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International Conference on Power Electronics and Drive systems (PEDS)

作者： Anindita Golder Kushan Tharuka Lulbadda Tarlochan Sidhu Sheldon S. Williamson Department of Electrical and Computer Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada

More and more electric vehicles and charging stations are penetrating into the power system today which introduce new challenges. As a result, the concept of vehicle to vehicle charging (V2V) is gaining interest. Therefore, it is important to understand the concept of V2V charging. In this paper, some of the recent developments in the area of vehicle to vehicle (V2V) charging are explored.

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Design Considerations for Electric Traction Power systems

Design Considerations for Electric Traction Power Systems

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Anindita Golder Jigyesh Sharma Kushan Tharuka Tarlochan Sidhu Sheldon S. Williamson Department of Electrical and Computer Engineering Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Smart Transportation Electrification and Energy Research (STEER) Group Ontario Tech University Oshawa ON Canada

There is growing interest in e-mobility which is not only limited to electric vehicles but also mass transit systems, heavy duty vehicles, off-road vehicles and so on. With the intense focus on sustainability and achieving net zero emissions, most transit systems will be electrified. The impact of mass transit electrification on the existing power system needs a thorough and detailed analysis. Also, the upgrade from fuel powered transit systems to electric transit systems needs to be done with minimal changes to the existing power network and maximum utilization of available resources in the transit system. Therefore, the study of electric transit systems, its impact on the power system and challenges that can arise from electrifying railways are presented in this paper.

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Semi-supervised trajectory classification using convolutional auto-encoders 1

Semi-supervised trajectory classification using convolutiona...

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1st ACM SIGSPATIAL International Workshop on on Animal Movement Ecology and Human Mobility, HANIMOB 2021

作者： Makris, Antonios Kontopoulos, Ioannis Psomakelis, Evangelos Tserpes, Konstantinos Department of Informatics and Telematics Harokopio University of Athens Distributed Knowledge and Media Systems Group National Technical University of Athens Athens Greece Harokopio University of Athens Department of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (纸本)9781450391221

Massive volumes of high-frequency and high-volume data are constantly being generated by the vast amount of available tracking sensors of moving objects. This phenomenon can be strongly observed in the maritime domain since most of the vessels transmit their location periodically. Automated methodologies able to extract meaningful information from vessel data is of utmost importance since it can reveal abnormal or illegal vessel activities in due time. Supervised deep learning approaches such as convolutional neural networks (CNNs) require a large-scale annotated image dataset. Thus, semi-and unsupervised feature learning algorithms which learn image features from unlabelled data are able to mitigate this problem. To this end, in this work we propose a semi-supervised convolutional autoencoder (CAE) model for trajectory classification which is able to provide high-precision classification of mobility patterns. Experimental results demonstrated that the vessel activity classification performance can reach an F1-score of over 94%. © 2021 ACM.

关键词： Learning algorithms

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Distributed AI in Zero-touch Provisioning for Edge Networks: Challenges and Research Directions

arXiv

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arXiv 2023年

作者： Hazra, Abhishek Morichetta, Andrea Murturi, Ilir Lovén, Lauri Dehury, Chinmaya Kumar Pujol, Víctor Casamayor Donta, Praveen Kumar Dustdar, Schahram Department of Computer Science and Engineering the Indian Institute of Information Technology Sri City India Communications & Networks Lab Department of Electrical and Computer Engineering National University of Singapore Singapore119260 Singapore Distributed Systems Group TU Wien Vienna1040 Austria Center for Ubiquitous Computing University of Oulu 90014 Finland Institute of Computer Science University of Tartu Tartu51009 Estonia

Zero-touch network is anticipated to inaugurate the generation of intelligent and highly flexible resource provisioning strategies where multiple service providers collaboratively offer computation and storage resources. This transformation presents substantial challenges to network administration and service providers regarding sustainability and scalability. This article combines Distributed Artificial Intelligence (DAI) with Zero-touch Provisioning (ZTP) for edge networks. This combination helps to manage network devices seamlessly and intelligently by minimizing human intervention. In addition, several advantages are also highlighted that come with incorporating Distributed AI into ZTP in the context of edge networks. Further, we draw potential research directions to foster novel studies in this field and overcome the current limitations. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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A Dual-Region Fiber-Optic SPR Biosensor with Self-Referencing Compensation of Bulk Refractive Index and Temperature Effects

A Dual-Region Fiber-Optic SPR Biosensor with Self-Referencin...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Valentina Bello Wouter Vandezande Devin Daems Jeroen Lammertyn Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Department of Biosystems MeBioS-Biosensor Group KU Leuven Leuven Belgium Department of Microbial and Molecular Systems Centre for Membrane Separations Adsorption Catalysis and Spectroscopy for Sustainable Solutions KU Leuven Leuven Belgium

ISBN: (纸本)9798350345995

In the last decades, surface plasmon resonance (SPR) has become one of the most advanced label-free, real-time detection technologies in the field of biosensing. In particular, fiber optic (FO)-SPR biosensors represent a cost-effective and easy-to-use alternative to expensive and complex prism-based SPR systems. [1]. However, when using FO-SPR biosensors, the need for self-referencing still represents a critical open problem, since the SPR signal can be affected also by external factors, such as changes in the bulk refractive index (RI) and, in particular, temperature fluctuations. To solve this issue, we have developed a dual-region self-referencing FO-SPR biosensor that provides a double-resonance SPR signal (Fig. 1(a)): the sensing region is designed to measure the binding events of the biomolecules of interest in the sample with the receptors on the surface of the sensor, while the reference region is used as a reference channel to take into account bulk RI changes and temperature oscillations. We have designed and tested two different configurations for the FO probe. The first one is composed of two regions coated with gold layers of different thickness (Fig. 1(b)). In the second configuration, the two regions are coated with gold layers of equal thickness, but the reference zone includes an extra PDMS layer with a high thermo-optic coefficient (Fig. 1(c)). First, the performance of the two configurations were theoretically studied and validated in sucrose–water solutions and in water at different temperatures, showing a response to RI and temperature changes with sensitivities up to 2000 nm/RIU and 1.559 nm/°C, respectively. Afterwards, the first configuration was validated with an aptamer-based bioassay for α-thrombin detection: only the sensing region was functionalized with aptamers. As shown in Fig. 1(d), the signal provided by the reference resonance was successfully used to eliminate the components of the sensing signal due to bulk-RI. On the other hand,

关键词： Biosensors Gold Polydimethylsiloxane Refractive index Sensors Surface plasmon resonance

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Introduction of hydrogen technologies to mitigate the impact of thermal consumption in a university campus

Introduction of hydrogen technologies to mitigate the impact...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Jorge Bracho Chrysanthos Charalambous Venizelos Efthimiou Dimitrios Lagos Tomasz Samotyjak Magnus Askeland George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Energy Systems Analytics Group Catalonia Institute for Energy Research (IREC) Barcelona Spain Instytut Energetyki Gdansk Poland Energy Systems SINTEF Energy Research Trondheim Norway

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

It is imperative for energy systems to reduce their environmental footprint in a cost-efficient manner, for which renwable energy sources (RES) and complementary technologies become desirable options to achieve said task. This is particularly the case for systems with different energy sources, as they’d be able to either address one specific type of energy demand and/or allow for the coupling of different energy carriers that would allow for technologies that normally don’t interact to start complementing each other. Therefore, this paper proposes various scenarios for the UCY campus in which hydrogen technologies would allow for the electrification of thermal demand while harnessing the surplus production coming from a solar park, which is modelled through the Integrate optimization tool and validated through DidSILENT PowerFactory. The results showcased a rise of up to 58% in self-sufficiency while the annual energy costs and CO2 emissions were reduced by 45% and 48% respectively.

关键词： Renewable energy sources Energy consumption Costs Hydrogen Fuel cells Production Optimization Carbon emissions Investment Resistance heating

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Speech Separation for Low-Resource Languages

Speech Separation for Low-Resource Languages

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Marvin Borsdorf Zexu Pan Pascal Himmelmann Haizhou Li Tanja Schultz Machine Listening Lab University of Bremen Bremen Germany Alibaba Group Singapore Department of Mathematics and Computer Science FernUniversität in Hagen Hagen Germany School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Cognitive Systems Lab University of Bremen Bremen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speech separation aims to equip machines with the human ability of selective listening, i.e. to focus attention on specific information in spoken communication. Studies have shown that the language spoken in a cocktail party scenario matters. While the development of speech separation models can leverage extensive databases, for the majority of languages only very limited data is available. This work presents the very first study on speech separation for low-resource languages. We choose blind source separation as the task to be studied and analyze three strategies to overcome the data scarcity of two low-resource languages from the GlobalPhoneMS2 database. We show that data from other languages can be used to develop models that work for low-resource languages. Finetuning additionally boosts the performance, and training on multiple languages increases both performance and robustness. We show that dynamic mixing in the development helps to find a trade-off between performance and development time.

关键词： Training Measurement Databases Signal processing Data models Blind source separation Robustness Acoustics Speech processing

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Cartpole Problem with PDL and GP Using Multi-objective Fitness Functions Differing in a Priori Knowledge 1

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14th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2021

作者： Shannon, Peter David Nehaniv, Chrystopher L. Phon-Amnuaisuk, Somnuk Adaptive Systems Research Group Centre for Computer Science & Informatics Research University of Hertfordshire Hatfield United Kingdom Laksmana College of Business Bandar Seri Begawan Brunei Department of Systems Design Engineering University of Waterloo WaterlooON Canada Department of Electrical & Computer Engineering University of Waterloo WaterlooON Canada School of Computing and Informatics Universiti of Teknologi Brunei Bandar Seri Begawan Brunei

ISBN: (数字)9783030802530

ISBN: (纸本)9783030802523

We present a study looking at the effect of a priori domain knowledge on an EA fitness function. Our experiment has two aims: (1) applying an existing NSGA-II framework for GP with PDL to the cartpole problem—applying GP & PDL to cartpole and a purely behavioral problem for the first time—and (2) contrasting two multi-objective fitness functions: one with high and the other with low a priori domain knowledge. In our experiment we created two populations with an EA, varying in the number of objectives use for the fitness function, 2 objective criteria to represent low a priori knowledge and 3 to represent high. With fitness functions tailored to find specifically prescribed solutions we expect greater discriminating power and more feedback to an evolutionary process. This comes at the cost of excluding some unexpected solutions from the evolutionary process and placing a greater burden on the designer. We address the question: how large is the disadvantage for the low a priori fitness function in a worst-case scenario, where innovative solutions will not enhance performance. This question is interesting because we would prefer to guide EA with simple, easy to create and understand, objective criteria rather than complex and highly specific criteria. Understanding any associated penalty for using simple, easy to create fitness functions, is crucial in assessing how much effort and should be put into designing objective criteria. © 2021, Springer Nature Switzerland AG.

关键词： Domain Knowledge

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deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques

arXiv

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arXiv 2022年

作者： Sa, Inkyu Lim, JongYoon Ahn, Ho Seok MacDonald, Bruce CSIRO Data61 Robotics and Autonomous Systems Group Robot Perception Team Switzerland CARES Department of Electrical Computer and Software Engineering University of Auckland New Zealand

This paper presents datasets utilised for synthetic near-infrared (NIR) image generation and bounding-box level fruit detection systems. A high-quality dataset is one of the essential building blocks that can lead to success in model generalisation and the deployment of data-driven deep neural networks. In particular, synthetic data generation tasks often require more training samples than other supervised approaches. Therefore, in this paper, we share the NIR+RGB datasets that are re-processed from two public datasets (i.e., nirscene and SEN12MS), expanded our previous study, deepFruits, and our novel NIR+RGB sweet pepper(capsicum) dataset. We oversampled from the original nirscene dataset at 10, 100, 200, and 400 ratios that yielded a total of 127 k pairs of images. From the SEN12MS satellite multispectral dataset, we selected Summer (45 k) and All seasons (180 k) subsets and applied a simple yet important conversion;digital number (DN) to pixel value conversion followed by image standardisation. Our sweet pepper dataset consists of 1615 pairs of NIR+RGB images that were collected from commercial farms. We quantitatively and qualitatively demonstrate that these NIR+RGB datasets are sufficient to be used for synthetic NIR image generation. We achieved Frechet Inception Distance (FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweet pepper datasets respectively. In addition, we release manual annotations of 11 fruit bounding boxes that can be exported as various formats using cloud service. Four newly added fruits [blueberry, cherry, kiwi, and wheat] compound 11 novel bounding box datasets on top of our previous work presented in the deepFruits project [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. The total number of bounding box instances of the dataset is 162 k and it is ready to use from cloud service. For the evaluation of the dataset, Yolov5 single stage detector is exploited and reported impressive mean-average-precision, mAP[0.

关键词： Generative adversarial networks

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PROV-IO+: A Cross-Platform Provenance Framework for Scientific Data on HPC systems

arXiv

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arXiv 2023年

作者： Han, Runzhou Zheng, Mai Byna, Suren Tang, Houjun Dong, Bin Dai, Dong Chen, Yong Kim, Dongkyun Hassoun, Joseph Thorsley, David Wolf, Matthew Department of Electrical and Computer Engineering Iowa State University United States Department of Computer Science and Engineering The Ohio State University United States Scientific Data Management Group Lawrence Berkeley National Laboratory United States Computer Science Department University of North Carolina Charlotte United States Systems Architecture Lab Samsung United States Samsung Advanced Institute of Technology Samsung Korea Republic of

Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing provenance solutions cannot address the challenges due to their incompatible provenance models and/or system implementations. In this paper, we analyze four representative scientific workflows in collaboration with the domain scientists to identify concrete provenance needs. Based on the first-hand analysis, we propose a provenance framework called PROV-IO+, which includes an I/O-centric provenance model for describing scientific data and the associated I/O operations and environments precisely. Moreover, we build a prototype of PROV-IO+ to enable end-to-end provenance support on real HPC systems with little manual effort. The PROV-IO+ framework can support both containerized and non-containerized workflows on different HPC platforms with flexibility in selecting various classes of provenance. Our experiments with realistic workflows show that PROV-IO+ can address the provenance needs of the domain scientists effectively with reasonable performance (e.g., less than 3.5% tracking overhead for most experiments). Moreover, PROV-IO+ outperforms a state-of-the-art system (i.e., ProvLake) in our experiments. © 2023, CC BY-NC-SA.

关键词： Life cycle

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