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SSRN 2023年

作者： Ghadimi, Laleh Saleh Tabrizi, Amin Goljanian Rezaie, Ehsan Parvini, Elahe Tavakoli, Mahmoud Abouzari-Lotf, Ebrahim Hajalilou, Abdollah Arsalani, Nasser Pahlevani, Majid Department of Electrical and Computer Engineering Queen's University KingstonON Canada Research Laboratory of Polymer Department of Organic and Biochemistry Faculty of Chemistry University of Tabriz Iran Laboratory for Advanced Materials Faculty of Natural Sciences Comenius University Bratislava842 15 Slovakia Soft and Printed Microelectronics Laboratory Institute of Systems and Robotics University of Coimbra Coimbra Portugal Electrochemical Energy Storage Helmholtzstraße 11 UlmD-89081 Germany

Our research aimed to enhance carbon-based electrochemical double-layer capacitors (EDLCs) by incorporating faradic redox processes. We synthesized a Ni0.5Zn0.5Fe2O4-Carbon Nanotube (NZF-CNT) nanocomposite via hydrothermal methods at various pH levels. X-ray diffraction (XRD) analysis at pH 12 confirmed the formation of spinel nickel-zinc ferrite with 15 nm particles. X-ray photoelectron spectroscopy (XPS) verified cation distribution changes, and high-resolution transmission electron microscopy (HRTEM) revealed well-arranged NZF crystals around CNTs. The successful fusion of NZF and CNTs into the nanocomposite framework was demonstrated. NZF-CNT exhibited a remarkable specific capacitance of approximately 368 F/g at 1.25 A/g, surpassing pure ferrite's 110 F/g. Even after 5000 charge/discharge cycles at 1 A/g, it retained 88.5% of its initial capacitance. Our findings showcase the potential of NZF-CNT as a promising material for energy storage devices, offering a unique combination of EDLC and pseudo-capacitive properties. This approach opens new avenues for improving supercapacitor performance and their diverse applications. © 2023, The Authors. All rights reserved.

关键词： Supercapacitor

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Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification

Class-Aware Patch Embedding Adaptation for Few-Shot Image Cl...

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International Conference on computer Vision (ICCV)

作者： Fusheng Hao Fengxiang He Liu Liu Fuxiang Wu Dacheng Tao Jun Cheng Shenzhen Institute of Advanced Technology Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences China The Chinese University of Hong Kong Hong Kong China AIAI School of Informatics University of Edinburgh United Kingdom School of Computer Science Faculty of Engineering The University of Sydney Australia

"A picture is worth a thousand words", significantly beyond mere a categorization. Accompanied by that, many patches of the image could have completely irrelevant meanings with the categorization if they were independently observed. This could significantly reduce the efficiency of a large family of few-shot learning algorithms, which have limited data and highly rely on the comparison of image patches. To address this issue, we propose a Class-aware Patch Embedding Adaptation (CPEA) method to learn "class-aware embeddings" of the image patches. The key idea of CPEA is to integrate patch embeddings with class-aware embeddings to make them class-relevant. Furthermore, we define a dense score matrix between class-relevant patch embeddings across images, based on which the degree of similarity between paired images is quantified. Visualization results show that CPEA concentrates patch embeddings by class, thus making them class-relevant. Extensive experiments on four benchmark datasets, miniImageNet, tieredImageNet, CIFAR-FS, and FC-100, indicate that our CPEA significantly outperforms the existing state-of-the-art methods. The source code is available at https://***/FushengHao/CPEA.

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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Design and Analysis of the Comb Gripper of the Saffron Flowers Harvesting Robot

Design and Analysis of the Comb Gripper of the Saffron Flowe...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Shima Shademani Shahriyar Kouravand Payam Zarafshan Khalil Aalipour Amir Ghalamzan Esfahani Department of Agro-Technology College of Aburaihan University of Tehran Teltran Iran Department of Mechatronics Engineering AGRicultural INtelligent Systems (AGRINS) Faculty of New Sciences and Technologies University of Tehran Teltran Iran Department of Mechatronics Engineering Advanced Service Robots (ASR) Laboratory Faculty of New Sciences and Technologies University of Tehran Teltran Iran School of Computer Science University of Lincoln United Kingdom

Saffron is one of the most valuable spices in the world, whose origin is attributed to Iran. Iran produces about 90% of the world's saffron annually, however, the harvesting of this product in Iran is mostly done manually. Manual harvest of saffron brings problems. Decreasing the quality of saffron and contamination of saffron with microorganisms are among the problems that arise during manual harvesting. For this purpose, a gripper is recommended for harvesting saffron flowers. One of the special features of the saffron plant is that several flowers may come out from each corm at different time intervals, for this reason, the proposed gripper uses the plucking method to harvest the flowers and separate flowers from the stem so that the next flowers can grow easily. Design, simulation, and control of the gripper have been conducted using conventional software such as SOLIDWORKS, ADAMS, and MATLAB.

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An 800G Integrated Silicon-Photonic Transmitter based on 16-Channel Mach-Zehnder Modulator and Co-Designed 5.35pJ/bit CMOS Drivers

An 800G Integrated Silicon-Photonic Transmitter based on 16-...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Jingbo Shi Han Liu Tao Yang Ming Jin Haowen Shu Fenghe Yang Lei Shi Yuansheng Tao Jianrui Deng Ruixuan Chen Changhao Han Jian Liu Nanjian Wu Nan Qi Liyuan Liu State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Chinese Academy of Sciences Institute of Semiconductors Beijing China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Zhangjiang Laboratories Shanghai China College of Computer Science and Engineering Southwest Minzu University Chengdu Sichuan China

A 800G integrated silicon-photonic transmitter is presented, including a 16-channel photonic integrated chip (PIC) and two electrical chiplets (EICs) that are realized based on an arrayed travelling wave dual-drive Mach-Zehnder modulator (MZM) and two 8-channel CMOS drivers. The proposed multi-channel PIC is fabricated on a high-resistance silicon-on-insulator (SOI) wafer with a 220 nm thick silicon layer and a $\mathbf{2}\ \boldsymbol{\mu} \mathbf{m}$ thick buried oxide (BOX) using the foundry-ready CMOS process, while the drivers are implemented in a standard $\mathbf{65}\mathbf{nm}$ CMOS process. The driver employs a combination of distributed architecture, 2-tap feedforward equalization (FFE) and push-pull output stage, experimentally exhibiting an averaged bandwidth higher than 28.5GHz and a differential swing of 4.0Vpp on $\mathbf{50}\mathbf{\Omega}$ load, respectively. The 50Gb/s electrical eye-diagram is measured with 1.41ps rms-jitter, while the optical extinction ratio (ER) exceeds 3.0dB with 5.35pJ/bit power efficiency.

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A Subspace-Based Non-Dominated Subset Selection Method

A Subspace-Based Non-Dominated Subset Selection Method

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Congress on Evolutionary Computation

作者： Qingshan Tan Changhe Li Sanyou Zeng Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Environmental selection is an important process in multi-objective evolutionary algorithms (MOEAs). As the evolution progresses, the number of non-dominated solutions increases. This paper is focused on selecting a subset from excess non-dominated solutions for evolutionary or the final output. However, traditional selection methods in classical MOEAs encounter difficulties when dealing with candidate solutions that possess irregular topologies. Although the distance-based subset selection methods are not sensitive to the topologies of the candidate points, they have significant room for reducing computational complexity. In order to address the above issues, a subspace selection method is proposed in this paper. It partitions the objective space into multiple subspaces that have comparable volumes and shapes. The maximal minimum distance of each solution is considered to ensure that the sparsest solution is always chosen first. To save computational costs, only the solutions in the neighboring subspaces are taken into account. The experimental results demonstrate that the proposed subspace selection method outperforms classical selection methods in solving problems with various shapes of the Pareto front.

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Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Generation

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Chenyu Yan, Shuo Chen, Yixuan Wang, Xianwei Wang, Yujiang Dong, Mingzhi Yang, Xiaochen Li, Dongsheng Zhu, Rui Clifton, David A. Dick, Robert P. Lv, Qin Yang, Fan Lu, Tun Gu, Ning Shang, Li Fudan University School of Computer Science Shanghai China Fudan University Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Oxford Suzhou Centre for Advanced Research Suzhou China University of Bath Bath United Kingdom University of Glasgow School of Mathematics and Statistics United Kingdom Microsoft Research Asia Shanghai China University of London Bayes Business School City United Kingdom University of Oxford Department of Engineering Science Oxford United Kingdom University of Michigan Department of Electrical Engineering and Computer Science College of Engineering Ann ArborMI United States University of Colorado Boulder BoulderCO United States

Denoising-based diffusion models have attained impressive image synthesis;however, their applications on videos can lead to unaffordable computational costs due to the per-frame denoising operations. In pursuit of efficient video generation, we present a Diffusion Reuse MOtion (Dr. Mo) network to accelerate the video-based denoising process. Our crucial observation is that the latent representations in early denoising steps between adjacent video frames exhibit high consistencies with motion clues. Inspired by the discovery, we propose to accelerate the video denoising process by incorporating lightweight, learnable motion features. Specifically, Dr. Mo will only compute all denoising steps for base frames. For a non-based frame, Dr. Mo will propagate the pre-computed based latents of a particular step with interframe motions to obtain a fast estimation of its coarse-grained latent representation, from which the denoising will continue to obtain more sensitive and fine-grained representations. On top of this, Dr. Mo employs a meta-network named Denoising Step Selector (DSS) to dynamically determine the step to perform motion-based propagations for each frame, ensuring the correct transformation of multi-granularity visual features. Extensive evaluations on video generation and editing tasks indicate that Dr. Mo delivers widely applicable acceleration for diffusion-based video generations while effectively retaining the visual quality and style. Video generation and visualization results can be found at https://***. © 1991-2012 IEEE.

关键词： HTTP

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Navigation-GPT: A Robust and Adaptive Framework Utilizing Large Language Models for Navigation Applications

arXiv

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arXiv 2025年

作者： Ma, Feng Wang, Xiu-Min Chen, Chen Xu, Xiao-Bin Yan, Xin-Ping Wuhan University of Technology Wuhan China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan China School of Computer Science & Engineering Wuhan Institute of Technology Wuhan China China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing Hangzhou Dianzi University Hangzhou China Nanjing Smart Water Transport Technology Co. Ltd. Nanjing China

Existing navigation decision support systems often perform poorly when handling non-predefined navigation scenarios. Leveraging the generalization capabilities of large language model (LLM) in handling unknown scenarios, this research proposes a dual-core framework for LLM applications to address this issue. Firstly, through ReAct-based prompt engineering, a larger LLM core decomposes intricate navigation tasks into manageable sub-tasks, which autonomously invoke corresponding external tools to gather relevant information, using this feedback to mitigate the risk of LLM hallucinations. Subsequently, a fine-tuned and compact LLM core, acting like a first-mate is designed to process such information and unstructured external data, then to generates context-aware recommendations, ultimately delivering lookout insights and navigation hints that adhere to the International Regulations for Preventing Collisions at Sea (COLREGs) and other rules. Extensive experiments demonstrate the proposed framework not only excels in traditional ship collision avoidance tasks but also adapts effectively to unstructured, non-predefined, and unpredictable scenarios. A comparative analysis with DeepSeek-R1, GPT-4o and other SOTA models highlights the efficacy and rationality of the proposed framework. This research bridges the gap between conventional navigation systems and LLMs, offering a framework to enhance safety and operational efficiency across diverse navigation applications. © 2025, CC BY-SA.

关键词： Ships

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Evaluation of Trade-Off Between Compression Ratio and Hardware Cost for Adaptive Bandwidth Compression Hardware Platform

Evaluation of Trade-Off Between Compression Ratio and Hardwa...

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IEEE Cool Chips

作者： Tomohiro Ueno Kaito Kitazume Masato Kiyama Kazutomo Yoshii Kento Sato Norihisa Fujita Ryohei Kobayashi Taisuke Boku Kentaro Sano Center for Computational Science Riken Kobe Japan Degree Programs in Systems and Information Engineering University of Tsukuba Tsukuba Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan Mathematics and Computer Science Argonne National Laboratory Lemont IL USA Center for Computational Sciences University of Tsukuba Tsukuba Japan Supercomputing Research Center Institute of Integrated Research Institute of Science Tokyo Kanagawa Japan

ISBN: (数字)9798331534844

ISBN: (纸本)9798331534851

Hardware-based, high-throughput data compression is a promising approach to reduce data movement costs in largescale systems and networks, thereby improving overall performance and power efficiency. However, since a data compression algorithm is only effective if it is suitable for the characteristics of the target data, data compression hardware with a fixed algorithm is unrealistic for use in general-purpose environments. To address this challenge, we have been researching adaptive bandwidth compression (ABC) hardware that flexibly provides an effective algorithm depending on the input data. This paper presents the design and the Chisel-based implementation of the ABC hardware platform for encoding compressed data and generating output blocks, as well as introducing a quantization parameter to reduce the circuit area. Our evaluation shows that the proposed quantization parameter can not only reduce the hardware cost, but also control the trade-off between the effective compression ratio and the hardware cost. In addition, based on the evaluation results, we discuss the design optimization of the ABC hardware platform.

关键词： Performance evaluation Costs Quantization (signal) Data compression Bandwidth Hardware Space exploration Resource management Compression algorithms Field programmable gate arrays

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Crossbreeding in random forest

arXiv

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arXiv 2021年

作者： Nadi, Abolfazl Moradi, Hadi Taheri, Khalil Advanced Robotics and Intelligent Systems Laboratory School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran11155-4563 Iran SKKU Suwon16419 Korea Republic of

Ensemble learning methods are designed to benefit from multiple learning algorithms for better predictive performance. The tradeoff of this improved performance is slower speed and larger size of ensemble learning systems compared to single learning systems. In this paper, we present a novel approach to deal with this problem in Random Forest (RF) as one of the most powerful ensemble methods. The method is based on crossbreeding of the best tree branches to increase the performance of RF in space and speed while keeping the performance in the classification measures. The proposed approach has been tested on a group of synthetic and real datasets and compared to the standard RF approach. Several evaluations have been conducted to determine the effects of the Crossbred RF (CRF) on the accuracy and the number of trees in a forest. The results show better performance of CRF compared to RF. © 2021, CC BY-NC-ND.

关键词： Learning systems

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