system showing for the first time in the literature that behavioral data-driven methods are capable to stabilize arbitrary forced equilibria of a real-world nonlinear system by the use of only 7 data points. © 2022, CC BY-NC-SA.

关键词： Nonlinear systems

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Residential energy consumption prediction using inter-household energy data and socioeconomic information 28

Residential energy consumption prediction using inter-househ...

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28th European Signal Processing Conference, EUSIPCO 2020

作者： Schirmer, Pascal A. Geiger, Christian Mporas, Iosif Comm. and Intelligent Systems Group School of Eng. and Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom Institute for Machine Tools Faculty of Mechanical Engineering Technical University of Munich 85748 Garching Germany

ISBN: (纸本)9789082797053

Previous studies have shown that residential energy consumption prediction accuracy can be improved when households energy data are fused with residents' socioeconomic information. In this article we propose an architecture for the prediction of residential energy consumption using past energy consumption from other/neighboring households in combination with socioeconomic information of the corresponding residents. The architecture is based on a Long Short Term Memory model and was evaluated using a large-scale dataset monitoring households of London. The proposed approach significantly improves the accuracy of the energy consumption predictor reducing the mean absolute error up to 25.2% with prediction error rate equal to 5.4%. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Energy utilization

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SMedBERT: A knowledge-enhanced pre-trained language model with structured semantics for medical text mining 59

SMedBERT: A knowledge-enhanced pre-trained language model wi...

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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Zhang, Taolin Cai, Zerui Wang, Chengyu Qiu, Minghui Yang, Bite He, Xiaofeng School of Software Engineering East China Normal University China Alibaba Group Shanghai Key Laboratory of Trsustworthy Computing China School of Computer Science and Technology East China Normal University China DXY Shanghai Research Institute for Intelligent Autonomous Systems China

ISBN: (纸本)9781954085527

Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding. For medical domains, the background knowledge sources are especially useful, due to the massive medical terms and their complicated relations are difficult to understand in text. In this work, we introduce SMedBERT, a medical PLM trained on large-scale medical corpora, incorporating deep structured semantics knowledge from neighbours of linked-entity. In SMedBERT, the mention-neighbour hybrid attention is proposed to learn heterogeneous-entity information, which infuses the semantic representations of entity types into the homogeneous neighbouring entity structure. Apart from knowledge integration as external features, we propose to employ the neighbors of linked-entities in the knowledge graph as additional global contexts of text mentions, allowing them to communicate via shared neighbors, thus enrich their semantic representations. Experiments demonstrate that SMedBERT significantly outperforms strong baselines in various knowledge-intensive Chinese medical tasks. It also improves the performance of other tasks such as question answering, question matching and natural language inference. © 2021 Association for Computational Linguistics

关键词： Knowledge graph

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