Event detection is an important task in the field of natural language processing, which aims to detect trigger words in a sentence and classify them into specific event types. Event detection tasks suffer from data sparsity and event instances imbala...
Using sensors embedded in smartphones to study earthquake early warning (EEW) technology can effectively reduce the high construction and maintenance costs of traditional EEW systems. However, due to the impact of human activities, it is very difficu...
This paper presents an Implicit Neural Representation method for Event-Based Imaging Velocimetry (INR-VG) to reconstruct dense velocity fields from sparse event streams. The core idea is to learn a mapping (multilayer perceptron) from spatial coordin...
An event camera generates an event stream based on changes in brightness, retaining only the characteristics of moving objects, and addresses the high power consumption associated with using high-frame-rate cameras for high-speed eye-tracking tasks....
Recently, the neural network control has been widely used in the field of wastewater treatment process (WWTP). However, most neural network (NN) control methods are time-driven, with a large number of transmissions and a large amount of neural networ...
We propose using derivative features for sound event detection based on deep neural networks. As input to the networks, we used log-mel-filterbank and its first and second derivative features for each frame of the audio signal. Two deep neural networ...
This paper is concerned with the problem of event-triggered state estimation for a class of fractional-order neural networks. An event-triggering strategy is proposed to reduce the transmission frequency of the output measurement signals with guarant...
The quasi-synchronization for a class of general heterogeneous neural networks is explored by event-based impulsive control strategy. Compared with the traditional average impulsive interval (AII) method, instead, an event-triggered mechanism (ETM) i...
Predicting the future parking locations and durations of private vehicles using vehicular edge devices is critical for real-time intelligent transportation services, ranging from instant point-of-interest recommendations to dynamic route planning. Ad...
In recent years, spiking neural networks (SNNs) have attracted increasingly more researchers to study by virtue of its bio-interpretability and low-power computing. The SNN simulator is an essential tool to accomplish image classification, recognitio...
Driving event detection and driver behavior recognition have been widely explored for many purposes, including detecting distractions, classifying driver actions, detecting kidnappings, pricing vehicle insurance, evaluating eco-driving, and managing...
Sound event detection tasks are rapidly advancing in the field of pattern recognition, and deep learning methods are particularly well suited for such tasks. One of the important directions in this field is to detect the sounds of emotional events ar...
Sound event detection (SED) is of great practical and research significance owing to its wide range of applications. However, due to the heavy reliance on dataset size for task performance, there is often a severe lack of data in real-world scenarios...
This paper is concerned with the event-based synchronization control for chaotic neural networks by using a refined discontinuous trigger scheme. To get rid of the Zeno phenomenon and decrease the triggering times, a refined discontinuous event-trigg...
This paper focuses on the event-triggered synchronization of coupled neural networks with reaction–diffusion terms. At first, an effective event-triggered controller was designed based on time sampling. It is worth noting that the data of the c...
This paper addresses the issue of course keeping control (CKC) for unmanned surface vehicles (USVs) under network environments, where various challenges, such as network resource constraints and discontinuities of course and yaw caused by data transm...
To solve the trajectory tracking control problem for a class of nonlinear systems with time-varying parameter uncertainties and unknown control directions, this paper proposed a neural sliding mode control strategy with prescribed performance against...
The advantages of an event camera, such as low power consumption, large dynamic range, and low data redundancy, enable it to shine in extreme environments where traditional image sensors are not competent, especially in high-speed moving target captu...
Drainage tunnel stability is crucial for engineering project safety (e.g., mine engineering and dams), and rockfall events and water release are key indicators of drainage tunnel stability. To address this, we developed a monitoring system to simulat...
Motion deblurring is an essential capability for high-speed vision applications such as autonomous driving and unmanned aerial vehicles. Existing frame-based deblurring methods often struggle with rapid motion, whereas event cameras offer a promising...
Event Detection (ED), which aims to identify trigger words from the given text and classify them into corresponding event types, is an important task in Natural Language Processing (NLP); it contributes to several downstream tasks and is beneficial f...
Audio event detection (AED) is a task of recognizing the types of audio events in an audio stream and estimating their temporal positions. AED is typically based on fully supervised approaches, requiring strong labels including both the presence and...
For the extraction of judicial events for Tibetan, a low-resource language, traditional simple neural network approaches struggle to adequately capture the deep semantics and features of the texts because Tibetan texts are usually lengthy and contain...
Spiking neural networks (SNNs) have emerged as a promising model for energy-efficient, event-driven processing of asynchronous event streams from Dynamic Vision Sensors (DVSs), a class of neuromorphic image sensors with microsecond-level latency and...
Wireless sensor and robot networks (WSRNs) often work in complex and dangerous environments that are subject to many constraints. For obtaining a better monitoring performance, it is necessary to deploy different types of sensors for various complex...
For processing streaming events from a Dynamic Vision Sensor camera, two types of neural networks can be considered. One are spiking neural networks, where simple spike-based computation is suitable for low-power consumption, but the discontinuity in...
Railroads are a critical part of the United States’ transportation sector. Over 40 percent (by weight) of the nation’s freight is transported by rail, and according to the Bureau of Transportation statistics, railroads moved $186.5 billio...
This study examines reservoir-triggered seismicity (RTS) in Poland and Vietnam. The current state of individual RTS seismic networks necessitates detecting earthquakes from only a few stations. The number of P waves is often inadequate for phase asso...
In cognitive neuroscience research, computational models of event-related potentials (ERP) can provide a means of developing explanatory hypotheses for the observed waveforms. However, researchers trained in cognitive neurosciences may face technical...
This work addresses the automatic berthing control problem of underactuated ships under false data injection (FDI) attack, and an event-sampled automatic berthing control scheme is proposed. To avoid the FDI attack signals from entering the closed-lo...
This paper presents an event identification process in complementary feature extractions via convolutional neural network (CNN)-based event classification. The CNN is a suitable deep learning technique for addressing the two-dimensional power system...
The use of a stochastic artificial neural network (SANN) implemented on a Field Programmable Gate Array (FPGA) provides a promising method of performing image recognition on event camera recordings, however, challenges exist due to the fact that even...
This paper investigates dynamic event-triggered control (DETC) and encoding–decoding schemes to achieve the synchronization of T-S fuzzy neural networks (FNNs). DETC allows the transmission signals to be controlled aperiodically during the actu...
A class of heterogeneous second-order multi-agent consensus problems is studied, in which an event-triggered method is used to improve the feasibility of the control protocol. The sliding mode control method is used to achieve the robustness of the s...
Event detection is an important subtask of information extraction, aiming to identify triggers and recognize event types in text. Previous state-of-the-art studies using graph neural networks (GNNs) are mainly applied to obtain long distance features...
This paper introduces a new finding regarding single event upsets (SEUs) in configuration memory, and their potential impact on enhancing the performance of deep neural networks (DNNs) on the multiprocessor system on chip (MPSoC) platform. Traditiona...
Federated learning enables collaborative model training without centralizing private data, yet its deployment on edge devices remains constrained by communication overhead, heterogeneous data distributions, and privacy leakage from shared model updat...
This paper proposes a sound event detection (SED) method in tunnels to prevent further uncontrollable accidents. Tunnel accidents are accompanied by crashes and tire skids, which usually produce abnormal sounds. Since the tunnel environment always ha...
In this paper, the fixed-time and preassigned-time synchronization issues of fully quaternion-valued fuzzy memristive neural networks are studied based on the dynamic event-triggered control mechanism. Initially, the fuzzy rules are defined within th...
This paper studies the event-triggered adaptive tracking control problem of a class of pure-feedback systems. Via the backstepping method and the neural network approximation with the central symmetric distribution, an event-triggered adaptive neural...
As the penetration of renewable energy sources (RESs) increases, the rate of conventional generators and the power system inertia are reduced accordingly, resulting in frequency-stability concerns. As one of the solutions, the battery-type energy sto...
Parking is a crucial element in urban mobility management. The availability of parking areas makes it easier to use a service, determining its success. Proper parking management allows economic operators located nearby to increase their business reve...
Operational production planning has become a key factor in a company’s competitiveness, enabling efficient resource management and a rapid response to fluctuations in demand. However, many manufacturing organizations base their decisions on a c...
This paper presents a secure control framework enhanced with an event-triggered mechanism to ensure resilient and resource-efficient operation under false data injection (FDI) attacks on sensor measurements. The proposed method integrates a Kalman fi...
The security challenges faced by smart cities are attracting more attention from more people. Criminal activities and disasters can have a significant impact on the stability of a city, resulting in a loss of safety and property for its residents. Th...
Online abnormal event detection of non-cooperative space targets based on space-based optical observations is an urgent requirement for ensuring space security, particularly for sudden state changes such as attitude and orbit maneuver events. However...
An event-triggered neural adaptive cooperative control is proposed for the towing system (TS) with model parameter uncertainties and unknown disturbances. Different from ordinary multi-vessel formation control, the tugs and unactuated offshore platfo...
Two convolution neural network (CNN) models are introduced to accurately classify event-related potentials (ERPs) by fusing frequency, time, and spatial domain information acquired from the continuous wavelet transform (CWT) of the ERPs recorded from...
In recent years, the advancement of pattern recognition algorithms, specifically the deep learning-related techniques, have propelled a tremendous amount of researches in fall event recognition systems. It is important to detect a fall incident as ea...
This paper investigates the synchronization problem of fractional-order coupled neural networks (FOCNNs) featuring higher-order interactions and mixed delays under an event-dependent intermittent control framework. The proposed model incorporates hig...
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