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Article
Automation & Control Systems
Adolfo Perrusquia et al.
Summary: This paper proposes an experience inference human-behavior learning method to solve the migration problem of optimal controllers applied to real-world nonlinear systems. The method is inspired by the complementary properties of the hippocampus, the neocortex, and the striatum learning systems in the brain. The hippocampus defines a physics informed reference model of the real-world nonlinear system for experience inference, and the neocortex is the adaptive dynamic programming (ADP) or reinforcement learning (RL) algorithm that ensures optimal performance of the reference model.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Automation & Control Systems
Chen Zhao et al.
Summary: This article examines a management architecture called decentralized/distributed autonomous operations/organizations (DAOs) that takes into account the human and social factors in transportation systems. Blockchain technology is used to ensure secure information exchange, mapping people's transportation needs from physical space to digital counterparts in cyberspace, ultimately creating the Internet of Minds (IoM). By incorporating consensus, community voting, and smart contracts into the organizational, coordination, and execution structure, reliable and prompt traffic decisions can be made using the federated intelligence of IoM. The article also provides details on operational procedures and key technologies, and showcases a case study on world model-driven cooperative signal control as a promising application of DAOs-based management in future transportation.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Guangyu Zhu et al.
Summary: In this paper, a new iterative adaptive dynamic programming algorithm called discrete-time time-varying policy iteration (DTTV) algorithm is developed for infinite horizon optimal control problems of discrete time-varying nonlinear systems. The algorithm updates the iterative value function to approximate the index function of optimal performance. The admissibility and convergence properties of the iterative control law are analyzed.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Engineering, Civil
Haifeng Lin et al.
Summary: Urban traffic congestion, especially at intersections, causes driving delays, exhaust emissions, and fuel wastage. This study presents a mathematical model for urban trunk traffic and proposes a new optimization method combining fuzzy control theory and adaptive sequencing mutation multi-objective differential evolution algorithm for traffic signal control at urban intersections. The simulation results demonstrate the effectiveness of the proposed model optimization algorithm.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jiangong Wang et al.
Summary: In this paper, a parallel training method based on artificial systems, computational experiments, and parallel execution is developed for the intelligent optimization and learning of autonomous vehicles in uncertain driving spaces. The method enhances the diversity of virtual scenarios to train intelligent vehicles to adapt to the uncertainties in real-world driving spaces. The paper proposes a standard operating procedure for intelligent driving systems and introduces digital quadruplets for parallel training. The parallel training approach establishes parallel and interactive virtual and real-world driving spaces, ultimately building a parallel driving system that fulfills safety, security, sustainability, sensitivity, service, and smartness.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Editorial Material
Automation & Control Systems
Fei-Yue Wang
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Automation & Control Systems
Xingxia Wang et al.
Summary: Recently, there have been intensive discussions and studies on Industry 5.0, capturing the attention of researchers, entrepreneurs, and policymakers worldwide. However, there is still no consensus on the reasons and definition of Industry 5.0. This paper provides a definition of Industry 5.0 based on its philosophical and historical origins and evolution, highlighting its focus on virtual-real duality and human-machine interaction. It introduces new theories and technologies, such as parallel intelligence, artificial societies, computational experiments, and cyber-physical-social systems, and emphasizes the potential and importance of Industry 5.0 in building smart societies.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Engineering, Civil
Arun Bala Subramaniyan et al.
Summary: This paper investigates the modeling of traffic delay prediction for multiple signalized intersections in Hawaii. It proposes a simple yet accurate hybrid modeling method that is suitable for real-time implementation in traffic control. The method outperforms existing models in terms of accuracy and computation efficiency.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Zhengshuai Wang et al.
Summary: In this article, a two-stage approach for traffic sign recognition is proposed, which utilizes prior knowledge and a novel lightweight superclass detector to improve performance. Experimental results demonstrate significant improvements in both processing speed and detection performance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Yiwen Wang et al.
Summary: This study introduces a deep learning-based method for traffic jam management using social media data. The method includes a multichannel network and keyword fuzzy matching, which can efficiently extract information about traffic jams from microblogs.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Junchen Jin et al.
Summary: This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, which uses Wasserstein Generative Adversarial Nets (WGAN) for data-driven traffic modeling. The proposed method combines generative and discriminative neural networks and captures spatial-temporal relations by stacking multiple neural networks. The results of the experiment demonstrate that this approach provides a scalable and effective traffic prediction solution for urban road networks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Zhishuai Li et al.
Summary: This paper proposes a multi-stream feature fusion approach for traffic flow prediction, which uses a data-driven adjacent matrix and utilizes multi-channel networks and soft-attention mechanism to extract and integrate features. Experimental results show that the proposed approach outperforms the state-of-the-art methods with acceptable time complexity.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Editorial Material
Computer Science, Cybernetics
Fei-Yue Wang et al.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Article
Engineering, Civil
Xiaoshuang Li et al.
Summary: This paper proposes a data augmented deep behavioral cloning (DADBC) method to imitate the problem-solving skills of traffic engineers, which utilizes a parallel learning (PL) framework that combines machine learning techniques to solve decision-making problems in traffic signal control. The method can learn traffic engineers' control schemes using the deep behavioral cloning (DBC) model, and has shown promising results in improving traffic efficiency in urban areas through real-world data validation from Hangzhou, China.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Yuanyuan Chen et al.
Summary: Accurate traffic prediction is an important and challenging task. In this study, an ensemble learning approach is proposed to improve the performance of traffic flow prediction by stacking multiple predictions based on dynamic traffic conditions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Antonio Torralba et al.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Engineering, Electrical & Electronic
Chao Huang et al.
Summary: This article proposes a human-machine adaptive shared control method for automated vehicles (AVs) to compensate for automation performance degradation by decreasing control authority allocated to the automation system and adaptively increasing driver engagement to ensure vehicle safety. Experimental validation demonstrates the effectiveness of this approach in different driving scenarios.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Engineering, Electrical & Electronic
Tianqi Zhang et al.
Summary: This article investigates a traffic-flow forecasting problem using long-short term memory (LSTM) and proposes a novel graph-attention LSTM structure to capture spatiotemporal dependencies among nodes in a road network. The proposed method outperforms existing baselines in two real-world datasets.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Computer Science, Information Systems
Mingtian Shao et al.
Summary: This paper proposes a self-deployed execution environment (SDEE) for HPC, which combines the advantages of traditional virtualization and modern containers. SDEE provides an isolated and customizable environment for users to develop, debug, and deploy applications. Experiments show that the overhead introduced by SDEE is negligible.
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING
(2022)
Article
Engineering, Civil
Zhili Zhou et al.
Summary: The study focuses on utilizing blockchain technology to address security issues in Intelligent Autonomous Transport Systems and enhance intelligent development in logistics transportation. By employing Ethereum as the underlying blockchain, sensitive information is securely recorded to ensure data integrity, while the LightGBM algorithm is used for vehicle and cargo matching. Results show high security prediction accuracy and superior performance of the proposed algorithm compared to others, demonstrating the potential for blockchain-based IATS in intelligent logistics transportation development.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Parth Kothari et al.
Summary: This study explores the development of human trajectory forecasting, comparing handcrafted representations with deep learning methods, and proposing two data-driven approaches to effectively capture social interactions. By establishing the TrajNet++ benchmark and introducing new performance metrics, the superiority of the proposed method on real-world and synthetic datasets is validated.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Junchen Jin et al.
Summary: This paper introduces a parallel recommendation engine, PRECOM, for traffic control operations in metropolitan areas to alleviate road congestion. With expert knowledge incorporated, the system includes three conceptual components and three algorithmic steps, and has been tested in Hangzhou, China with promising results supporting traffic control practices.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Zhishuai Li et al.
Summary: This paper proposes a methodological framework to handle large-scale cellular network data and discover trip purposes in an unsupervised way. The framework includes heuristic rules to identify home/work purposes and a flexible latent Dirichlet allocation (LDA) model to discover activities for remaining trips. The experimental results show that the proposed method can identify diverse trip purposes effectively.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Jaeyoung Lee et al.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Engineering, Electrical & Electronic
Yunyang Shi et al.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Engineering, Electrical & Electronic
Chen Zhao et al.
Summary: This article provides an overview of parallel transportation management and control, discussing its principles, applications, challenges, and emerging opportunities. Additionally, it proposes a transportation foundation model based on parallel learning and federated intelligence as a potential path for future development.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Computer Science, Information Systems
Xingyuan Dai et al.
Summary: Traffic signal control is shifting towards proactive control, requiring an effective prediction model for controllers. This paper proposes a learning-based traffic world model that describes traffic states in image form and generates planning data for control policy optimization. Experimental results show that the model provides accurate prediction and outperforms baseline methods in optimized control policy.
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Weizhi Qiu et al.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Engineering, Electrical & Electronic
Tong Wang et al.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Editorial Material
Computer Science, Information Systems
Ruizhi Liao et al.
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING
(2022)
Article
Computer Science, Information Systems
Fei-Yue Wang et al.
Summary: This paper aims to illustrate the concept of mutually trustworthy human-machine knowledge automation as the technical mechanism of hybrid augmented intelligence based complex system cognition, management, and control. It describes the historical development of complex system science and analyzes the limitations of human intelligence and machine intelligence. The need for using human-machine HAI in complex systems is explained in detail. The proposed concept of mutually trustworthy HM-KA mechanism is demonstrated using an example of corrective control in bulk power grid dispatch.
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Jiawei Xu et al.
Summary: Studying drivers' eye movement and driving operation behavior when they encounter traffic rule violators is crucial for safe driving. Novice drivers tend to ignore their vehicle position and may collide with other road users. In emergent situations, they can only perform either steering or braking, lacking the ability to combine both like experienced drivers do. Furthermore, experienced drivers allocate less time to looking and more time to scanning their surroundings when faced with higher driving difficulty. This study reveals the differences between novice and experienced drivers, providing valuable reference for future advanced driving assistance systems.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2022)
Article
Computer Science, Artificial Intelligence
Zhengqiu Zhu et al.
Summary: This article introduces the application of blockchain technology in vehicular crowdsensing (VCS) systems, addressing the problems faced by centralized VCS frameworks and presenting potential directions for future research.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2022)
Article
Computer Science, Artificial Intelligence
Markus Roth et al.
Summary: In this study, a novel method for predicting vehicle-pedestrian paths considering the awareness of the driver and the pedestrian towards each other is presented. The method utilizes a Dynamic Bayesian Network model to jointly predict the paths of the vehicle and the pedestrian, taking into account the environment and entity-specific context cues. The results show that context-aware models outperform context-agnostic models in path prediction for scenarios with a dynamics change, and driver attention-aware models improve collision risk estimation compared to driver-agnostic models.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2022)
Article
Computer Science, Artificial Intelligence
Zhiyang Ju et al.
Summary: This article reviews recent advances in attack/anomaly detection and resilience strategies for connected and automated vehicles (CAVs) from a vehicle dynamics and control perspective. It summarizes the existing results on attack/anomaly detection and resilience in control frameworks, categorizing them based on the positions of the attacks/anomalies. The article also identifies potential research directions and challenges for future studies.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2022)
Article
Computer Science, Artificial Intelligence
Victor Fors et al.
Summary: An approach that utilizes model predictive control to plan and control autonomous vehicles in multi-vehicle traffic scenarios is proposed. This method improves robustness by generating adversarial traffic predictions and assumes no ill intent from other agents. Simulation results demonstrate that the proposed method can handle complex traffic situations in real-time and outperforms state-of-the-art reinforcement-learning approaches.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2022)
Article
Automation & Control Systems
Junchen Jin et al.
Summary: This article proposes a human-in-the-loop recommendation system for strategic urban traffic management, which utilizes a multiagent design to generate customized operational schemes at different levels of traffic control objects.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Zhenyi Zhang et al.
Summary: This paper proposes a novel two-layer reinforcement learning behavioral control method to reduce dependence on human intelligence by trial-and-error learning. The upper layer uses a reinforcement learning mission supervisor to learn the optimal mission priority and improves the dynamic performance of mission priority adjustment. The lower layer uses a reinforcement learning controller to learn the optimal control policy and reduces the control cost of mission priority adjustment.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Automation & Control Systems
Fei-Yue Wang
Summary: This paper presents an investigation and outline of MetaControl and DeControl in Metaverses for control intelligence and knowledge automation. It proposes prescriptive control with prescriptive knowledge and parallel philosophy as the starting point for the new control philosophy and technology. The paper argues that circular causality should be adapted as the fundamental principle for control and management of metasystems in metaverses, and suggests an interdisciplinary approach for MetaControl and DeControl based on five control metaverses.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Automation & Control Systems
Wenzhang Liu et al.
Summary: In multi-agent reinforcement learning, the behaviors of each agent can influence the learning of others, making it challenging to explore in the environment. This paper proposes a new approach by transferring knowledge across tasks to improve the exploring efficiency and performance of MARL tasks.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Chen Zhao et al.
Summary: This paper presents a Transformer-based cooperation mechanism for controlling large-scale traffic networks. By considering dynamic modeling and scale requirements, as well as designing relative position encoding, this mechanism can better describe traffic conditions and effectively utilize spatial-temporal correlations to improve traffic control efficiency.
2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Aso Validi et al.
Summary: This paper investigates the application of hybrid on/off-blockchain vehicle data management approaches in the transport of personal protective equipment and proposes a three-step mechanism and a workflow model to process, simulate, and store/visualize aggregated vehicle datasets. By implementing a hybrid blockchain platform based on Hyperledger Fabric and Gluster file systems, the results demonstrate the efficiency, stability, and ability of the on/off-blockchain mechanisms to meet the platform quality of service requirements.
2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
(2022)
Article
Computer Science, Artificial Intelligence
Chen Zhao et al.
Summary: Smart cities require intelligent transportation systems for smart mobility. This article introduces TengYun, a transportation foundation model designed for the transportation metaverse called TransVerse. TengYun supports decentralized autonomous organizations and various federated technologies. The operating procedure of TengYun is illustrated through an example of a federation of transportation transformers.
IEEE INTELLIGENT SYSTEMS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Yu Du et al.
Summary: This paper introduces a novel method for open-vocabulary object detection based on a pre-trained vision-language model. The method learns continuous prompt representations to improve detection performance. It incorporates a background interpretation scheme and a context grading scheme, and experiments show that it outperforms the baseline method on various datasets.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2022)
Review
Computer Science, Artificial Intelligence
Yining Ma et al.
Summary: This paper reviews various verification and validation approaches for the decision-making and planning system of automated vehicles, dividing them into scenario-based testing, fault injection testing, and formal verification. It proposes six criteria for comparing and evaluating these approaches, and matches suitable methods to verify and validate each module based on their functional requirements. Finally, it concludes with current challenges and future research directions.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2022)
Article
Computer Science, Artificial Intelligence
Yonglin Tian et al.
Summary: This paper reviews the importance of cooperative computing in autonomous driving, focusing on the challenges of multi-modal data and privacy protection. It introduces the concepts of Transformers and federated learning, and proposes a hierarchical structure of Transformers for intelligent vehicles to achieve privacy-preserving collaboration.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2022)
Article
Computer Science, Artificial Intelligence
Xuan Li et al.
Summary: This article introduces the theoretical framework of scenarios engineering for building trustworthy AI techniques. It proposes six key dimensions, such as intelligence and index, calibration and certification, and verification and validation, to achieve more robust and trusting AI.
IEEE INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiao Wang et al.
Summary: This article reviews the development and changes in the field of cyber-physical-social systems (CPSS) over the past decade since 2010. It focuses on the comparison between digital twins in CPS and parallel intelligence in CPSS, and discusses their relationship with blockchain intelligence, smart contracts, metaverses, DAO, Web3, and decentralized science. The concept of DeMetaverses is introduced as a decentralized autonomous metaverse based on DAO. The characteristics, mechanism, and impact of DeMetaverses are explored, envisioning an integrated human, artificial, natural, and organizational intelligence in 6S societies.
IEEE INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Fei-Yue Wang et al.
Summary: In the last decade, there have been remarkable achievements in automated driving technology. However, the safety of automated vehicles is still not fully guaranteed. Therefore, testing and improving the safety of automated vehicles have become increasingly important. This article briefly introduces China's current efforts in verification and validation of safety and capability for intelligent vehicles.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2022)
Article
Computer Science, Artificial Intelligence
Dongfang Yang et al.
Summary: This study presents a novel neural network architecture to predict pedestrian crossing intention by fusing different spatio-temporal features. By optimally combining various phenomena such as RGB imagery sequences, semantic segmentation masks, and ego-vehicle speed, the proposed method achieves state-of-the-art performance.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2022)
Article
Computer Science, Information Systems
Juanjuan Li et al.
Summary: This study proposes a model based on the Generalized Second Price (GSP) auction mechanism to tackle the issues of instability and low efficiency in Bitcoin confirmation games. Computational experiments validate the superiority of this mechanism over the currently adopted Generalized First Price (GFP) mechanism, while also demonstrating the convergence of the game under the Instant Balanced (IB) strategy.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Zikang Wang et al.
Summary: Multihop knowledge reasoning is a fundamental and important task to find missing entities for incomplete triples by finding paths on knowledge graphs. In this article, a hierarchical reinforcement learning algorithm is devised to model the reasoning process effectively. By incorporating a high-level reasoning layer to handle abstract concepts and guiding the low-level reasoning process for concrete entities and relations, the proposed approach achieves competitive results on link prediction tasks and demonstrates the effectiveness of the hierarchical structure.
IEEE INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Dongpu Cao et al.
Summary: This is the brief report of the first IEEE Distributed/Decentralized Hybrid Workshop on Future Directions of Intelligent Vehicles. The workshop addressed various issues related to intelligent vehicles and potential topics for future research and development.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2022)
Article
Business
Juanjuan Li et al.
Summary: This paper investigates the priority rule for queuing transactions in the Bitcoin system based on associated fees and users' strategies in the transaction confirmation game. Through game-theoretical modeling and empirical studies, it is found that users' fee decisions are significantly influenced by waiting time and unit time cost.
ELECTRONIC COMMERCE RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Kewei Cheng et al.
Summary: This article introduces a privacy-preserving tree-boosting system called SecureBoost based on federated learning, which allows joint learning processes without violating user privacy. SecureBoost provides the same level of accuracy as non-privacy-preserving methods while keeping private data providers' information confidential.
IEEE INTELLIGENT SYSTEMS
(2021)
Article
Automation & Control Systems
Dong Huang et al.
Summary: Ensemble clustering based on fast propagation of cluster-wise similarities via random walks addresses the issues of lack of information at higher levels of granularity and neglect of multiscale relationships in current ensemble clustering research. By constructing a cluster similarity graph and conducting random walks, a new cluster-wise similarity matrix is derived to achieve an enhanced co-association matrix. The proposed approach demonstrates effectiveness and efficiency through extensive experiments on various real-world datasets.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Enrique Herrera-Viedma et al.
Summary: The article provides an overview of fuzzy and linguistic decision-making trends, studies, methodologies, and models developed in the last 50 years. It discusses core decision-making frameworks and new complex decision-making frameworks that have emerged in recent years. The challenges associated with these frameworks and key guidelines for future research in the field are highlighted.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Review
Automation & Control Systems
Giancarlo Fortino et al.
Summary: The Internet of Things (IoT) demands innovative and evolutionary approaches to tackle its multifaceted aspects, with a plethora of methodologies, frameworks, platforms, and tools proposed over the years creating a high entry-barrier to IoT system engineering. This survey aims to aid IoT developers by providing baseline definitions and reviewing seventy relevant products through a comparative approach, in order to reduce confusion and simplify the approach to IoT system engineering.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Engineering, Civil
Junchen Jin et al.
Summary: The paradigm shift towards agile and adaptive traffic signal control powered by Big Data and IoT technologies enables fine-tuning of signal timing parameters based on real-time traffic information. Traffic engineers adjust hyperparameters to improve traffic efficiency as traffic patterns evolve.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Civil
Zhihan Lv et al.
Summary: This study utilizes big data analysis technology to improve electric vehicle transportation networks by enhancing algorithms and simulating the network, effectively reducing data transmission performance delay and suppressing congestion spread, providing experimental references for the development of electric vehicle transportation networks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Review
Automation & Control Systems
Dezhen Xiong et al.
Summary: This paper examines the role of deep learning in decoding EMG signals for human-machine interaction applications. It reviews recent advancements in network structures, processing schemes, and tasks like movement classification and joint angle prediction. The discussion also includes new challenges, such as multimodal sensing and robustness towards disturbances, and presents potential future research directions.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Review
Automation & Control Systems
Dan Zhang et al.
Summary: Multi-agent systems (MASs) have a wide range of applications in smart grids, smart manufacturing, sensor networks, and intelligent transportation systems, but the coordination and cooperation performance are affected by information interaction. Unexpected physical faults and cyber attacks on a single agent may lead to severe degradation of the whole system performance and even destruction.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Automation & Control Systems
Qiyue Wang et al.
Summary: This paper introduces an innovative investigation on prototyping a digital twin as a platform for human-robot interactive welding and welder behavior analysis. The study shows that the human-robot interaction working style and data-driven welder behavior analysis can enhance operational productivity and accelerate novice welder training.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Automation & Control Systems
Huimin Lu et al.
Summary: BGP is a crucial infrastructure of the Internet vulnerable to security issues. Current solutions based on PKI have high risks, leading to the proposal of DRRS-BC, a decentralized blockchain-based system to address authentication and security concerns, while also defending against various attacks.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Automation & Control Systems
Yaguang Lin et al.
Summary: Mobile social networks provide real-time information services but face challenges of rumors and fraud, requiring control strategies to minimize individual losses. Research uses optimal control theory and dynamic allocation to inhibit fraud information diffusion and reduce total cost.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Engineering, Multidisciplinary
PeiYun Zhang et al.
Summary: This research presents a performance-optimized consensus mechanism based on node classification, which enhances the throughput and fault tolerance of the blockchain system by dividing nodes into different categories based on trust values. Experimental results demonstrate that the proposed mechanism outperforms popular methods in terms of throughput, consumption, and fault tolerance, thus advancing the field of consortium blockchains.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Automation & Control Systems
Kunhua Liu et al.
Summary: Researchers proposed a novel generative adversarial network (GAN) for foggy image semantic segmentation, which consists of two parts that aim to extract and express texture, achieving state-of-the-art performance in experiments on foggy cityscapes datasets and foggy driving datasets.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Engineering, Electrical & Electronic
Zhenxing Yao et al.
Summary: Existing research on travel behavior detection based on cellphone data mainly focuses on algorithm exploration and evaluation, and existing platforms have limited functions. This paper proposes a real-time urban mobility monitoring and traffic management platform using cellular data, with a field case study conducted in Guiyang.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2021)
Article
Computer Science, Artificial Intelligence
Jian Nie et al.
Summary: The paper introduces a multimodality fusion method based on deep neural networks, proposing the IMF-DNN framework for object detection and driving policy prediction. Additionally, a DNN safety test strategy is presented to analyze the robustness and generalization ability of the model systematically.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2021)
Article
Computer Science, Artificial Intelligence
Abderrahim Kasmi et al.
Summary: An end-to-end ego-localization framework is introduced in this work with two main novelties: a complete solution tackling every part of ego-localization, and an information-driven approach using road structure prior from digital maps. Fusion framework techniques based on Bayesian Network and Hidden Markov Model are elaborated to enhance robustness to erroneous sensor data.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2021)
Article
Computer Science, Artificial Intelligence
Paul Watta et al.
Summary: A pre-crash detection and warning system in a host vehicle requires accurate determination of the position of each remote vehicle in its vicinity and the context of the driving environment. V2V communication emerges as a promising technology to enhance vehicle-resident sensors, addressing crash scenarios with improved warning timing. The Geo+NN system, utilizing neural network and geometric modeling, effectively detects and predicts remote vehicles within the context of 8 different positions based on real-world V2V signals.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2021)
Article
Computer Science, Artificial Intelligence
Mahdi Morsali et al.
Summary: This paper proposes a two-step procedure for efficient trajectory planning of autonomous vehicles in complex traffic scenarios. It first uses a support vector machine (SVM) to represent the surrounding environment and classify the search space, and then uses an A* algorithm in a state space lattice for path and velocity planning. By introducing a heuristic method and pruning technique, the search efficiency is significantly improved.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2021)
Article
Engineering, Electrical & Electronic
Monireh Abdoos
Summary: Agent-based systems are widely used in urban traffic network modeling, with cooperation being an important feature, especially in dynamic complex networks. Game theory can be employed in interactive decision making for MASs. The focus of this article is on developing traffic signal controllers for multiple intersections using MASs, with a two-mode agent architecture proposed for effective control of traffic congestion through independent and cooperative procedures.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2021)
Article
Computer Science, Artificial Intelligence
Dingqi Yang et al.
Summary: The article explores the unique characteristics of location-centric social media data, including spatial, temporal, semantic, and social dimensions, and emphasizes three key challenges in data analytics. It also discusses the opportunities of leveraging this data for urban analytics and smart city development, including data analysis within and across the four data dimensions.
IEEE INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Markus Koschi et al.
Summary: The proposed formal set-based prediction method in this work analyzes the future behaviors of vehicles, pedestrians, and cyclists, ensuring that autonomous vehicles will not cause accidents in complex traffic scenarios.
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