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Pay-per-flight dynamic pricing of UAV operations 1

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Pay-per-flight dynamic pricing of UAV operations

1st International Conference on artificial intelligence and data Analytics for Air Transportation, AIDA-AT 2020

作者： Rubio-Hervas, Jaime Gupta, Abhishek Ong, Yew-Soon Reyhanoglu, Mahmut Data Science and Artificial Intelligence Research Centre Nanyang Technological University Singapore Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore University of North Carolina at Asheville Department of Engineering Asheville United States

ISBN: (纸本)9781728153803

Insuring unmanned aerial vehicles (UAVs) is a relatively new concept, where not much data is available yet. We propose the combination of available data from different sources, other than past accident rates, to stochastically model the operational environment by using Gaussian process-based function approximations. A data-driven risk measure is then derived through such stochastic formulation accounting for both aleatoric uncertainties of the considered environmental factors as well as epistemic uncertainties originating from the geographical sparsity of data collection sources. The risk measure is obtained in a path-integral form which represents the operational risk associated with a defined operation in partially unknown environments. A novel pay-per-flight dynamic pricing scheme is derived from such risk measure. © 2020 IEEE.

关键词： Antennas

Dynamic Lane Management with IoT for Real-time Lane Configuration and Traffic Flow

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Dynamic Lane Management with IoT for Real-time Lane Configur...

computer, Communication and Control (IC4), International Conference on

作者： Mohammed Rehaman Pasha S. Latha R. Usha V. Bini Marin M.V Ishwarya M. Muthulekshmi Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Department of Biotechnology Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation(DU) Salem Tamil Nadu India Department of Mathematics S. A. Engineering College Chennai Tamil Nadu India Faculty of Management Studies SRM Institute of Science and Technology Ramapuram Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Traffic may be reduced and transportation efficiency improved by properly controlling traffic flow in urban areas. This research provides a new method for managing lanes in real-time by using IoT connectivity. To optimize traffic flow in response to shifting demand patterns, the suggested system prioritizes making lane design changes in real time depending on the current traffic conditions. Using information collected from Internet of Things (IoT) sensors and linked automobiles, the system may dynamically allot lanes in response to variations in traffic volumes and congestion. It describes developing and deploying a dynamic lane control system based on the Internet of Things, stressing the technology’s potential to improve traffic flow in congested metropolitan areas. The simulation results show its efficiency in decreasing traffic and increasing throughput. It demonstrates the potential of intelligent transportation systems for creating smarter and more efficient cities by combining IoT, real-time data processing, and traffic control.

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Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection

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arXiv 2024年

作者： Zhang, Yaning Li, Qiufu Yu, Zitong Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China School of Computing and Information Technology Great Bay University Guangdong Dongguan523000 China

Face forgery detection (FFD) is devoted to detecting the authenticity of face images. Although current CNN-based works achieve outstanding performance in FFD, they are susceptible to capturing local forgery patterns generated by various manipulation methods. Though transformer-based detectors exhibit improvements in modeling global dependencies, they are not good at exploring local forgery artifacts. Hybrid transformer-based networks are designed to capture local and global manipulated traces, but they tend to suffer from the attention collapse issue as the transformer block goes deeper. Besides, soft labels are rarely available. In this paper, we propose a distilled transformer network (DTN) to capture both rich local and global forgery traces and learn general and common representations for different forgery faces. Specifically, we design a mixture of expert (MoE) module to mine various robust forgery embeddings. Moreover, a locally-enhanced vision transformer (LEVT) module is proposed to learn locally-enhanced global representations. We design a lightweight multi-attention scaling (MAS) module to avoid attention collapse, which can be plugged and played in any transformer-based models with only a slight increase in computational costs. In addition, we propose a deepfake self-distillation (DSD) scheme to provide the model with abundant soft label information. Extensive experiments show that the proposed method surpasses the state of the arts on five deepfake datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

Affine Frequency Division Multiplexing: Extending OFDM for Scenario-Flexibility and Resilience

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arXiv 2025年

作者： Yin, Haoran Tang, Yanqun Bemani, Ali Kountouris, Marios Zhou, Yu Zhang, Xingyao Liu, Yuqing Chen, Gaojie Yang, Kai Liu, Fan Masouros, Christos Li, Shuangyang Caire, Giuseppe Xiao, Pei School of Electronics and Communication Engineering Sun Yat-sen University China Mathematical and Algorithmic Sciences Laboratory Huawei France Research and Development France Communication Systems Department EU-RECOM France Department of Computer Science and Artificial Intelligence University of Granada Spain Sun Yat-sen University China School of Information and Electronics Beijing Institute of Technology China National Mobile Communications Research Laboratory Southeast University Bangladesh Department of Electrical and Electronic Engineering University College London United Kingdom Department of Electrical Engineering and Computer Science Technical University of Berlin Germany 5G/6G Innovation Centre University of Surrey United Kingdom

Next-generation wireless networks are conceived to provide reliable and high-data-rate communication services for diverse scenarios, such as vehicle-to-vehicle, unmanned aerial vehicles, and satellite networks. The severe Doppler spreads in the underlying time-varying channels induce destructive inter-carrier interference (ICI) in the extensively adopted orthogonal frequency division multiplexing (OFDM) waveform, leading to severe performance degradation. This calls for a new air interface design that can accommodate the severe delay-Doppler spreads in highly dynamic channels while possessing sufficient flexibility to cater to various applications. This article provides a comprehensive overview of a promising chirp-based waveform named affine frequency division multiplexing (AFDM). It is featured with two tunable parameters and achieves optimal diversity order in doubly dispersive channels (DDC). We study the fundamental principle of AFDM, illustrating its intrinsic suitability for DDC. Based on that, several potential applications of AFDM are explored. Furthermore, the major challenges and the corresponding solutions of AFDM are presented, followed by several future research directions. Finally, we draw some instructive conclusions about AFDM, hoping to provide useful inspiration for its development. Copyright © 2025, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

Inverse learning of black-box aggregator for robust Nash equilibrium

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios

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arXiv 2024年

作者： Masri, Sari Ashqar, Huthaifa I. Elhenawy, Mohammed Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Civil Engineering Department Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Artificial Intelligence Program Fu Foundation School of Engineering and Applied Science Columbia University 500 W 120th St New YorkNY10027 United States CARRS-Q Centre for Data Science Queensland University of Technology 130 Victoria Park Rd Kelvin GroveQLD4059 Australia

Urban traffic management faces significant challenges due to the dynamic environments, and traditional algorithms fail to quickly adapt to this environment in real-time and predict possible conflicts. This study explores the ability of a Large Language Model (LLM), specifically, GPT-4o-mini to improve traffic management at urban intersections. We recruited GPT-4o-mini to analyze, predict position, detect and resolve the conflicts at an intersection in real-time for various basic scenarios. The key findings of this study to investigate whether LLMs can logically reason and understand the scenarios to enhance the traffic efficiency and safety by providing real-time analysis. The study highlights the potential of LLMs in urban traffic management creating more intelligent and more adaptive systems. Results showed the GPT-4o-mini was effectively able to detect and resolve conflicts in heavy traffic, congestion, and mixed-speed conditions. The complex scenario of multiple intersections with obstacles and pedestrians saw successful conflict management as well. Results show that the integration of LLMs promises to improve the effectiveness of traffic control for safer and more efficient urban intersection management. © 2024, CC BY.

关键词： Traffic congestion

Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm

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arXiv 2024年

This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as traffic controllers. The study utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput and provide feedback based on traffic conditions in real-time. LLMs centralize traditionally disconnected traffic control processes and can integrate traffic data from diverse sources to provide context-aware decisions. LLMs can also delivere tailored outputs using various means such as wireless signals and visuals to drivers, infrastructures, and autonomous vehicles. To evaluate LLMs ability as traffic controllers, this study proposed a four-stage methodology. The methodology includes data creation and environment initialization, prompt engineering, conflict identification, and fine-tuning. We simulated multi-lane four-leg intersection scenarios and generates detailed datasets to enable conflict detection using LLMs and Python simulation as a ground truth. We used chain-of-thought prompts to lead LLMs in understanding the context, detecting conflicts, resolving them using traffic rules, and delivering context-sensitive traffic management solutions. We evaluated the prformance GPT-mini, Gemini, and Llama as traffic controllers. Results showed that the fine-tuned GPT-mini achieved 83% accuracy and an F1-score of 0.84. GPT-mini model exhibited a promising performance in generating actionable traffic management insights, with high ROUGE-L scores across conflict identification of 0.95, decision-making of 0.91, priority assignment of 0.94, and waiting time optimization of 0.92. This methodology confirmed LLMs' benefits as a traffic controller in real-world applications. We demonstrated that LLMs can offer precise recommendations to drivers in real-time including yielding, slowing, or stopping based on vehicle dynamics. This study demonstrates LLMs transformative potential for traffic control, enhancing efficiency and safety a

关键词： Street traffic control

A Blockchain-Based Secure Communication Framework For IOMT With Improved Security Via Hybrid Metaheuristic Models

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A Blockchain-Based Secure Communication Framework For IOMT W...

Recent Advances in science and engineering Technology (ICRASET), International Conference on

作者： Parikshit N. Mahalle V.P. Arul Kumar Ansh Kataria S Srividhya Sudhanshu Dev H Malathi Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India Department of Information Technology Karpagam Institute of Technology Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Sciences Biotechnology and Genetics JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Multiple healthcare devices are integrated into an IoMT network to enhance monitoring of patients and real-time treatment. These networks make crucial health care choices using many devices. To protect these devices and networks, researchers have used encoding, hashing, confidentiality safeguarding, attribute-based admittance controller, and more. However, continuous monitoring security frameworks are complicated or unchangeable. Present methods’ security relies on their interior setup, which is seldom extendable for new assaults. This explore recommends a hybrid metaheuristic approach for health care IoT safety. Changing encryption and hashing standards may dynamically rearrange the blockchain paradigm. EHO and GWO are used to continually optimize blockchain-based IoMT implementation security and QoS performance. In the proposed paradigm, dual fitness functions increase safety and QoS for numerous attacks. Fitness functions adjust encryption and hashing settings to enhance performance against various attacks. Blockchain-based deployment may be scaled and used in real time by integrating EH and GW optimization models for dynamic attack situations. The prototypical is evaluated beneath masquerade, Sybil, MITM, and DDoS assaults and associated to leading models.

关键词： Biological system modeling Computational modeling Metaheuristics Medical services Quality of service Throughput Real-time systems Safety Security Monitoring

Asymmetric Cross-Scale Alignment for Text-Based Person Search

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arXiv 2022年

作者： Ji, Zhong Hu, Junhua Liu, Deyin Wu, Lin Yuanbo Zhao, Ye The School of Electrical and Information Engineering Tianjin University Tianjin300072 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230039 China Department of Computer Science Swansea University SA1 8EN United Kingdom The Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Text-based person search (TBPS) is of significant importance in intelligent surveillance, which aims to retrieve pedestrian images with high semantic relevance to a given text description. This retrieval task is characterized with both modal heterogeneity and fine-grained matching. To implement this task, one needs to extract multi-scale features from both image and text domains, and then perform the cross-modal alignment. However, most existing approaches only consider the alignment confined at their individual scales, e.g., an image-sentence or a region-phrase scale. Such a strategy adopts the presumable alignment in feature extraction, while overlooking the cross-scale alignment, e.g., image-phrase. In this paper, we present a transformer-based model to extract multi-scale representations, and perform Asymmetric Cross-Scale Alignment (ACSA) to precisely align the two modalities. Specifically, ACSA consists of a global-level alignment module and an asymmetric cross-attention module, where the former aligns an image and texts on a global scale, and the latter applies the cross-attention mechanism to dynamically align the cross-modal entities in region/image-phrase scales. Extensive experiments on two benchmark datasets CUHK-PEDES and RSTPReid demonstrate the effectiveness of our approach. Codes are available at https://***/mulhjh/ACSA. Copyright © 2022, The Authors. All rights reserved.

关键词： Alignment