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Optimal Energy Storage Operation under Demand Uncertainty: A Prospect Theory Analysis

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Optimal Energy Storage Operation under Demand Uncertainty: A...

IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Qisheng Huang Jin Xu Peng Sun Bo Liu Costas Courcoubetis School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China School of Management Huazhong University of Science and Technology Wuhan China College of Computer Science and Electronic Engineering Hunan University Changsha China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

In this paper, we study the consumer’s optimal energy storage operation problem under demand uncertainty. Each consumer can purchase energy storage service from an independent energy storage aggregator to shift demand from peak periods to off-peak periods under time-of-use (ToU) pricing. Previous studies on energy storage operation and investment mainly focused on the expected cost-minimization problem of a risk-neutral consumer based on the expected utility theory (EUT). We propose a prospect theory (PT) model from behavioral economics to understand the consumer’s realistic energy storage operation behaviors considering their risk preferences. Although it is challenging to solve the PT-based non-convex optimization problem, we are able to characterize the optimal solution by exploiting the unimodal structure of the objective function. Theoretical and numerical results show that the consumer’s risk preferences have a significant impact on his decisions: 1) a PT-consumer with a low reference point is more willing to use energy storage to reduce risk compared with the EUT benchmark; and 2) a PT consumer is more willing to use the energy storage when the probability of high demand is small, due to the probability distortion.

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Jigsaw-based Benchmarking for Learning Robotic Manipulation

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arXiv 2023年

作者： Liu, Xiaobo Wan, Fang Ge, Sheng Wang, Haokun Sun, Haoran Song, Chaoyang Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China School of Design Southern University of Science and Technology Guangdong Shenzhen518055 China Robotics & Autonomous Systems Thrust System Hub Hong Kong University of Science and Technology Hong Kong Department of Computer Science The University of Hong Kong Hong Kong

Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers can selectively adopt the proposed protocol to benchmark their research outputs, on a comparable scale in the functional, task, and system-level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence. © 2023, CC BY.

关键词： Benchmarking

Algorithm to technology Co-Optimization for CiM-Based Hyperdimensional Computing

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Algorithm to Technology Co-Optimization for CiM-Based Hyperd...

Design, Automation and Test in Europe Conference and Exhibition

作者： Mahta Mayahinia Simon Thomann Paul R. Genssler Christopher Münch Hussam Amrouch Mehdi B. Tahoori Department of Computer Science and Informatics Karlsruhe Institute of Technology Karlsruhe Germany Chair of AI Processor Design TUM School of Computation Information and Technology Technical University of Munich Munich Institute of Robotics and Machine Intelligence Munich Germany Semiconductor Test and Reliability University of Stuttgart Stuttgart Germany

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Hyperdimensional computing (HDC) has been recognized as an efficient machine learning algorithm in recent years. Robustness against noise and simple computational operations, while being limited by the memory bandwidth, make it a perfect fit for the concept of computation in memory (CiM) with emerging nonvolatile memory (NVM) technologies. For an HDC accelerator based on NVM-CiM, there are different parameters from the algorithm all the way down to the technology that interact with each other and affect the overall inference accuracy as well as the energy efficiency of the accelerator. Therefore, in this paper, we propose, for the first time, a full-stack co-optimization method and use it to design an HDC accelerator based on NVM-based content addressable memory (CAM). By incorporating the device manufacturing variability and co-optimizing the algorithm and hardware design, HDC inference on our proposed NVM-based CiM accelerator can reduce the energy consumption by 3.27x, while compared to the purely software-based implementation, the inference accuracy loss is merely 0.125%.

关键词： Associative memory Energy consumption Machine learning algorithms Nonvolatile memory Noise Inference algorithms Hardware

OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning

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arXiv 2024年

作者： Hong, Lingyi Yan, Shilin Zhang, Renrui Li, Wanyun Zhou, Xinyu Guo, Pinxue Jiang, Kaixun Chen, Yiting Li, Jinglun Chen, Zhaoyu Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China The Chinese University of Hong Kong Hong Kong Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China

Visual object tracking aims to localize the target object of each frame based on its initial appearance in the first frame. Depending on the input modility, tracking tasks can be divided into RGB tracking and RGB+X (e.g. RGB+N, and RGB+D) tracking. Despite the different input modalities, the core aspect of tracking is the temporal matching. Based on this common ground, we present a general framework to unify various tracking tasks, termed as OneTracker. OneTracker first performs a large-scale pre-training on a RGB tracker called Foundation Tracker. This pretraining phase equips the Foundation Tracker with a stable ability to estimate the location of the target object. Then we regard other modality information as prompt and build Prompt Tracker upon Foundation Tracker. Through freezing the Foundation Tracker and only adjusting some additional trainable parameters, Prompt Tracker inhibits the strong localization ability from Foundation Tracker and achieves parameter-efficient finetuning on downstream RGB+X tracking tasks. To evaluate the effectiveness of our general framework OneTracker, which is consisted of Foundation Tracker and Prompt Tracker, we conduct extensive experiments on 6 popular tracking tasks across 11 benchmarks and our OneTracker outperforms other models and achieves state-ofthe-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Foundations

OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning

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OneTracker: Unifying Visual Object Tracking with Foundation ...

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Lingyi Hong Shilin Yan Renrui Zhang Wanyun Li Xinyu Zhou Pinxue Guo Kaixun Jiang Yiting Chen Jinglun Li Zhaoyu Chen Wenqiang Zhang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China CUHK MMLab Shanghai Artificial Intelligence Laboratory Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Visual object tracking aims to localize the target object of each frame based on its initial appearance in the first frame. Depending on the input modility, tracking tasks can be divided into RGB tracking and RGB+X (e.g. RGB+N, and RGB+D) tracking. Despite the different input modalities, the core aspect of tracking is the temporal matching. Based on this common ground, we present a general framework to unify various tracking tasks, termed as One Tracker. One- Tracker first performs a large-scale pre-training on a RGB tracker called Foundation Tracker. This pretraining phase equips the Foundation Tracker with a stable ability to estimate the location of the target object. Then we regard other modality information as prompt and build Prompt Tracker upon Foundation Tracker. Through freezing the Foundation Tracker and only adjusting some additional trainable parameters, Prompt Tracker inhibits the strong localization ability from Foundation Tracker and achieves parameter- efficient finetuning on downstream RGB+X tracking tasks. To evaluate the effectiveness of our general framework OneTracker, which is consisted of Foundation Tracker and Prompt Tracker, we conduct extensive experiments on 6 popular tracking tasks across 11 benchmarks and our One- Tracker outperforms other models and achieves state-of-the-art performance.

关键词： Location awareness Visualization computer vision Adaptation models Target tracking Benchmark testing Pattern recognition

Double-Dot Network for antipodal grasp detection

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arXiv 2021年

作者： Wang, Yao Zheng, Yangtao Gao, Boyang Di Huang The State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China The Geometry Robotics

This paper proposes a new deep learning approach to antipodal grasp detection, named Double-Dot Network (DD-Net). It follows the recent anchor-free object detection framework, which does not depend on empirically pre-set anchors and thus allows more generalized and flexible prediction on unseen objects. Specifically, unlike the widely used 5-dimensional rectangle, the gripper configuration is defined as a pair of fingertips. An effective CNN architecture is introduced to localize such fingertips, and with the help of auxiliary centers for refinement, it accurately and robustly infers grasp candidates. Additionally, we design a specialized loss function to measure the quality of grasps, and in contrast to the IoU scores of bounding boxes adopted in object detection, it is more consistent to the grasp detection task. Both the simulation and robotic experiments are executed and state of the art accuracies are achieved, showing that DD-Net is superior to the counterparts in handling unseen objects. © 2021, CC BY.

关键词： Object recognition

ExpoNAS: Using Exposure-based Candidate Exclusion to Reduce NAS Space for Heterogeneous Pipeline of CNNs 29

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ExpoNAS: Using Exposure-based Candidate Exclusion to Reduce ...

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Duan, Huijuan Wang, Danghui Zhao, Xingting Mei, Kuizhi Wang, Jihe Polytechnical University School of Computer Science Northwestern Xi'an China Northwestern Polytechnical University Engineering Research Center of Embedded System Integration Ministry of Education Xi'an China Xi'An Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an China Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9798350330717

Efficiently deploying neural network models on heterogeneous edge systems faces unique challenges. Two major issues are underutilized pipelines caused by hardware heterogeneity and explosive growth of architecture search space. To address these challenges, we propose ExpoNAS, a framework enabling fast deployment-oriented neural architecture search on heterogeneous platforms. ExpoNAS evaluates model partitioning and deployability before search to meet accuracy and latency constraints. However, repeated partitioning leads to exponential growth in architecture search space. We first observe a strong correlation between operational exposure and accuracy improvement, and then we propose an exposure-based reduction technique that excludes the operations of low exposure. By removing these non-critical operations, the exposure-based pruning successfully reduces architecture search time. Our experiments on popular datasets show that the ExpoNAS pipelined design improves execution efficiency by a factor of 2 compared to traditional batch processing. Without compromising accuracy, ExpoNAS is able to reduce the architecture search time by 47.5% for discovering high-accuracy networks, compared to the SOTA NAS strategies. © 2023 IEEE.

关键词： heterogeneous systems NAS pipeline mapping search space reduction

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion

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arXiv 2024年

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024, CC BY.

关键词： Color image processing

Accurate Prior-centric Monocular Positioning with Offline LiDAR Fusion

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Accurate Prior-centric Monocular Positioning with Offline Li...

IEEE International Conference on robotics and Automation (ICRA)

作者： Jinhao He Huaiyang Huang Shuyang Zhang Jianhao Jiao Chengju Liu Ming Liu Thrust of Robotics and Autonomous Systems The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Baidu Autonomous Driving Technology Department (ADT) Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Computer Science University College London London UK School of Electronics and Information Engineering Tongji University Shanghai China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Unmanned vehicles usually rely on Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) sensors to achieve high-precision localization results for navigation purpose. However, this combination with their associated costs and infrastructure demands, poses challenges for widespread adoption in mass-market applications. In this paper, we aim to use only a monocular camera to achieve comparable onboard localization performance by tracking deep-learning visual features on a LiDAR-enhanced visual prior map. Experiments show that the proposed algorithm can provide centimeter-level global positioning results with scale, which is effortlessly integrated and favorable for low-cost robot system deployment in real-world applications.

关键词： Location awareness Visualization Laser radar Robot vision systems Cameras Sensor systems Robustness