Constraint-weighted support vector ordinal regression to resist constraint noises

The existing YOLOv5‐based framework has achieved great success in the field of target detection. However, in forest fire detection tasks, there are few high‐quality forest fire images available, and the performance of the YOLO model has suffered a serious decline in detecting small‐scale forest fires. Making full use of context information can effectively improve the performance of small target detection. To this end, this paper proposes a new graph‐embedded YOLOv5 forest fire detection framework, which can improve the performance of small‐scale forest fire detection using different scales of context information. To mine local context information, we design a spatial graph convolution operation based on the message passing neural network (MPNN) mechanism. To utilize global context information, we introduce a multi‐head self‐attention (MSA) module before each YOLO head. The experimental results on FLAME and our self‐built fire dataset show that our proposed model improves the accuracy of small‐scale forest fire detection. The proposed model achieves high performance in real‐time performance by fully utilizing the advantages of the YOLOv5 framework.

Section: Discussionmentioning

confidence: 99%

An effective graph embedded YOLOv5 model for forest fire detection

Yuan,

Lu,

Zhang

et al. 2024

“…On the other hand, how to predict the expression intensity is an open topic in FER 65 . Different from classification, the expression intensity has strictly ordinal relation which is an ordinal regression problem 66,67 …”

Section: Discussionmentioning

confidence: 99%

A joint hierarchical cross‐attention graph convolutional network for multi‐modal facial expression recognition

Xu,

Du,

Wang

et al. 2023

Emotional recognition in conversations (ERC) is increasingly being applied in various IoT devices. Deep learning‐based multimodal ERC has achieved great success by leveraging diverse and complementary modalities. Although most existing methods try to adopt attention mechanisms to fuse different information, these methods ignore the complementarity between modalities. To this end, the joint cross‐attention model is introduced to alleviate this issue. However, multi‐scale feature information on different modalities is not utilized. Moreover, the context relationship plays an important role in feature extraction in the expression recognition task. In this paper, we propose a novel joint hierarchical graph convolution network (JHGCN) which exploits different layer features and context relationships for facial expression recognition based on audio‐visual (A‐V) information. Specifically, we adopt different deep networks to extract features from different modalities individually. For V modality, we construct V graph data based on patch embeddings which are extracted from the transformer encoder. Moreover, we embed the graph convolution which can leverage the intra‐modality relationships with the transformer encoder. Then, the deep feature from different layers is fed to the hierarchical fusion module to enhance feature representation. At last, we use the joint cross‐attention mechanism to exploit the complementary inter‐modality relationships. To validate the proposed model, we have conducted various experiments on the AffWild2 and CMU‐MOSI datasets. All results confirm that our proposed model achieves highly promising performance compared to the joint cross‐attention model and other methods.

“…8,9 Especially in the field of photovoltaic power generation forecasting, ML-based technologies for PV power prediction get better results than existing physical methods. 10 For example, Support Vector Machine (SVM) [11][12][13][14] is applied on PV power prediction with good nonlinear mapping ability, which can accurately fit future PV power generation prediction. Similarly, nonlinear auto-regression and exogenous vector input neural network (NARX) is proposed, which takes various meteorological factors as exogenous input to improve prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Short‐term photovoltaic power forecasting using hybrid contrastive learning and temporal convolutional network under future meteorological information absence

Lu,

Chen,

et al. 2023

Photovoltaic (PV) power generation is widely utilized to satisfy the increasing energy demand due to its cleanness and inexhaustibility. Accurate PV power forecasting can improve the penetration of PV power in the grid. However, it is pretty challenging to predict PV power in short‐term under precious future meteorological information absence conditions. To address this problem, this study proposes the hybrid Contrastive Learning and Temporal Convolutional Network (CL‐TCN), and this forecasting approach consists of two parts, including model training and adaptive processes of forecasting models. In the model training stage, this forecasting method firstly trains 18 TCN models for 18 time points from 9:00 a.m. to 17:30 p.m. These TCN models are trained by only using historical PV power data samples, and each model is used to predict the next half‐hour power output. The adaptive process of models means that, in a practical forecasting stage, PV power samples from historical data are firstly evaluated and scored by a CL based data scoring mechanism to search for the most similar data samples to current measured samples. Then these similar samples are further applied to training a single above‐mentioned well‐trained TCN model to improve its performance in forecasting the next half‐hour PV power. The experimental results tested at the time resolution of 30 min demonstrate that the proposed approach has superior performance in forecasting accuracy not only in smooth PV power samples but also in fluctuating PV power samples. Moreover, the proposed CL based data scoring mechanism can filter useless data samples effectively accelerating the forecasting process.