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Generalized Model and Deep Reinforcement Learning-Based Evolutionary Method for Multitype Satellite Observation Scheduling

Song, Yanjie (author), Ou, Junwei (author), Pedrycz, Witold (author), Suganthan, Ponnuthurai Nagaratnam (author), Wang, X. (author), Xing, Lining (author), Zhang, Yue (author)

Multitype satellite observation, including optical observation satellites, synthetic aperture radar (SAR) satellites, and electromagnetic satellites, has become an important direction in integrated satellite applications due to its ability to cope with various complex situations. In the multitype satellite observation scheduling problem ...

journal article 2024

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Model and Control of R-Soft Inverted Pendulum

Caradonna, Daniele (author), Pierallini, M. (author), Della Santina, C. (author), Angelini, Franco (author), Bicchi, Antonio (author)

Soft robots enable safe and robust operations in unstructured environments. However, the nonlinearities of their continuum structure complicate the accomplishment of classic robotic tasks, such as pick and place. In this work, we propose the R-Soft Inverted Pendulum, a Soft Inverted Pendulum (SIP) actuated only by a revolute joint at the base...

journal article 2024

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Classifying Human Manual Control Behavior Using LSTM Recurrent Neural Networks

Versteeg, Rogier (author), Pool, D.M. (author), Mulder, Max (author)

This article discusses a long short-term memory (LSTM) recurrent neural network that uses raw time-domain data obtained in compensatory tracking tasks as input features for classifying (the adaptation of) human manual control with single- and double-integrator controlled element dynamics. Data from two different experiments were used to train...

journal article 2024

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Safe and Adaptive 3-D Locomotion via Constrained Task-Space Imitation Learning

Ding, J. (author), Lam, Tin Lun (author), Ge, Ligang (author), Pang, Jianxin (author), Huang, Yanlong (author)

Bipedal locomotion has been widely studied in recent years, where passive safety (i.e., a biped rapidly brakes without falling) is deemed to be a pivotal problem. To realize safe 3-D walking, existing works resort to nonlinear optimization techniques based on simplified dynamics models, requiring hand-tuned reference trajectories. In this...

journal article 2023

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Weakly-supervised Learning for Fine-grained Emotion Recognition using Physiological Signals

Zhang, T. (author), El Ali, Abdallah (author), Wang, Chen (author), Hanjalic, A. (author), Cesar, Pablo (author)

Instead of predicting just one emotion for one activity (e.g., video watching), fine-grained emotion recognition enables more temporally precise recognition. Previous works on fine-grained emotion recognition require segment-by-segment, fine-grained emotion labels to train the recognition algorithm. However, experiments to collect these...

journal article 2023

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DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features

Li, Wanda (author), Xu, Zhiwei (author), Sun, Yi (author), Gong, Qingyuan (author), Chen, Y. (author), Ding, Aaron Yi (author), Wang, Xin (author), Hui, Pan (author)

Outstanding users (OUs) denote the influential, 'core' or 'bridge' users in online social networks. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on...

journal article 2023

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Energy-Efficient Routing of a Multirobot Station: A Flexible Time-Space Network Approach

Xin, Jianbin (author), Meng, Chuang (author), D'Ariano, Andrea (author), Schulte, F. (author), Peng, Jinzhu (author), Negenborn, R.R. (author)

This paper investigates a novel routing problem of a multi-robot station in a manufacturing cell. In the existing literature, the objective is to minimize the cycle time or energy consumption separately. The routing problem considered in this paper aims to reduce the cycle time and energy consumption jointly for each robot while avoiding...

journal article 2023

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Model Predictive Path Planning of AGVs: Mixed Logical Dynamical Formulation and Distributed Coordination

Xin, Jianbin (author), Wu, Xuwen (author), D'Ariano, Andrea (author), Negenborn, R.R. (author), Zhang, Fangfang (author)

Most of the existing path planning methods of automated guided vehicles (AGVs) are static. This paper proposes a new methodology for the path planning of a fleet of AGVs to improve the flexibility, robustness, and scalability of the AGV system. We mathematically describe the transport process as a dynamical system using an ad hoc mixed...

journal article 2023

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A Cost-Sensitive Machine Learning Model With Multitask Learning for Intrusion Detection in IoT

Telikani, Akbar (author), Rudbardeh, Nima Esmi (author), Soleymanpour, Shiva (author), Shahbahrami, Asadollah (author), Shen, Jun (author), Gaydadjiev, G. (author), Hassanpour, Reza (author)

A problem with machine learning (ML) techniques for detecting intrusions in the Internet of Things (IoT) is that they are ineffective in the detection of low-frequency intrusions. In addition, as ML models are trained using specific attack categories, they cannot recognize unknown attacks. This article integrates strategies of cost-sensitive...

journal article 2023

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To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration

Ottati, F. (author), Gao, Chang (author), Chen, Qinyu (author), Brignone, Giovanni (author), Casu, Mario R. (author), Eshraghian, Jason K. (author), Lavagno, Luciano (author)

As deep learning models scale, they become increasingly competitive from domains spanning from computer vision to natural language processing; however, this happens at the expense of efficiency since they require increasingly more memory and computing power. The power efficiency of the biological brain outperforms any large-scale deep...

journal article 2023

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Improving Pedestrian Prediction Models with Self-Supervised Continual Learning

Knödler, L. (author), Salmi, C. (author), Zhu, H. (author), Ferreira de Brito, B.F. (author), Alonso-Mora, J. (author)

Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This paper introduces a self-supervised continual learning framework to improve data-driven pedestrian prediction models online across various scenarios continuously. In...

journal article 2022

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Social-aware Federated Learning: Challenges and Opportunities in Collaborative Data Training

Ottun, Abdul Rasheed (author), Mane, Pramod C. (author), Yin, Zhigang (author), Paul, Souvik (author), Liyanage, Mohan (author), Pridmore, Jason (author), Ding, Aaron Yi (author), Sharma, Rajesh (author), Nurmi, Petteri (author), Flores, Huber (author)

Federated learning (FL) is a promising privacy-preserving solution to build powerful AI models. In many FL scenarios, such as healthcare or smart city monitoring, the user's devices may lack the required capabilities to collect suitable data, which limits their contributions to the global model. We contribute social-aware federated learning...

journal article 2022

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Streaming Graph Embeddings via Incremental Neighborhood Sketching

Yang, Dingqi (author), Qu, Bingqing (author), Yang, J. (author), Wang, Liang (author), Cudre-Mauroux, Philipe (author)

Graph embeddings have become a key paradigm to learn node representations and facilitate downstream graph analysis tasks. Many real-world scenarios such as online social networks and communication networks involve streaming graphs, where edges connecting nodes are continuously received in a streaming manner, making the underlying graph...

journal article 2022

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Free Energy Principle for the Noise Smoothness Estimation of Linear Systems with Colored Noise

Anil Meera, A. (author), Wisse, M. (author)

The free energy principle (FEP) from neuroscience provides a framework called active inference for the joint estimation and control of state space systems, subjected to colored noise. However, the active inference community has been challenged with the critical task of manually tuning the noise smoothness parameter. To solve this problem, we...

conference paper 2022

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MuCoMiD: A Multitask graph Convolutional Learning Framework for miRNA-Disease Association Prediction

Dong, Thi Ngan (author), Mucke, Stefanie (author), Khosla, M. (author)

Growing evidence from recent studies implies that microRNAs or miRNAs could serve as biomarkers in various complex human diseases. Since wet-lab experiments for detecting miRNAs associated with a disease are expensive and time-consuming, machine learning techniques for miRNA-disease association prediction have attracted much attention in...

journal article 2022

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Efficient Online Globalized Dual Heuristic Programming With an Associated Dual Network

Zhou, Y. (author)

Globalized dual heuristic programming (GDHP) is the most comprehensive adaptive critic design, which employs its critic to minimize the error with respect to both the cost-to-go and its derivatives simultaneously. Its implementation, however, confronts a dilemma of either introducing more computational load by explicitly calculating the...

journal article 2022

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Task-Aware Connectivity Learning for Incoming Nodes Over Growing Graphs

Das, B. (author), Hanjalic, A. (author), Isufi, E. (author)

Data processing over graphs is usually done on graphs of fixed size. However, graphs often grow with new nodes arriving over time. Knowing the connectivity information of these nodes, and thus, the expanded graph is crucial for processing data over the expanded graph. In its absence, its inference and the subsequent data processing become...

journal article 2022

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Mean Field Behavior of Collaborative Multiagent Foragers

Jarne Ornia, D. (author), Zufiria, Pedro J. (author), Mazo, M. (author)

Collaborative multiagent robotic systems, where agents coordinate by modifying a shared environment often result in undesired dynamical couplings that complicate the analysis and experiments when solving a specific problem or task. Simultaneously, biologically inspired robotics rely on simplifying agents and increasing their number to obtain...

journal article 2022

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Using Eye-Tracking Data to Predict Situation Awareness in Real Time During Takeover Transitions in Conditionally Automated Driving

Zhou, Feng (author), Yang, X. Jessie (author), de Winter, J.C.F. (author)

Situation awareness (SA) is critical to improving takeover performance during the transition period from automated driving to manual driving. Although many studies measured SA during or after the driving task, few studies have attempted to predict SA in real time in automated driving. In this work, we propose to predict SA during the takeover...

journal article 2022

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MPC-based Haptic Shared Steering System: A Driver Modeling Approach for Symbiotic Driving

Rios Lazcano, A.M. (author), Niu, Tenghao (author), Carrera Akutain, Xabier (author), Cole, David (author), Shyrokau, B. (author)

Advanced Driver Assistance Systems (ADAS) aim to increase safety and reduce mental workload. However, the gap in the understanding of the closed-loop driver-vehicle interaction often leads to reduced user acceptance. In this study, an optimal torque control law is calculated online in the Model Predictive Control (MPC) framework to guarantee...

journal article 2021