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arXiv 2022年

作者： Zhang, He Li, Sizhen Zhang, Liang Mathews, David H. Huang, Liang School of Electrical Engineering & Computer Science Oregon State University CorvallisOR97330 United States Baidu Research USA SunnyvaleCA94089 United States Dept. of Biochemistry & Biophysics United States Center for RNA Biology United States Dept. of Biostatistics & Computational Biology University of Rochester Medical Center RochesterNY14642 United States

Many ncRNAs function through RNA-RNA interactions. Fast and reliable RNA structure prediction with consideration of RNA-RNA interaction is useful. Some existing tools are less accurate due to omitting the competing of intermolecular and intramolecular base pairs, or focus more on predicting the binding region rather than predicting the complete secondary structure of two interacting strands. Vienna RNAcofold, which reduces the problem into the classical single sequence folding by concatenating two strands, scales in cubic time against the combined sequence length, and is slow for long sequences. To address these issues, we present LinearCoFold, which predicts the complete minimum free energy structure of two strands in linear runtime, and LinearCoPartition, which calculates the cofolding partition function and base pairing probabilities in linear runtime. LinearCoFold and LinearCoPartition follows the concatenation strategy of RNAcofold, but are orders of magnitude faster than RNAcofold. For example, on a sequence pair with combined length of 26,190 nt, LinearCoFold is 86.8× faster than RNAcofold MFE mode (0.6 minutes vs. 52.1 minutes), and LinearCoPartition is 642.3× faster than RNAcofold partition function mode (1.8 minutes vs. 1156.2 minutes). Different from the local algorithms, LinearCoFold and LinearCoPartition are global cofolding algorithms without restriction on base pair length. Surprisingly, LinearCoFold and LinearCoPartition’s predictions have higher PPV and sensitivity of intermolecular base pairs. Furthermore, we apply LinearCoFold to predict the RNA-RNA interaction between SARS-CoV-2 gRNA and human U4 snRNA, which has been experimentally studied, and observe that LinearCoFold’s prediction correlates better to the wet lab results. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Language-Assisted Deep Learning for Autistic Behaviors Recognition

arXiv

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arXiv 2022年

作者： Deng, Andong Yang, Taojiannan Chen, Chen Chen, Qian Neely, Leslie Oyama, Sakiko Center for Research in Computer Vision University of Central Florida OrlandoFL United States Dept. of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX United States Child and Adolescent Policy and Research Institute University of Texas at San Antonio San AntonioTX United States Dept. of Kinesiology University of Texas at San Antonio San AntonioTX United States

Correctly recognizing the behaviors of children with Autism Spectrum Disorder (ASD) is of vital importance for the diagnosis of Autism and timely early intervention. However, the observation and recording during the treatment from the parents of autistic children may not be accurate and objective. In such cases, automatic recognition systems based on computer vision and machine learning (in particular deep learning) technology can alleviate this issue to a large extent. Existing human action recognition models can now achieve impressive performance on challenging activity datasets, e.g., daily activity, and sports activity. However, problem behaviors in children with ASD are very different from these general activities, and recognizing these problem behaviors via computer vision is less studied. In this paper, we first evaluate a strong baseline for action recognition, i.e., Video Swin Transformer, on two autism behaviors datasets (SSBD and ESBD) and show that it can achieve high accuracy and outperform the previous methods by a large margin, demonstrating the feasibility of vision-based problem behaviors recognition. Moreover, we propose language-assisted training to further enhance the action recognition performance. Specifically, we develop a two-branch multimodal deep learning framework by incorporating the"freely available" language description for each type of problem behavior. Experimental results demonstrate that incorporating additional language supervision can bring an obvious performance boost for the autism problem behaviors recognition task as compared to using the video information only (i.e., 3.49% improvement on ESBD and 1.46% on SSBD). Project website: https://***/UCF-ASD. Copyright © 2022, The Authors. All rights reserved.

关键词： computer vision

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An Efficient Deep CNN Design for EH Short-Packet Communications in Multihop Cognitive IoT Networks

arXiv

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arXiv 2022年

作者： Nguyen, Toan-Van Huynh-The, Thien Nguyen, Van-Dinh da Costa, Daniel Benevides Hu, Rose Qingyang An, Beongku Dept. of Electrical and Computer Engineering Utah State University LoganUT United States ICT Convergence Research Center Kumoh National Institute of Technology Korea Republic of SnT-Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg AI & Telecom Research Center Technology Innovation Institute 9639 Masdar City Abu Dhabi United Arab Emirates Taiwan Dept. of Software and Communications Engineering Hongik University Korea Republic of

In this paper, we design an efficient deep convolutional neural network (CNN) to improve and predict the performance of energy harvesting (EH) short-packet communications in multi-hop cognitive Internet-of-Things (IoT) networks. Specifically, we propose a Sum-EH scheme that allows IoT nodes to harvest energy from either a power beacon or primary transmitters to improve not only packet transmissions but also energy harvesting capabilities. We then build a novel deep CNN framework with feature enhancement-collection blocks based on the proposed Sum-EH scheme to simultaneously estimate the block error rate (BLER) and throughput with high accuracy and low execution time. Simulation results show that the proposed CNN framework achieves almost exactly the BLER and throughput of Sum-EH one, while it considerably reduces computational complexity, suggesting a real-time setting for IoT systems under complex scenarios. Moreover, the designed CNN model achieves the root-mean-square-error (RMSE) of 1.33 × 10−2 on the considered dataset, which exhibits the lowest RMSE compared to the deep neural network and state-of-the-art machine learning approaches. © 2022, CC BY.

关键词： Internet of things

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Topological modes observed in Si photonics SSH integrated circuit

Topological modes observed in Si photonics SSH integrated ci...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO/PR 2022

作者： Nakamura, R. Nakama, T. Balčytis, A. Ozawa, T. Ota, Y. Iwamoto, S. Ito, H. Baba, T. Dept of Electrical & Computer Eng. Yokohama National Univ. 79-5 Tokiwadai Hodogaya-ku Yokohama240-8501 Japan School of Engineering RMIT University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku Univ. Sendai980-8577 Japan Dept. of Applied Physics and Physico-Informatics Keio Univ. 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan

We fabricated a sophisticated Si photonic integrated circuit for investigating topological photonics SSH model. In the selective excitation and observation of SSH coupled microrings, we observed the wavefunctions of e... 详细信息

关键词： Timing circuits

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Topological modes observed in Si photonics SSH integrated circuit

Topological modes observed in Si photonics SSH integrated ci...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Nakamura, R. Nakama, T. Balčytis, A. Ozawa, T. Ota, Y. Iwamoto, S. Ito, H. Baba, T. Dept of Electrical &Computer Eng. Yokohama National Univ. 79-5 Tokiwadai Hodogaya-ku Yokohama240-8501 Japan School of Engineering RMIT University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku Univ. Sendai980-8577 Japan Dept. of Applied Physics and Physico-Informatics Keio Univ. 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan

ISBN: (纸本)9798350350012

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Induction Motor Eccentricity Fault Analysis and Quantification with Modified Winding Function based Model

Induction Motor Eccentricity Fault Analysis and Quantificati...

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International Conference on electrical Machines and Systems (ICEMS)

作者： Bingnan Wang Mesaad W. Albader Hiroshi Inoue Makoto Kanemaru Mitsubishi Electric Research Laboratories 201 Broadway Cambridge MA USA Dept. of Electrical & Computer Engineering Texas A&M University College Station TX USA Advanced Technology R&D Center Mitsubishi Electric Corporation Amagasaki Hyogo Japan

ISBN: (数字)9781665493024

ISBN: (纸本)9781665493031

In this paper, we build a fast simulation model for the analysis of eccentric induction motor. The self and mutual inductances of the motor windings are calculated with modified winding function method, with slotting effect and core saturation considered for improved accuracy. The calculated inductances are then fed into a coupled circuit model to simulate the dynamics of an eccentric induction motor. Compared with time-stepping finite-element method, the model is much faster for machines under eccentric faulty conditions. The accuracy of the model is validated with experiment measurements, with good match for fault component amplitudes.

关键词： Analytical models Induction motors Atmospheric modeling Windings Stator windings Modulation Maintenance engineering

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Comparison of optimal power flow formulations in active distribution grids 12

Comparison of optimal power flow formulations in active dist...

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12th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2020

作者： Mylonas, Costas Karagiannopoulos, Stavros Aristidou, Petros Shchetinin, Dmitry Hug, Gabriela Power Systems Laboratory ETH Zurich Zurich8092 Switzerland Dept. of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol3036 Cyprus ABB Corporate Research Center Baden5405 Switzerland

ISBN: (纸本)9781839535635

Active Distribution Networks (DNs) are expected to host an increasing number of Distributed Generators (DGs) and other Distributed Energy Resources (DERs), offering new flexible sources and enabling the provision of ancillary services to system operators. Centralized DER controls that use Optimal Power Flow (OPF) methods necessitate tractable and scalable computational tools that can handle large DNs with satisfactory performance. In this paper, we compare an iterative OPF method against the standard exact AC OPF calculations in terms of the computational effort and solution quality. Furthermore, we highlight the suitability of the selected formulation to offer voltage support (VS) as an ancillary service to the transmission network. The results are demonstrated using a combination of a medium and low voltage grid, and show that tractable OPF formulations can unlock financial business cases for DNs that can actively participate in VS schemes. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Acoustic generators

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Techno-Economic Evaluation Software for Optimal Business Model and Sizing of Photovoltaic Power Station-Linked Energy Storage System

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Transactions of the Korean Institute of electrical Engineers 2020年第9期69卷 1338-1348页

作者： Cho, Kyeong-Hee Kim, Seul-Ki Jo, Hyung-Chul Son, Wanbin Kim, Eung-Sang Park, June Ho Dept. of Electrical and Computer Engineering Pusan National University Korea Republic of Dept. of Electrical and Computer Engineering Pusan National University Korea Republic of Smart Grid Research Center Korea Electrotechnology Research Institute Korea Republic of

Under the Korea government’s Energy Transition Roadmap, energy transition is underway with the gradual reduction of nuclear power plants and the expansion of renewable energy. Moreover, in the Third Energy Master Plan, the target share of renewable energy by 2040 was increased to 30-35%. The increase in the capacity of small-and medium-sized photovoltaic power stations (PV) is the highest among different renewable energy sources. As the output of PV power stations varies considerably, linking them with energy storage systems(ESSs)is attracting attention. When designing a PV-ESS, as the investment costs are high and the applicable support policies vary with the purpose of operation, it is important to review the economic feasibility of various models before installation. However, conducting this review might be very complex and difficult for the operator. Accordingly, the present study introduces a design program for distributed resources (MODDER) that can select the optimal capacity and business model considering the domestic policies and the operating characteristics of the distributed resources. The program comprises a database of weather information, load patterns according to application, sales unit costs, electricity rate unit costs, and renewable energy system specifications and can conveniently analyze the economic feasibility of a PV-ESS by selecting and configuring it according to the user’s purpose. Accordingly, the program helps determine the optimal capacity and business model. To verify the effectiveness of the proposed program, through a case study, simulations of a demand side management business and a renewable energy generation sale business were conducted based on actual industrial customers, and the results were analyzed. The developed program can be used to maximize the return on investment of renewable power generation and ESSs, thereby reducing investment risk and contributing to improving the acceptance of renewable energy. Copyright © The Kor

关键词： Renewable energy resources

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Wireless ATMEL AVR In-Circuit Serial Programmer based on Wi-Fi and ZigBee 16

Wireless ATMEL AVR In-Circuit Serial Programmer based on Wi-...

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16th International computer engineering Conference, ICENCO 2020

作者： Ahmed, Ahmed Sharf, Samy Badawy, Wael Madian, Ahmed Abdelhamied, Rafaat Mekky, Mostafa Abdelhamied, Mohamed School of Information Technology and Computer Science Nile University Giza Egypt Badr University in Cairo Electrical Engineering Dept. Cairo Egypt Nisc Research Center Nile University Giza Egypt Fares Pcb for Electronic Solutions Electronic Design Engineering Dept. Cairo Egypt Fares Pcb for Electronic Solutions Electronic Manufacturing Engineering Dept. Cairo Egypt

ISBN: (纸本)9781728163925

An ATMEL AVR ICSP programmer is considered an important device for who uses ATMEL AVR microcontrollers. Legacy programmer devices require wired connection to a PC via USB cable. This technique forces a user to place the target microcontroller close enough to a PC. Modernized systems and applications require wireless communications via many technologies, such as Wi-Fi and ZigBee. Systems that are inaccessible or installed in dangerous zones require a remote communication technique for firmware upgrading. Unfortunately, the current devices do not support remote firmware upgrading. Therefore, it resembles a critical problem for many factories. In this research paper, we will introduce a new device for programming all ATMEL AVR microcontrollers remotely via Wi-Fi and ZigBee. Also, we will provide two experimental works to prove our device's new architecture concept alongside with deploying it in real life use cases, such as Massive Deployment. © 2020 IEEE.

关键词： Zigbee

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Live multi-streaming and donation recommendations via coupled donation-response tensor factorization

arXiv

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arXiv 2021年

作者： Lai, Hsu-Chao Huang, Jiun-Long Tsai, Jui-Yi Lee, Wang-Chien Shuai, Hong-Han Yang, De-Nian Dept. of Computer Science National Chiao Tung Univ. Inst. of Information Science Academia Sinica Taiwan Dept. of Computer Science National Chiao Tung Univ. Taiwan Dept. of Electrical and Computer Engineering National Chiao Tung Univ. Inst. of Information Science Academia Sinica Taiwan Dept. of Computer Science and Engineering Pennsylvania State Univ. United States Dept. of Electrical and Computer Engineering National Chiao Tung Univ. Taiwan Inst. of Information Science Research Center Information Technology Innovation Academia Sinica Taiwan

In contrast to traditional online videos, live multi-streaming supports real-time social interactions between multiple streamers and viewers, such as donations. However, donation and multi-streaming channel recommendations are challenging due to complicated streamer and viewer relations, asymmetric communications, and the tradeoff between personal interests and group interactions. In this paper, we introduce Multi-Stream Party (MSP) and formulate a new multi-streaming recommendation problem, called Donation and MSP Recommendation (DAMRec). We propose Multi-stream Party Recommender System (MARS) to extract latent features via socio-temporal coupled donation-response tensor factorization for donation and MSP recommendations. Experimental results on Twitch and Douyu manifest that MARS significantly outperforms existing recommenders by at least 38.8% in terms of hit ratio and mean average precision. Copyright © 2021, The Authors. All rights reserved.

关键词： Factorization

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