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Application of COPRAS, PROMETHEE, and EDAS methods in sustainable energy development: A comparative study case

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Procedia computer science 2024年 246卷 5428-5438页

作者： Jakub Więckowski Paweł Gajewski Krzysztof Swałdek Wojciech Sałabun National Institute of Telecommunications ul. Szachowa 1 04-894 Warsaw Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 31-155 Krakow Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. żołnierska 49 71-210 Szczecin Poland

This paper provides a comparative analysis of three selected Multi-Criteria Decision Analysis (MCDA) methods in a practical decision problem to examine the robustness of the obtained results. The Complex Proportional Assessment (COPRAS), Preference Ranking Organization METHod for Enrichment of Evaluations (PROMEHTEE) II, and Evaluation based on Distance from Average Solution (EDAS) methods were utilized for the evaluation purposes, while the Ranking Comparison (RANCOM) method was applied to determine criteria weights. The application was directed toward sustainable development management in selected European countries considering different energy sources in the consumption share per capita. The obtained results showed, that depending on the used MCDA method, the ranking of countries differed, thus showing that the evaluation process is sensitive to changing assessment methods.

关键词： multi-criteria decision analysis comparative analysis sustainable development renewable energy sources

All-in-one simulation-based inference

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arXiv 2024年

作者： Gloeckler, Manuel Deistler, Michael Weilbach, Christian Wood, Frank Macke, Jakob H. Machine Learning in Science University of Tübingen Tübingen AI Center Tübingen Germany Department of Computer Science University of British Columbia Vancouver Canada Max Planck Institute for Intelligent Systems Department Empirical Inference Tübingen Germany

Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference for any newly observed data. However, current simulation-based amortized inference methods are simulation-hungry and inflexible: They require the specification of a fixed parametric prior, simulator, and inference tasks ahead of time. Here, we present a new amortized inference method—the Simformer—which overcomes these limitations. By training a probabilistic diffusion model with transformer architectures, the Simformer outperforms current state-of-the-art amortized inference approaches on benchmark tasks and is substantially more flexible: It can be applied to models with function-valued parameters, it can handle inference scenarios with missing or unstructured data, and it can sample arbitrary conditionals of the joint distribution of parameters and data, including both posterior and likelihood. We showcase the performance and flexibility of the Simformer on simulators from ecology, epidemiology, and neuroscience, and demonstrate that it opens up new possibilities and application domains for amortized Bayesian inference on simulation-based models. © 2024, CC BY-NC-SA.

关键词： Stochastic systems

Spatio-temporal Motion Planning for Autonomous Vehicles with Trapezoidal Prism Corridors and Bézier Curves

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Spatio-temporal Motion Planning for Autonomous Vehicles with...

American Control Conference (ACC)

作者： Srujan Deolasee Qin Lin Jialun Li John M. Dolan Department of Computer Science & Information Systems Birla Institute of Technology and Science Pilani Rajasthan India Department of Electrical Engineering and Computer Science Cleveland State University Cleveland OH USA Dajiang Innovation Technology Co. Ltd Shenzhen China Robotics Institute Carnegie Mellon University Pittsburgh PA USA

Safety-guaranteed motion planning is critical for self-driving cars to generate collision-free trajectories. A layered motion planning approach with decoupled path and speed planning is widely used for this purpose. This approach is prone to be suboptimal in the presence of dynamic obstacles. Spatial-temporal approaches deal with path planning and speed planning simultaneously; however, the existing methods only support simple-shaped corridors like cuboids, which restrict the search space for optimization in complex scenarios. We propose to use trapezoidal prism-shaped corridors for optimization, which significantly enlarges the solution space compared to the existing cuboidal corridors-based method. Finally, a piecewise Bézier curve optimization is conducted in our proposed corridors. This formulation theoretically guarantees the safety of the continuous-time trajectory. We validate the efficiency and effectiveness of the proposed approach in numerical and CommonRoad simulations.

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MF-MOS: A Motion-Focused Model for Moving Object Segmentation

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MF-MOS: A Motion-Focused Model for Moving Object Segmentatio...

IEEE International Conference on robotics and Automation (ICRA)

作者： Jintao Cheng Kang Zeng Zhuoxu Huang Xiaoyu Tang Jin Wu Chengxi Zhang Xieyuanli Chen Rui Fan School of Electronic and Information Engineering South China Normal University Foshan China Department of Computer Science Aberystwyth University Aberystwyth U.K. Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China School of Internet of Things Engineering Jiangnan University Wuxi China College of Intelligence Science and Technology National University of Defense Technology Changsha China College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems the State Key Laboratory of Intelligent Autonomous Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Moving object segmentation (MOS) provides a reliable solution for detecting traffic participants and thus is of great interest in the autonomous driving field. Dynamic capture is always critical in the MOS problem. Previous methods capture motion features from the range images directly. Differently, we argue that the residual maps provide greater potential for motion information, while range images contain rich semantic guidance. Based on this intuition, we propose MF-MOS, a novel motion-focused model with a dual-branch structure for LiDAR moving object segmentation. Novelly, we decouple the spatial-temporal information by capturing the motion from residual maps and generating semantic features from range images, which are used as movable object guidance for the motion branch. Our straightforward yet distinctive solution can make the most use of both range images and residual maps, thus greatly improving the performance of the LiDAR-based MOS task. Remarkably, our MF-MOS achieved a leading IoU of 76.7% on the MOS leaderboard of the SemanticKITTI dataset upon submission, demonstrating the current state-of-the-art performance. The implementation of our MF-MOS has been released at https://***/SCNU-RISLAB/MF-MOS.

关键词： Laser radar Semantics Dynamics Object segmentation Data augmentation Data models Reliability

Transmission Control Protocol (TCP) delay analysis in real time network

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Transmission Control Protocol (TCP) delay analysis in real t...

2020 Advances in science and Engineering Technology International Conferences, ASET 2020

作者： D'Souza, Joanita Kaur, Maninder Jeet Mohamad, Hassan Anwar Maheshwari, Piyush Amity University Dubai Department of Computer Science Engineering Dubai United Arab Emirates Institute of Robotics and Intelligent Systems Toronto Canada

ISBN: (纸本)9781728146409

The aim of this paper is to evaluate the Transmission control protocol (TCP) delay performance of IEEE802.11 WLANs using Wireshark which is a network performance monitoring tool. It also shows a detailed study of sniffing tools, logging tools, pre filtering and post sniffing. This paper also provides a way to find the maximum TCP delays over a time period and to plot a graph using Microsoft excel in order to perform further analysis. The study was carried out in a real time WLAN environment. The output that is obtained shows the maximum delay over time along with certain filters that have been applied. This is only possible for the active nodes present in the network. © 2020 IEEE.

关键词： Transmission control protocol

Provably Convergent Federated Trilevel Learning

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arXiv 2023年

作者： Jiao, Yang Yang, Kai Wu, Tiancheng Jian, Chengtao Huang, Jianwei Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to their nested decision-making structure. In addition, existing works on TLO face the following key challenges: 1) they all focus on the non-distributed setting, which may lead to privacy breach;2) they do not offer any non-asymptotic convergence analysis which characterizes how fast an algorithm converges. To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems. The proposed method utilizes µ-cuts to construct a hyper-polyhedral approximation for the TLO problem and solve it in an asynchronous manner. We demonstrate that the proposed µ-cuts are applicable to not only convex functions but also a wide range of non-convex functions that meet the µ-weakly convex assumption. Furthermore, we theoretically analyze the non-asymptotic convergence rate for the proposed method by showing its iteration complexity to obtain ϵ-stationary point is upper bounded by (Formula Presented). Extensive experiments on real-world datasets have been conducted to elucidate the superiority of the proposed method, e.g., it has a faster convergence rate with a maximum acceleration of approximately 80%. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

Vivim: a Video Vision Mamba for Medical Video Segmentation

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arXiv 2024年

作者： Yang, Yijun Xing, Zhaohu Yu, Lequan Huang, Chunwang Fu, Huazhu Zhu, Lei Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China The Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong Hong Kong Guangdong Provincial People’s Hospital Guangzhou China The Institute of High Performance Computing A*STAR Singapore Robotics and Autonomous Systems Thrust Hong Kong University of Science and Technology Guangzhou China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

Medical video segmentation gains increasing attention in clinical practice due to the redundant dynamic references in video frames. However, traditional convolutional neural networks have a limited receptive field and transformer-based networks are mediocre in constructing long-term dependency from the perspective of computational complexity. This bottleneck poses a significant challenge when processing longer sequences in medical video analysis tasks using available devices with limited memory. Recently, state space models (SSMs), famous by Mamba, have exhibited impressive achievements in efficient long sequence modeling, which develops deep neural networks by expanding the receptive field on many vision tasks significantly. Unfortunately, vanilla SSMs failed to simultaneously capture causal temporal cues and preserve non-casual spatial information. To this end, this paper presents a Video Vision Mamba-based framework, dubbed as Vivim, for medical video segmentation tasks. Our Vivim can effectively compress the long-term spatiotemporal representation into sequences at varying scales with our designed Temporal Mamba Block. We also introduce an improved boundary-aware affine constraint across frames to enhance the discriminative ability of Vivim on ambiguous lesions. Extensive experiments on thyroid segmentation, breast lesion segmentation in ultrasound videos, and polyp segmentation in colonoscopy videos demonstrate the effectiveness and efficiency of our Vivim, superior to existing methods. The code is available at: https://***/scottyjyang/Vivim. The dataset will be released once accepted. © 2024, CC BY.

关键词： Deep neural networks

NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning

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arXiv 2023年

作者： Wang, Jianfeng Hu, Xiaolin Lukasiewicz, Thomas Computer Science Department University of Oxford United Kingdom Institute for Artificial Intelligence The State Key Laboratory of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China Institute of Logic and Computation TU Wien Austria

Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to the semi-supervised image classification task, resulting in a new method named NP-Match. NP-Match is suited to this task for two reasons. Firstly, NP-Match implicitly compares data points when making predictions, and as a result, the prediction of each unlabeled data point is affected by the labeled data points that are similar to it, which improves the quality of pseudo-labels. Secondly, NP-Match is able to estimate uncertainty that can be used as a tool for selecting unlabeled samples with reliable pseudo-labels. Compared with uncertainty-based SSL methods implemented with Monte-Carlo (MC) dropout, NP-Match estimates uncertainty with much less computational overhead, which can save time at both the training and the testing phases. We conducted extensive experiments on five public datasets under three semi-supervised image classification settings, namely, the standard semi-supervised image classification, the imbalanced semi-supervised image classification, and the multi-label semi-supervised image classification, and NP-Match outperforms state-of-the-art (SOTA) approaches or achieves competitive results on them, which shows the effectiveness of NP-Match and its potential for SSL. © 2023, CC BY.

关键词： Image classification

A Development of Time-Varying Weight Model Predictive Control for Autonomous Vehicles

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A Development of Time-Varying Weight Model Predictive Contro...

2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Qazani, Mohammad Reza Chalak Asadi, Houshyar Shajari, Arian Najdovski, Zoran Lim, Chee Peng Nahavandi, Saeid Murray State University Murray Department of the Computer Science and Information Systems MurrayKY42071 United States Institute for Intelligent System Research and Innovation Deakin University GeelongVIC43216 Australia Swinburne University of Technology HawthornVIC Australia Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA United States

ISBN: (纸本)9798350337020

Autonomous vehicles, commonly known as self-driving cars, are rapidly gaining popularity due to their numerous advantages, such as reducing traffic, pollution, and emissions while increasing safety, convenience, and transportation connectivity. In order to accurately track the motion signal, these vehicles are now utilising advanced control techniques, such as model predictive control (MPC). However, the efficiency of MPCs heavily relies on properly tuning their weights. The primary function of the MPC is to recalculate the optimal values for the vehicle control commands, such as desired speed, steering angle, etc., while considering the dynamic model of the autonomous vehicle. The existing linear MPC models cannot reach higher efficiency because of using fixed weights without considering the error. This paper introduces a novel approach for developing an MPC model with a time-varying weights algorithm for autonomous vehicles. The study aims to minimise motion tracking errors such as lateral position and yaw angle errors. Relevant MPC weights are calculated online using fuzzy logic-based units considering the lateral position and yaw angle errors. The proposed linear time-varying MPC was designed and developed using MATLAB software, resulting in improved motion tracking performance with 31.62% and 20.89% reduction of the root means square error of lateral position and yaw angle. © 2023 IEEE.

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