Engineering Applications of Artificial Intelligence最新文献

{"title":"Multimodal sentiment analysis based on multiple attention","authors":"Hongbin Wang,&nbsp;Chun Ren,&nbsp;Zhengtao Yu","doi":"10.1016/j.engappai.2024.109731","DOIUrl":"10.1016/j.engappai.2024.109731","url":null,"abstract":"<div><div>The development of the Internet makes various types of data widely appear on various social platforms, multimodal data provides a new perspective for sentiment analysis. Although the data types are different, there are information expressing the same sentiment. The existing researches on extracting those information are static, and this means that there is a problem of extracting common information in a fixed amount. Therefore, to address this problem, we proposes a method named multimodal sentiment analysis based on multiple attention(MAMSA). Firstly, this method utilized the adaptive attention interaction module to dynamically determine the amount of information contributed by text and image features in multimodal fusion, and multimodal common representations are extracted through cross modal attention to improve the performance of each modal feature representation. Secondly, using sentiment information as a guide to extract text and image features related to sentiment. Finally, using hierarchical manner to fully learning the internal correlations between sentiment-text association representation, sentiment-image association representation, and multimodal common information to improve the performance of the model. We conducted extensive experiments using two public multimodal datasets, and the experimental results validated the availability of the proposed method.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109731"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of interrelationships of human errors using linguistic decision-making trial and evaluation laboratory with consensus reaching process","authors":"Qiaohong Zheng ,&nbsp;Xinwang Liu","doi":"10.1016/j.engappai.2024.109676","DOIUrl":"10.1016/j.engappai.2024.109676","url":null,"abstract":"<div><div>Analyzing human errors' interrelationships is one of the most important assignments for human reliability improvement in sociotechnical systems. Human factor analysis and classification system (HFACS) is effective in human error analysis due to its taxonomy and systematical perspective. It reveals interrelationships among human errors emerging in a multi-hierarchy of systems. However, the conventional HFACS method is incapable of quantifying their interrelationship. Especially, due to the nature of human errors, their objective data is limited. Experts' opinions are important resources to facilitate human error analysis. However, limited improved HFACS considers experts' consensus on interrelationships analysis results, especially in linguistic environments. Accordingly, this paper aims to address HFACS-based interrelationships analysis problems utilizing linguistic decision-making trial and evaluation laboratory (DEMATEL) with consensus reaching process (CRP). First, probabilistic linguistic terms are utilized to represent experts' opinions on human errors' interrelationships. Second, CRP is introduced to derive consensual opinions on human errors' interrelationships, shifting the focus to identifying human errors with low consensus levels rather than experts. Then, a hybrid weighting method is introduced to determine the weight of experts' opinions in the information fusion phase, which reflects inherent uncertainty and inter-recognition of experts’ opinions. Furthermore, DEMATEL is introduced to model direct and indirect interrelationships among human errors. Finally, a case study of a drug administration process is conducted to validate the efficiency of the proposed method. The case study indicates that neglect of safety culture development and limited financial and human resources are the top two human errors, with importance degree 0.148 and 0.107.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109676"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-scale Patch Mixer Network for Time Series Anomaly Detection","authors":"Qiushi Wang ,&nbsp;Yueming Zhu ,&nbsp;Zhicheng Sun ,&nbsp;Dong Li ,&nbsp;Yunbin Ma","doi":"10.1016/j.engappai.2024.109687","DOIUrl":"10.1016/j.engappai.2024.109687","url":null,"abstract":"<div><div>With the development of Internet of Things (IoT) technology, a large amount of data with temporal characteristics is collected and stored. How to efficiently and accurately identify anomalies from these data is a major challenge. At present, there are many problems in the application of anomaly detection, including non-stationary data, complex and difficult-to-collect anomalies, the need for real-time detection and the limitation of computing resources. But few methods can comprehensively consider these issues. To overcome these challenges, we propose a lightweight neural network, Multi-scale Patch Mixer Network (MP-MixerNet). It is mainly composed of a Mixer Block based on fully connected layer design, which contains a Temporal-Mixer and a Spatial-Mixer, and can simultaneously model the intra- and inter-series dependencies of multivariate time series. We also perform multi-scale patch segmentation based on frequency analysis, which helps the model extract robust features from multiple period views. In addition, we design an Input Stabilization module to help the model deal with data distribution shift. Experimental results on a public time series anomaly detection dataset show that we are able to achieve higher comprehensive performance with fewer parameters and inference time.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109687"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-supervised anomaly detection and localization for X-ray cargo images: Generalization to novel anomalies","authors":"Bipin Gaikwad ,&nbsp;Abani Patra ,&nbsp;Carl R. Crawford ,&nbsp;Eric L. Miller","doi":"10.1016/j.engappai.2024.109675","DOIUrl":"10.1016/j.engappai.2024.109675","url":null,"abstract":"<div><div>Robust detection of illicit items using X-ray inspection methods has gained increasing importance in recent years due to the large volume of cargo crossing international borders. In addition to detecting the presence of such items, determining their location, size, and shape is challenging due to the unpredictable nature of anomalies, but essential for expediting security inspections. Viewing the illicit items as anomalies relative to expected cargo, we propose a self-supervised learning framework consisting of an encoder–decoder–classifier–segmenter model, a multi-component loss function, coupled with a training strategy to extract discriminative features tailored for detection of the presence of anomalies, as well as localization of such items in X-ray cargo images. Our framework addresses the challenges posed by limited labeled data and offers a model capable of both detecting and localizing anomalies effectively. Moreover, we present a diverse dataset encompassing various cargo scenarios with and without anomalies, providing a robust evaluation environment for this class of problems. Unlike existing approaches, which are trained to detect specific types of objects with a fixed set of illicit items, our framework is adaptable to real-world scenarios where a wide range of illicit items may be present in the cargo. This versatility enhances the practical applicability of our model. We evaluate the performance of our framework on our dataset as well as two other publicly available datasets, demonstrating our method’s strong detection and localization performance even when faced with complex novel anomalies significantly different from those encountered during training.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109675"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-order complementary cloud application programming interface recommendation with logical reasoning for incremental development","authors":"Zhen Chen ,&nbsp;Denghui Xie ,&nbsp;Xiaolong Wang ,&nbsp;Dianlong You ,&nbsp;Limin Shen","doi":"10.1016/j.engappai.2024.109698","DOIUrl":"10.1016/j.engappai.2024.109698","url":null,"abstract":"<div><div>Cloud application programming interface, as the best carrier for service delivery, data exchange, and capability replication, has been an indispensable element of innovation in today’s app-driven world. However, it is difficult for developers to select the suitable one when facing the sea of cloud application programming interfaces. Existing researches focus on generating single-function and high-quality recommendation lists, while ignoring developers’ needs for high-order complementary cloud application programming interfaces in incremental development. In this paper, we present a high-order complementary cloud application programming interface recommendation with logical reasoning. Firstly, we conduct data analysis to demonstrate the necessity of recommending high-order complementary cloud application programming interfaces and the existence of substitute noise. Secondly, a logical reasoning network is designed using projection, intersection, and negation three logic operators, wherein high-order complementary relations are mined and substitute noises are eliminated. Then, the cloud application programming interface base vector that is complementary but not substitute to the query set is generated, and Kullback–Leibler divergence is subsequently introduced to generate complementary recommendation results. Finally, experimental results demonstrate the superiority of our approach in low-, high-, and hybrid-order complementary recommendation scenarios, and there is a significant increase in hit rate, normalize discounted cumulative gain, mean reciprocal rank, and substitute degree by 11.43%/4.86%, 10.08%/4.28%, 7.50%/2.67%, and 36.33%/32.35% on ProgrammableWeb and Huawei AppGallery datasets respectively. The proposed approach is not only more likely to produce diversified results that meet developers’ needs but also help providers better formulate pricing strategies to achieve combined sales and improve revenue.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109698"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards robust ferrous scrap material classification with deep learning and conformal prediction","authors":"Paulo Henrique dos Santos ,&nbsp;Valéria de Carvalho Santos ,&nbsp;Eduardo José da Silva Luz","doi":"10.1016/j.engappai.2024.109724","DOIUrl":"10.1016/j.engappai.2024.109724","url":null,"abstract":"<div><div>The classification of ferrous scrap materials is a well-explored problem in the literature, recognized for its significance in the steel production industry. While deep learning models are effective for this task, their deployment in industrial settings requires addressing model uncertainties and ensuring proper calibration. This study proposes adapting split conformal prediction to quantify uncertainties and facilitate model calibration. The results indicate that the Hierarchical Vision Transformer using Shifted Windows (Swin) models, particularly Swin V2, serves as the most reliable backbone for this task. Although the performance of Swin models is comparable to other evaluated models, Swin V2 demonstrates superior confidence, achieving 95.51% accuracy and the lowest conformal prediction threshold. The method is rigorously evaluated on a real-world dataset comprising 8,147 images across nine classes of ferrous scrap widely used in the Brazilian steel industry. Explainability methods corroborate the results of conformal prediction, enhancing transparency and trust in model predictions, and thereby facilitating industrial adoption. This approach bridges the gap between advanced deep learning and practical application in ferrous scrap classification, underscoring the importance of model calibration in industrial deployment.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109724"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"State of health estimation of lithium-ion battery cell based on optical thermometry with physics-informed machine learning","authors":"Jeongwoo Jang ,&nbsp;Junhyoung Jo ,&nbsp;Jinsu Kim ,&nbsp;Seungmin Lee ,&nbsp;Tonghun Lee ,&nbsp;Jihyung Yoo","doi":"10.1016/j.engappai.2024.109704","DOIUrl":"10.1016/j.engappai.2024.109704","url":null,"abstract":"<div><div>Effective thermal management and accurate state of health (SOH) estimation of lithium-ion batteries is crucial for ensuring their safety, reliability, and longevity. This study presents three innovative physics-informed machine learning-based SOH estimation techniques trained and demonstrated using experimental temperature data. Temperature distribution measurements were obtained using optical frequency domain reflectometry with optical fibers embedded in a cylindrical lithium-ion battery cell under various SOH. One of the trained model accurately predicted the SOH of a cell within 2% with only a 10-minute measurement. This technique also enables the estimation of SOH for individual cells connected in series or parallel within a battery module or pack simultaneously, thereby reducing the overall SOH estimation uncertainty without the need for disassembly. Furthermore, this not only highlights the necessity of precise thermal management in maintaining battery health but also offers a practical and efficient solution for real-time SOH monitoring in battery systems.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109704"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Arithmetic Logic Units with Two Transition Matrix and Independent Gates","authors":"Sthefanie Jofer Gomes Passo,&nbsp;Vishal H. Kothavade,&nbsp;Wei-Ming Lin,&nbsp;Clair Walton","doi":"10.1016/j.engappai.2024.109663","DOIUrl":"10.1016/j.engappai.2024.109663","url":null,"abstract":"<div><div>Neural Networks have traditionally been used to handle numerical information based on their training. However, they often struggle with systematic generalization, particularly when the numerical range during testing differs from that used in training. To tackle this issue, we propose an enhanced version of an existing architecture known as Neural Arithmetic Logic Units (NALU), incorporating Independent Gates. We refer to this new architecture as Neural Arithmetic Logic Units with Independent Gates (NALUIG), which can represent numerical values through linear activations. It employs primitive arithmetic operators, managed by learned gates that operate independently of the input, to differentiate weight matrices for both the adder and multiplier. Additionally, we introduce two new architectures: Neural Arithmetic Logic Unit with two Transition Matrices (NALU2M) and Neural Arithmetic Logic Unit with two Transition Matrices and Independent Gates (NALU2MIG). Our experiments demonstrate that the enhanced neural networks can effectively learn to perform arithmetic and numeric image classification from the Modified National Institute of Standards and Technology database (MNIST), achieving significantly lower error rates compared to other existing neural networks. This approach utilizes independent gates to represent numerical values as distinct neurons without introducing non-linearity. In this paper, we present improved results regarding numerical range generalization compared to the current state-of-the-art.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109663"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Edge artificial intelligence and super-resolution for enhanced weapon detection in video surveillance","authors":"Daniele Berardini ,&nbsp;Lucia Migliorelli ,&nbsp;Alessandro Galdelli ,&nbsp;Manuel J. Marín-Jiménez","doi":"10.1016/j.engappai.2024.109684","DOIUrl":"10.1016/j.engappai.2024.109684","url":null,"abstract":"<div><div>The prevalence of crimes involving handguns and knives underscores the importance of early weapon detection. This, along with the spread of video surveillance systems, boosted the development of automatic approaches for weapon detection from surveillance cameras. Despite the advancements from classical computer vision to Deep Learning (DL) techniques, accurately detecting weapons in real-time remains challenging due to their small size. Current DL methods, which attempt to mitigate this issue using complex detection architectures, are resource-intensive, resulting in high costs and energy usage, and hindering their deployment on efficient edge devices. This creates challenges in resource-limited environments, making these methods impractical for edge and real-time applications. To address these shortcomings, our work proposes YOLOSR, which integrates a You Only Look Once (YOLO) v8-small model with an Enhanced Deep Super Resolution (EDSR)-based network using a shared backbone. During training, the auxiliary Super Resolution (SR) helps in learning better features, which could benefit the weapon detection task. During inference, the SR branch is removed, keeping the detector’s computational complexity unchanged. The YOLOSR’s accuracy and efficiency were validated on our WeaponSense dataset and on a NVIDIA Jetson Nano, against other weapon detectors. The results exhibited that YOLOSR, compared to the state-of-the-art YOLOv8-small model, maintained the same computational complexity with 28.8 billion floating point operations and on-device latency of 101 ms per image, while increasing the Average Precision by 10.2 percentage points. Thus, the YOLOSR emerges as an effective solution for real-time weapon detection in resource-constrained environments, achieving an optimal trade-off between efficiency and accuracy.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109684"},"PeriodicalIF":7.5,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive neural boundary control for multi-agent manipulators system with uncertainties through cooperative disturbance observers network","authors":"Zhibo Zhao ,&nbsp;Yuan Yuan ,&nbsp;Xiaodong Xu ,&nbsp;Biao Luo ,&nbsp;Tingwen Huang","doi":"10.1016/j.engappai.2024.109669","DOIUrl":"10.1016/j.engappai.2024.109669","url":null,"abstract":"<div><div>This paper addresses vibration control problem of multi-agent flexible manipulators systems in the presence of simultaneous uncertainty and unknown external disturbance. Particularly, the goal is to suppress vibration of both flexible link and joint angular. In this paper, the dynamic model of the considered flexible manipulator is described by the fourth order partial differential equation. Without control, the system is unstable and vibrate constantly due to initial states, the external unknown disturbances and system uncertainties. To compensate the uncertainty in each agent, the neural networks are employed and novel adaptation laws are developed to update weighting parameters in the neural networks. While for the compensation of the external disturbance a cooperative network of disturbance observers is proposed to enhance the observation reliability. With the resulting estimations of uncertainties and the unknown disturbance, adaptive distributed boundary controllers are derived to suppress vibration in-domain and keep joint angular position to zero. The closed-loop system is proven to be uniform ultimately bounded through Lyapunov stability theory. Numerical simulations result shows that compared with the proportional–derivative control, the proposed method almost reduces all overshoot and steady-state error.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109669"},"PeriodicalIF":7.5,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}