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NARX Neural Network Modeling of Batch Distillation Process

NARX Neural Network Modeling of Batch Distillation Process

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11th IEEE International Conference on system engineering and Technology, ICSET 2021

作者： Putri, Adi Novitarini MacHbub, Carmadi Hidayat, Egi Muhammad Idris School of Electrical Engineering and Informatics Institut Teknologi Control Computer System Research Group Bandung Indonesia

ISBN: (纸本)9781665437660

The batch distillation model is highly nonlinear due to the influence of mass and composition of the initial material to be separated. It is also caused by the thermodynamics of the system which is not ideal. Therefore, analytical batch distillation modeling does not fulfill superposition theory, or is nonlinear. Thus, any well-established linear control schemes can not be applied here. Meanwhile, modeling a system using the Nonlinear Autoregressive with eXogenous inputs (NARX) is quite widely used today because of its ability to represent non-linear system dynamics. This paper propose a system identification using NARX Neural Network (NARX-NN) to modeling batch distillation process. In order to prove the superiority of NARX-NN's accuracy in the system identification process, a comparison with linear ARMA models is done. In this study, ARMA approximation was carried out in two ways. The first way is to use a neural network, while the second method is through approximation to the discrete transfer function. The validation results show that the NARX-NN achieves significantly better fit compared to the linear models. NARX-NN and ARMA-NN were compared and their MSE ratio for delay input and output 1 and 3, respectively have the smallest value, i.e 1.71e-04 © 2021 IEEE.

关键词： Thermodynamics Analytical models system dynamics Conferences Neural networks Transfer functions systems engineering and theory

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ESNet: Evolution and Succession Network for High-Resolution Salient Object Detection 41

ESNet: Evolution and Succession Network for High-Resolution ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Hongyu Cong, Runmin Li, Hua Xu, Qianqian Huang, Qingming Zhang, Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature;the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24. Copyright 2024 by the author(s)

关键词： Miocene

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection 30

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Cheng, Long He, Haoyang Gu, Yan Liu, Qingzhi Zhao, Zhiming Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Wageningen University and Research Information Technology Group Netherlands University of Amsterdam Research Group of Multi-Scale Networked Systems Netherlands

ISBN: (纸本)9798331515966

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of 53.18% and costs by 61.98% compared to basic techniques, and achieving 20.26% and 25.71% improvements over the latest advanced methods, respectively. © 2024 IEEE.

关键词： Deep reinforcement learning

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Vulnerability Detection Based on Adapter Tuning and Enhanced Feature Learning 36

Vulnerability Detection Based on Adapter Tuning and Enhanced...

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36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Luo, Hui Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China PengCheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangdong Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangdong Guangzhou China

ISBN: (纸本)1891706594

Pre-trained code models have achieved promising results in the vulnerability detection field. The prevailing approach is to adapt these models with vulnerability datasets using inefficient full-model fine-tuning. These pre-trained models primarily capture the semantic features of code, while neglecting its structural characteristics. To address these issues, this paper proposes a vulnerability detection method with adapter tuning and enhanced feature learning. First, adapter modules are introduced to UniXcoder and tune only the parameters in the adapters to extract semantic features. This significantly reduces the number of training parameters and better adapts the model to downstream tasks. Then, structural features, including control and data flow information, are extracted from the Program Dependence Graph (PDG) to compensate for the limitations of pre-trained models that rely solely on semantic features. Finally, the semantic and structural features are integrated to train the detection model. The experimental results demonstrate that our proposed method outperforms state-of-the-art approaches and is highly efficient in terms of both training parameters and training data. © 2024 Knowledge systems Institute Graduate school. All rights reserved.

关键词： Data flow graphs

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Radar-Based In-Home Monitoring system for Supporting Aging and Wellness

Radar-Based In-Home Monitoring System for Supporting Aging a...

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Microwaves Antennas and Propagation Conference (MAPCON)

作者： Hajar Abedi Ahmad Ansariyan Plinio P Morita Alexander Wong Jennifer Boger George Shaker Electrical and Computer Engineering University of Waterloo Waterloo Canada School of Public Health Sciences University of Waterloo Waterloo Canada System Design Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798350379693

ISBN: (纸本)9798350379709

This study presents a novel approach to continuous, in-home gait analysis using Multiple Input Multiple Output Frequency-Modulated Continuous Wave (MIMO FMCW) radar, with a specific focus on step length and step count detection within naturalistic environments. Traditional gait monitoring methods are often intrusive and impractical for continuous use, particularly in personal living spaces. Our system was designed to address these limitations by enabling reliable, non-invasive monitoring of gait patterns in cluttered, real-world environments. The study is the first of its kind to successfully detect and analyze varying step lengths and counts in non-linear walking paths within a small apartment setting. The results demonstrate that our radar-based system can accurately capture step length and count with minimal error, offering a promising solution for autonomous, continuous athome gait monitoring. The experimental results demonstrate the method’s robustness in detecting gait parameters in a small apartment, highlighting its potential for long-term, nonintrusive monitoring of individuals at risk of falls or other mobility issues. This study marks a significant advancement in deploying non-invasive gait analysis systems in naturalistic home settings, with promising applications in healthcare, rehabilitation, and elderly care.

关键词： Legged locomotion Accuracy Radar detection Medical services Aging Reliability engineering Robustness Biomedical monitoring Monitoring Gait recognition

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Seastar: A Cache-Efficient and Load-Balanced Key-Value Store on Disaggregated Memory

Seastar: A Cache-Efficient and Load-Balanced Key-Value Store...

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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Du, Jingwen Wang, Fang Feng, Dan Zeng, Dexin Yi, Sheng Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System China School of Computer Science and Technology Huazhong University of Science and Technology China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (纸本)9798350358711

In modern datacenters, memory disaggregation un-packs monolithic servers to build independent network-connected compute and memory pools, greatly improving resource uti-lization. Existing memory-disaggregated key-value stores adopt ownership sharing or ownership partitioning for request scheduling and they all show poor performance and scalability. The former generates multiple cache misses and multiple network round trips due to poor cache locality, while the latter fails to fully use resources on all Compute Nodes (CNs) due to load imbalance. In this paper, we propose Seastar, a fast and scalable key-value store that achieves high cache locality as well as load balancing. To enable high concurrency and ensure linearizability, Seastar exploits a self-verification version chain to provide out-of-place atomic updates and ensure consistent reads. To improve cache locality, Seastar leverages a pair-based ownership assigning scheme to assign the processing permission of requests corre-sponding to each key to a CN pair through two independent hash functions. To ensure load balancing among CNs, Seastar proposes a decentralized power-of-two-choices routing scheme to route the requests to the less-loaded CNs in CN pairs for handling. Experimental results demonstrate that Seastar improves the throughput by up to 6.7x and significantly reduces the latency compared with state-of-the-art memory-disaggregated key-value stores. © 2024 IEEE.

关键词： Scalability

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Towards Large-Scale Benchmark Dataset for Remote Sensing Object Detection on Battlefield

Towards Large-Scale Benchmark Dataset for Remote Sensing Obj...

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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Yechan Kim JongHyun Park Sihyun Kim Sungheon Kim SooYeon Kim YeongMin Ko Junggyun Oh Moongu Jeon School of Electrical Engineering and Computer Science GIST South Korea Department of Robot and Smart System Engineering Kyungpook National University South Korea HDC Labs South Korea

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

Precise object detection allows military personnel to clearly understand their surroundings, leading to planning effective military strategies. Particularly, satellites and drones allow real-time surveillance over large areas, which is crucial for military operations. Collaboration among military, academic, and industrial institutions is required to promote innovation in military target detection. However, potential security concerns usually restrict access to real data from the military. To mitigate this issue, this paper proposes a novel approach to generate synthetic data for remote sensing object detection on the bat-tlefield with ARMA3, one of the renowned military tactic games. With our method, the data for model training can be easily generated without any manpower. To demonstrate the efficacy of our approach, we provide a detailed analysis of the examples from our method. As ARMA3 is well-known for its realistic military combat simulation, we believe our method can effectively contribute to military object detection in remote sensing.

关键词： Training Technological innovation Satellites Surveillance Object detection Real-time systems Sensors Security Remote sensing Synthetic data

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Hybridization of Variable Neighborhood Search and Simulated Annealing for the Collaborative Truck-Drone Routing Problem

Hybridization of Variable Neighborhood Search and Simulated ...

引用

International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Luo, Qizhang Wu, Guohua Mao, Ni Shi, Jianmai School of Traffic and Transportation Engineering Central South University Changsha410075 China Department of Electrical and Computer Engineering National University of Singapore Singapore119260 Singapore College of System Engineering National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789811694912

In this paper, we address a collaborative routing problem of the truck-drone delivery system, in which a truck collaborates with multiple drones to perform parcel deliveries. To meet the practical demands of non-emergency deliverirs, each vehicle (i.e., a truck or a drone) is allowed to serve a customer earlier and later than the required time window. We propose a variable neighborhood search metaheuristic incorporated with an acceptable criterion of simulated annealing (HVNS-SA) to solve this problem. Besides, we design a novel solution representation and introduce 6 problem-specific neighborhood structures to construct the hybrid metaheuristic. Finally, extensive experiments are conducted based on a set of benchmark instances to evaluate the performance of HVNS-SA. The comparison studies with simulated annealing (SA), conventional variable neighborhood search (CVNS), and variable neighborhood descent (VND) show the superiority of HVNS-SA. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Simulated annealing

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Extension of Harmonic Product Spectrum and Its Application in Bearing Condition Monitoring 1

引用

UNIfied Conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Chen, Bingyan Wang, Shengbo Cheng, Yao Gu, Fengshou Zhang, Weihua Zhou, Zewen Huang, Li State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong University Chengdu610031 China Centre for Efficiency and Performance Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom School of Computer Sichuan Technology and Business University Chengdu611745 China

ISBN: (数字)9783031494130

ISBN: (纸本)9783031494123

Accurate condition monitoring and timely fault diagnosis are very important for rotating machinery equipment. The harmonic product spectrum (HPS) based on envelope spectrum has been proven to be an effective fault diagnosis tool by revealing the cyclostationarity of fault signals of rotating machinery. However, its performance under different interfering noises has not been well explored and revealed. In this paper, based on the recently proposed generalized envelope spectra, three new versions of HPS are first proposed using log-envelope spectrum, fractional envelope spectrum and squared envelope spectrum respectively. Subsequently, the effects of discrete harmonic noise, random impulse noise and Gaussian background noise on the performance of envelope spectrum-based HPS and three new versions of HPS are comparatively investigated by numerical simulation signals. The results show that different versions of HPS show different robustness to different interference noises. The robustness of HPS based on log-envelope spectrum to random impulse noise is stronger than that based on envelope spectrum and squared envelope spectrum. When Gaussian background noise is dominant, the four HPSs have similar capabilities to reveal cyclostationarity. Finally, four HPSs are applied to the condition monitoring of rolling bearings by virtue of their capability to characterize cyclostationarity. The experimental results show that the four HPSs can not only effectively monitor the degradation status of rolling bearings but also accurately diagnose early faults. This work provides new and effective tools for condition monitoring of rolling bearings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Condition monitoring

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An Area- and Power-Efficient 32-channel Multi-Energy Photon Counting Readout ASIC Using Successive Approximation Register Logic

An Area- and Power-Efficient 32-channel Multi-Energy Photon ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： T. Lu L. Sun J. Zhang T. Gao Y. Dou Y. Zhang X. Gao W. Gao Interdisciplinary Research Center of Bio-inspired System-on-chip Northwestern Polytechnical University School of Computer Science Xi’an China Engineering Research Center of Embedded System Integrated Ministry of Education Xi’an China

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

In computed tomography (CT) X-ray imaging technology, the multi-energy window setting offers greater advantages in material decomposition and image quality. A traditional multi-energy discrimination photon counting circuit is typically implemented by a multiple-discriminator scheme, which employs multiple comparators to generate pulse signals and drives multiple counters for photon counting, within a single channel to implement the division of different energy windows. However, area overhead and static power consumption of each channel are significantly increased when the number of energy windows increases. This paper presents a novel circuit scheme based on peak detector voltage signals digitized by a successive approximation (SAR) analog-to-digital converter (ADC). The number of comparators will not increase with the extension of energy windows. A 32-channel front-end readout application-specific integrated circuit (ASIC) is designed in $0.35 \mu \mathrm{m}$ CMOS process with a die area of $4.8 \mathrm{~mm} \times 4.8 \mathrm{~mm}$. A charge-sensitive amplifier, a CR-RC pulse shaper, a gain amplifier, an event-driven peak detector and a 4-bit SAR ADC are integrated into each channel to discriminate and count incoming photon information through eight energy windows with a dynamic range of from 3 keV to 80 keV. The simulated equivalent noise charge is $53 \mathrm{e}^{-}$(rms) plus $4 \mathrm{e}^{-} / \mathrm{pF}$. The count rate is up to 1 Mcps under the power dissipation of about $4.9 \mathrm{~mW} /$ channel with a supply voltage of 3.3 V. The area overhead of the proposed circuit is shrunk by 5 times, and power consumption is reduced by 22%, compared with traditional multiple energy discrimination counting circuits.

关键词： Power demand Semiconductor detectors Computed tomography Noise Voltage Registers Power dissipation

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