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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Han, Lei Jiao, Liangbo Shi, Zhan Xu, Mengxi Nanjing Institute of Technology School of Computer Engineering Nanjing China Nanjing Institute of Technology Jiangsu Province Engineering Research Center of IntelliSense Technology and System Nanjing China

ISBN: (纸本)9798350352009

Aspect-level sentiment analysis is a challenging task that involves classifying emotions towards specific aspects mentioned in a sentence. Despite notable progress in previous research, this task remains highly complex. The key focus is on effectively encoding semantics and capturing the interaction between aspect items and context. To address these challenges, this paper proposes a novel sentiment classification method called ALSA-CA. By leveraging the BERT model for semantic encoding and incorporating a cross-attention mechanism to model aspect-item-context interactions, our approach demonstrates improved performance. Additionally, we introduce a positional weighting method that considers the relative positions of words and aspect items within sentences. Through extensive experiments conducted on three public datasets, we demonstrate the effectiveness of our method compared to current state-of-the-art techniques. © 2024 IEEE.

关键词： Semantics

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A New Database of Houma Alliance Book Ancient Handwritten Characters and Its Baseline Algorithm 23

A New Database of Houma Alliance Book Ancient Handwritten Ch...

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8th International Conference on Multimedia systems and Signal Processing, ICMSSP 2023

作者： Yuan, Xiaoyu Zhang, Zhibo Sun, Yabo Xue, Zekai Shao, Xiuyan Huang, Xiaohua School of Computer Engineering Nanjing Institute of Techology Nanjing China School of Economics and Management Southeast University Nanjing China Jiangsu Province Engineering Research Center of IntelliSense Technology and System Nanjing Jiangsu China

ISBN: (纸本)9798400700606

The Houma Alliance Book is one of the national treasures of the Museum in Shanxi Museum Town in China. It has great historical significance in researching ancient history. To date, the research on the Houma Alliance Book has been staying in the identification of paper documents, which is inefficient to identify and difficult to display, study and publicize. Therefore, the digitization of the recognized ancient characters of Houma League can effectively improve the efficiency of recognizing ancient characters and provide more reliable technical support and text data. This paper proposes a new database of Houma Alliance Book ancient handwritten characters and a multi-modal fusion method to recognize ancient handwritten characters. In the database, 297 classes and 3,547 samples of Houma Alliance ancient handwritten characters are collected from the original book collection and by human imitative writing. Furthermore, the decision-level classifier fusion strategy is applied to fuse three well-known deep neural network architectures for ancient handwritten character recognition. Experiments are performed on our new database. The experimental results first provide the baseline result of the new database to the research community and then demonstrate the efficiency of our proposed method. © 2023 ACM.

关键词： Database systems

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Anchor-free face detection in the wild with an application to facial expression recognition 7

Anchor-free face detection in the wild with an application t...

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7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Huang, Xiaohua Nanjing Institute of Technology School of Computer Engineering Nanjing China Jiangsu Province Engineering Research Center of IntelliSense Technology and System Nanjing China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Facial expression recognition (FER) has been received more attention in the field of affective computing. FER starts from the basic tasks of bounding box detection and landmark localization. For the sake of simplicity, we call bounding box detection and landmark localization as face detection. In this paper, we aim at proposing a new face detection method for FER system to improve the detect precision. More specifically, we present a new face localization based on a full convolution one-stage object detection algorithm, namely FCOSFace. It utilizes the advantages of Feature Pyramid Network and center-ness loss, which extract the shallow and high-level semantic features and suppress the influence of negative samples. Furthermore, based on FCOSFace, we used the landmark detection with DLib on the detected face. In order to evaluate the efficiency of the proposed system, experiments on both face localization and FER databases have been conducted. Experimental results demonstrate our proposed method has well-performance on face detection. Lastly, we extend our proposed method in facial expression recognition, especially in two challenging emotion databases including FER2013 and SFEW-SPI. Results on facial expression databases see the our designed system has promising results on facial expression recognition. © 2022 IEEE.

关键词： Face recognition

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Light-Field Depth Estimation Using RNN and CRF 7

Light-Field Depth Estimation Using RNN and CRF

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7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Shi, Zhan Zheng, Shengnan Huang, Xiaohua Xu, Mengxi Han, Lei Nanjing Institute of Technology School of Computer Engineering Nanjing China Nanjing Institute of Technology Jiangsu Province Engineering Research Center of IntelliSense Technology and System Nanjing China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Convolutional Neural Networks (CNNs) have recently been successfully applied to depth estimation from light field. Different from those CNN-based methods, we utilize the sequence characteristics of Epipolar Plane Images (EPIs) and introduce a novel light-field depth estimation method based on the Recurrent Neural Network (RNN). Our network builds upon two-stages architectures, involving a local depth estimation and a depth refinement part. In the first part, we regard an EPI patch as a vector sequence which is fed into the RNN to obtain a local depth value. Then, guided by the theory of Conditional Random Field (CRF), we globally optimize the depth map in the second part. Our network was trained in the disparity truth values provided by the synthetic light-field dataset. Experimental results show that our method allows to estimate high-quality disparity results. © 2022 IEEE.

关键词： Recurrent neural networks

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Tunnel silicon nitride manipulated reconfigurable bi-mode nociceptor analog

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Photonics research 2024年第8期12卷 1820-1827页

作者： CHENGDONG YANG YILONG LIU LINLIN SU XINWEI LI LIHUA XU QIMEI CHENG Jiangsu Province Engineering Research Center of Integrated Circuit Reliability Technology and Testing System Wuxi UniversityWuxi 214105China

Neuromorphic applications have shown great promise not only for efficient parallel computing mode to hold certain computational tasks,such as perception and recognition,but also as key biomimetic elements for th intelligent sensory system of next-generation ***,achieving such a biomimetic nociceptor tha can adaptively switch operation mode with a stimulation threshold remains a *** rational design of material properties and device structures,we realized an easily-fabricated,low-energy,and reconfigurable no *** is capable of threshold-triggered adaptive bi-mode jump that resembles the biological alarm system With a tunnel silicon nitride(Si_(3)N_(4))we mimicked the intensity-and rehearsal-triggered jump by means of th tunneling mode transition of Si_(3)N_(4)*** threshold signals the device can also express some common synaptic functions with an extremely low energy density of 33.5 f J∕μm^(2).In addition,through the modulation o Si_(3)N_(4)thickness it is relatively easy to fabricate the device with differing pain *** nociceptor analog based on a tunneling layer provides an opportunity for the analog pain alarm system and opens up a new path toward threshold-related novel applications.

关键词： Tunnel tunneling rational

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Automatic Longitudinal Landing Control of FanWing 2

Automatic Longitudinal Landing Control of FanWing

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2nd International Conference on Mechanical, Electronics, and Electrical and Automation Control, METMS 2022

作者： Meng, Lin Gao, Yang Zhang, Jiachao Jiao, Liangbao Artificial Intelligence Industrial Technology Research Institute Nanjing Institute of Technology Nanjing211167 China Jiangsu Province Engineering Research Center of IntelliSense Technology and System Nanjing211167 China

ISBN: (数字)9781510654785

ISBN: (纸本)9781510654778

This paper addresses the autonomous landing control of FanWing. FanWing has good slow flight capability that shares certain fundamental features of both a fixed-wing aircraft and a rotary aircraft results in short take-off and landing (STOL) capability and no stall with a large angle of attack. FanWing is an aircraft configuration that uses a simple cross-flow fan mounted in the wing to provide distributed propulsion and augmented wing lift at very low flying speeds, leading to the high coupling of the attitude control and the altitude control. A control solution based on the pitch angle rate is proposed to obtain the pitch attitude stability control. And the combination control of the elevator and the fan wing is proposed to actualize the stability control of the longitudinal attitude and height control. © 2022 SPIE

关键词： Altitude control

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MACLSTM: A Weather Attributes Enabled Recurrent Approach to Appliance-Level Energy Consumption Forecasting

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Computers, Materials & Continua 2025年第2期82卷 2969-2984页

作者： Ruoxin Li Shaoxiong Wu Fengping Deng Zhongli Tian Hua Cai Xiang Li Xu Xu Qi Liu NARI-TECH Nanjing Control System Co. Ltd.Nanjing211106China School of Software Nanjing University of Information Science&TechnologyNanjing210044China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044China

Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.

关键词： Electrical load forecasting cloud computing smart grid weather attributes energy consumption time-series analysis

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Depth estimation from light fields via epipolar geometry and an axial attention mechanism

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Optics Express 2025年第12期33卷 24321-24340页

作者： Lei Han Zhan Shi Xing Wang Yunfei Gao School of Computer Engineering Nanjing Institute of Technology Nanjing 211167 China Jiangsu Province Engineering Research Center of IntelliSense Technology and System Nanjing Institute of Technology Nanjing 211167 China School of Mechanical Engineering Nanjing Institute of Technology Nanjing 211167 China

Light-field depth estimation plays a pivotal role in various applications. This technology facilitates the creation of immersive 3D environments in virtual and augmented reality, and supports real-time environmental perception for enhanced autonomous driving safety. The academic community widely recognizes that the epipolar plane image (EPI) contains essential depth cues. To further explore this characteristic, we analyze the linear texture of EPI patches from the vector sequence perspective, through which we find that the coupling relationship between sequences can represent the complex morphology of EPI strips. Moreover, we discover that using the horizontal and vertical EPIs of an object point as depth-estimation metadata aligns well with the axial-attention calculation method. Building upon these findings, we design an EGAA model, which combines E PI g eometry and an a xial- a ttention mechanism. EGAA’s encoder module is designed to process multi-directional image volumes, where directional features are independently extracted before undergoing comprehensive fusion encoding. At the heart of this encoder lies a sophisticated axial attention block, which integrates dual attention mechanisms: horizontal attention and vertical attention. EGAA’s decoder is composed of stacked hourglass-shaped decoding blocks. These hourglass-shaped decoding blocks are implemented by convolutional neural networks and can simultaneously receive the skip connections from the encoding layer and the output of the previous decoder layer. We carried out comparative experiments and ablation experiments on both synthetic light-field datasets and real light-field datasets. The experimental results show that the EGAA model exhibits excellent performance in both quantitative and qualitative comparisons.

关键词： Augmented reality Microlens arrays Neural networks Plenoptic imaging Three dimensional reconstruction Virtual reality

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Aspect-Level Sentiment Analysis Using Cross Attention Mechanism

Aspect-Level Sentiment Analysis Using Cross Attention Mechan...

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International Conference on Big Data and Artificial Intelligence (BDAI)

作者： Lei Han Liangbo Jiao Zhan Shi Mengxi Xu School of Computer Engineering Nanjing Institute of Technology Nanjing China Jiangsu Province Engineering Research Center of IntelliSense Technology and System Nanjing Institute of Technology Nanjing China

ISBN: (数字)9798350352009

ISBN: (纸本)9798350352016

关键词： Sentiment analysis Analytical models Attention mechanisms Semantics Buildings Bidirectional control Big Data Encoding Data models Context modeling

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A bibliometric analysis of research on remote sensing-based monitoring of soil organic matter conducted between 2003 and 2023

Artificial Intelligence in Agriculture

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Artificial Intelligence in Agriculture 2025年第1期15卷 26-38页

作者： Xionghai Chen Fei Yuan Syed Tahir Ata-Ul-Karim Xiaojun Liu Yongchao Tian Yan Zhu Weixing Cao Qiang Cao National Engineering and Technology Center for Information Agriculture MOE Engineering and Research Center for Smart AgricultureMARA Key Laboratory for Crop System Analysis and Decision MakingJiangsu Key Laboratory for Information AgricultureCollaborative Innovation Centre for Modern Crop Production Co-sponsored by Province and MinistryNanjing Agricultural UniversityNanjing 210095China Department of Geography Minnesota State UniversityMankatoMN 56001USA Department of Agroecology Climate and WaterAarhus UniversityTjele 8830Denmark

Soil organic matter(SOM)is a key metric for assessing soil quality and crop yield *** plays a vital role in maintaining the ecological balance environment and promoting sustainable farming *** review examines the evolving trends in remote sensing(RS)-based SOM monitoring by analyzing 739 scholarly publications from the Web of Science database from 2003 to 2023 using a bibliometric *** study reveals that research on RS-based SOM monitoring has entered a rapid growth phase since 2018,with China and the United States as the main contributors and an extensive international cooperation *** model construction,high frequency covariates such as soil pH,precipitation,temperature,and topography significantly improved the prediction *** preprocessing methods such as Standard Normal Variables(SNV),Principal Component Analysis(PCA),and Multiple Scattering Correction(MSC)enhanced data *** statistical models are gradually being replaced by nonlinear machine learning and deep learning methods(CNN,XGBoost,andStacking),which are particularly good at handling complex high-dimensional *** spectral libraries(OzSoil and AfSIS)excel in local accuracy,while global spectral libraries(ISRIC and LUCAS)are more suitable for cross-region modeling,and the migration learning technique effectively improves the model generalization ability in low data *** models(CNN-LSTM and GAN)have significant advantages in capturing the spatial and temporal dynamics of SOMs,and uncertainty quantification methods(Bayesian inference,Monte Carlo simulation)enhance the reliability of the models in multi-source data and data-scarce *** research should focus on further optimization of multi-source data fusion and uncertainty quantification to promote the development and applicability of RS-based SOM monitoring techniques for precision soil management and sustainable agriculture.

关键词： Data fusion Modeling methods Transfer learning Spatial mapping Spectral libraries

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