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A 28-nm 10.4-fJ/b Cryogenic embedded DRAM with 3T1C Gain Cell and MBIST at 4-Kelvin 21

A 28-nm 10.4-fJ/b Cryogenic embedded DRAM with 3T1C Gain Cel...

引用

21st International System-on-Chip Design Conference, ISOCC 2024

作者： Zheng, Jiapei Nie, Xinkai Zhi, Zhenghang Tang, Zhidong Liu, Qi Kou, Xufeng Chen, Chixiao Fudan University State Key Lab of Integrated Chips and Systems Frontier Institute of Chip and System Shanghai China ShanghaiTech University School of Information Science and Technology Shanghai China

ISBN: (纸本)9798350377088

Quantum error correction is essential for achieving large-scale quantum computing. Large-scale quantum state detection requires low-power embedded memory that can operate at 4K. Embedded Dynamic Random Access Memory is a strong candidate for cryogenic memory, offering high density and low power consumption. In this paper, we propose a cryogenic eDRAM using a 28nm CMOS process, which utilizes a 3T1C gain cell structure. Considering the short lifespan of the readout data, the refresh function of eDRAM is eliminated. Additionally, it integrates Memory Built-In Self-Test circuits for various testing purposes. We measured the performance of the eDRAM at 4K and achieved a read power of 4.9fJ/b and a write power of 15.9fJ/b. The median retention time reached 4.8ms, and the bandwidth reached 61.4Gb/s. © 2024 IEEE.

关键词： Quantum computers

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Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs

Context-Driven Index Trimming: A Data Quality Perspective to...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Ma, Kexin Jin, Ruochun Wang, Haotian Wang, Xi Chen, Huan Tang, Yuhua Wang, Qian Institute for Quantum Information State Key Laboratory of High Performance Computing China College of Computer Science and Technology National University of Defense Technology Changsha China Intelligent Game and Decision Lab Academy of Military Science Beijing China

ISBN: (纸本)9798891761681

Retrieval-Augmented Large Language Models (RALMs) have made significant strides in enhancing the accuracy of generated responses. However, existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods. We propose to boost the precision of RALMs' answers from a data quality perspective through the Context-Driven Index Trimming (CDIT) framework, where Context Matching Dependencies (CMDs) are employed as logical data quality rules to capture and regulate the consistency between retrieved contexts. Based on the semantic comprehension capabilities of Large Language Models (LLMs), CDIT can effectively identify and discard retrieval results that are inconsistent with the query context and further modify indexes in the database, thereby improving answer quality. Experiments demonstrate average improvement of 3.75% in accuracy on challenging question-answering tasks. Also, the flexibility of CDIT is verified through its compatibility with various language models and indexing methods, which offers a promising approach to bolster RALMs' data quality and retrieval precision jointly. © 2024 Association for Computational Linguistics.

关键词： Data accuracy

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Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis 37

Robust-MSA: Understanding the Impact of Modality Noise on Mu...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Mao, Huisheng Zhang, Baozheng Xu, Hua Yuan, Ziqi Liu, Yihe State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China Beijing100084 China School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China

ISBN: (纸本)9781577358800

Improving model robustness against potential modality noise, as an essential step for adapting multimodal models to real-world applications, has received increasing attention among researchers. For Multimodal Sentiment Analysis (MSA), there is also a debate on whether multimodal models are more effective against noisy features than unimodal ones. Stressing on intuitive illustration and in-depth analysis of these concerns, we present Robust-MSA, an interactive platform that visualizes the impact of modality noise as well as simple defence methods to help researchers know better about how their models perform with imperfect real-world data. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Sentiment analysis

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Event-Triggered Mechanism-Based MPC for Path-Tracking Control of Four-Wheel Steering Vehicles 22

Event-Triggered Mechanism-Based MPC for Path-Tracking Contro...

引用

22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhang, Xiangyu Xu, Guoyan Li, Han Chen, Peng Xia, Qi Cai, Han School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

In this study, we tackle the path-tracking problem of a nonlinear four-wheel steering vehicle dynamics model subject to model mismatches and propose a model predictive control (MPC) algorithm based on an event-triggered mechanism (ET-MPC). The goal is to maintain closed-loop control performance while reducing the computational and communication burdens of traditional MPC. We introduce an ET-MPC framework utilizing a model-free reinforcement learning agent with proximal policy optimization (PPO). This agent interacts with the MPC system, progressively learning to determine the optimal event-triggered mechanism. To enhance exploration and training efficiency, we incorporate the Long Short-Term Memory (LSTM) technique into PPO. Experimental results show that the proposed ET-MPC framework, combined with reinforcement learning for reward optimization, demonstrates superior overall performance in path-tracking control of four-wheel steering vehicles. © 2024 IEEE.

关键词： Four wheel steering

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Cable segmentation based on Mask2Former in open-pit mining area 22

Cable segmentation based on Mask2Former in open-pit mining a...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Wang, Liyun Zhou, Bin Yang, Songyue Liu, Wentao Li, Huazhi Sun, Shengdi School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

The development of unmanned transportation technology has improved operational efficiency and safety in open-pit mining areas. However, there remain significant challenges to be addressed. One pressing issue is the need for mining trucks to pass through the area where the cable is laid on the ground. Accidentally crushing or damaging these cables would lead to significant risk to the mining area operation. However, due to the complexity of the mining environment and the characteristics of cables being thin and curved, the existing methods such as edge segmentation are difficult to meet the requirements for cable segmentation. This paper introduces a cable segmentation method based on Mask2Former and carries out comprehensive experiments to examine the effectiveness of the method. Experimental results show that the proposed method achieves IoU of 66.04% and PA of 80.08%, which can meet the requirements of practical applications. © 2024 IEEE.

关键词： Mine trucks

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Enhanced Multi-Agent Proximal Policy Optimization for Multi-UAV Target Offensive-Defensive Decision 42

Enhanced Multi-Agent Proximal Policy Optimization for Multi-...

引用

42nd Chinese Control Conference, CCC 2023

作者： Zheng, Yifan Xin, Bin Jiao, Keming Zhao, Zhixin Wang, Yuyang Zhao, Yunming School of Electronics and Information Engineering Tongji University Shanghai201804 China School of Automation Beijing Institute of Technology Beijing100081 China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789887581543

Autonomous collaborative decision-making is the key technology to achieve large-scale unmanned combat. Focus on the problem of multiple unmanned aerial vehicles' cooperative decision in target offensive and defensive combat, a multi-agent deep reinforcement learning (MADRL) based decision framework is proposed in this paper. Firstly, the simulation environment with a high-fidelity fixed-wing motion model is built. Secondly, to address the issue of high-dimension state space and credit assignment under a multi-agent environment, an enhanced multi-agent proximal policy optimization with mean-field counterfactual advantage (MAPPO - MFCOA) is proposed. Finally, the results of simulation experiments will verify the performance of the proposed approach. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Reinforcement learning

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Anomaly Detection and Fault Diagnosis Method for Autonomous Transport Vehicles on Unstructured Roads 22

Anomaly Detection and Fault Diagnosis Method for Autonomous ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhang, Yifang Yu, Guizhen Li, Han Zhang, Chaoqi Li, Lecong Zhang, Chuanying School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

Autonomous vehicles in mining areas undertake substantial production tasks and are prone to various faults during operation. Early detection of abnormalities, along with timely fault warnings and diagnoses, can enhance transportation safety and increase vehicle turnout rates. This study utilizes driving data from autonomous vehicles in mining areas and considers the characteristics of unstructured road scenes. The driving area is segmented into distinct intervals, and Kullback-Leibler (KL) divergence is applied within each interval to detect anomalies in the vehicle's lateral deviation during operation. Experimental results demonstrate that the proposed method achieves an anomaly detection accuracy of 91.4%, with a false negative rate of 8.3% and a false positive rate of 8.7%. © 2024 IEEE.

关键词： Vehicle detection

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Prediction of Driving Departure of Mining Autonomous Transport Vehicles Based on GRU Network 22

Prediction of Driving Departure of Mining Autonomous Transpo...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Li, Lecong Yu, Guizhen Li, Han Xia, Qi Zhang, Xiangyu Cai, Han School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

Open-pit mining areas have special geological structures, complex road networks, multi-rotation sections and poor road conditions, which bring many challenges to the operation of mining autonomous transport vehicles. Due to the large size and high control difficulty of mining autonomous transport vehicles, poor control effect and inaccurate steering mechanism implementation are prone to occur during the driving process, which leads to the vehicle departure from the reference path. To ensure the safety of autonomous operation in intelligent mine, this paper proposes a method for predicting driving departure of mining autonomous vehicles based on Gated Recurrent Unit (GRU). Firstly, the vehicle's historical trajectory data is obtained via on-board sensors, followed by data cleaning and normalization processes. key features are extracted, and a vehicle driving scene recognition module based on a GRU-based network is designed using deep learning. Subsequently, a GA-seq2seqGRU trajectory prediction module is constructed utilizing the scene recognition results, and the Genetic Algorithms (GA) is employed to tune the network hyper-parameters. Based on the predicted trajectories, the overall departure risk is calculated using two departure judgment methods based on cross-lane time and predicted lateral deviation, which are mapped to the departure level. Simulation experiments demonstrate that the accuracy of the driving scene recognition model in the proposed method in this paper reaches 0.9721, which is better than the 0.9585 of the Long Short Memory Neural Network (LSTM) model, and the trajectory prediction model based on the results of the driving scene recognition has a smaller RMSE value than the LSTM, GRU, and GA-seq2seqLSTM models when the prediction time domains are 1s, 2s, 3s, 4s, and 5s, and the departure detection module The average time consumed is 0.142ms. © 2024 IEEE.

关键词： Mine transportation

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Real-time 3D Object Detection based on Dynamic Sparse Voxel Transformer in Mining Area 22

Real-time 3D Object Detection based on Dynamic Sparse Voxel ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Sun, Shengdi Wang, Zhangyu Liu, Runsen Yang, Songyue Wang, Liyun Liu, Wentao School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

To improve the safety and efficiency of mining operation, the development of unmanned transportation technologies for mining trucks is extraordinarily rapid. The application of LiDAR in autonomous mining trucks has also become increasingly popular. However, due to the significant difference in the size of objects and the complex noise, the problem of point cloud object detection in mining scenarios remains a major challenge. Existing methods, such as those based on point cloud clustering algorithms, have proved to be difficult to meet practical application requirements. In this paper, a real-time point cloud object detection based on Dynamic Sparse Voxel Transformer (DSVT) is introduced. A series of experiments were conducted to examine the efficacy of the proposed methodology. The experimental results demonstrated that the proposed method attained a mean average precision of 75%. Additionally, the detection latency of the proposed method was observed to be 62 ms. Collectively, the detection speed and accuracy both align with the practical application requirements. © 2024 IEEE.

关键词： Trucks

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Versatile Jumping of Humanoid Robots via Curriculum-Assisted Reinforcement Learning

Versatile Jumping of Humanoid Robots via Curriculum-Assisted...

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Chinese Automation Congress (CAC)

作者： Ruiyu Tan Xueai Li Fenglei Ni Dong Zhou Yi Ji Xiangyu Shao Key Laboratory of Autonomous Intelligent Unmanned Systems Harbin Institute of Technology Harbin China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Versatile jumping is crucial for agile and adaptive behavior of humanoid robots in complex environments. However, humanoid robots have to manage impact forces while maintaining balance and stability throughout the take-off, flight, and landing phases, which imposes significant challenges for motion planning and controller design. This paper proposes a curriculum-assisted reinforcement learning framework for the humanoid robot to achieve various jumping tasks. The curriculum learning divides the jump training process into multi-stages and design different episodes in each stage to guarantee the training feasibility and generalize learned skills. Simulations on a 28 DOFs humanoid robot verify the excellent diverse jumping capability of the proposed policy. This exploration of different tasks also proves the policy's robustness, allowing the robot quickly recover from perturbations using a variety of learned maneuvers.

关键词： Training Automation Perturbation methods Humanoid robots Reinforcement learning Robustness Stability analysis Planning

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