Journal of Automation and Intelligence最新文献_第3页

A genetic programming approach with adaptive region detection to skin cancer image classification 基于自适应区域检测的遗传规划皮肤癌图像分类方法

Journal of Automation and Intelligence Pub Date : 2024-12-01 DOI: 10.1016/j.jai.2024.08.003

Kunjie Yu , Jintao Lian , Ying Bi , Jing Liang , Bing Xue , Mengjie Zhang

{"title":"A genetic programming approach with adaptive region detection to skin cancer image classification","authors":"Kunjie Yu , Jintao Lian , Ying Bi , Jing Liang , Bing Xue , Mengjie Zhang","doi":"10.1016/j.jai.2024.08.003","DOIUrl":"10.1016/j.jai.2024.08.003","url":null,"abstract":"<div><div>Dermatologists typically require extensive experience to accurately classify skin cancer. In recent years, the development of computer vision and machine learning has provided new methods for assisted diagnosis. Existing skin cancer image classification methods have certain limitations, such as poor interpretability, the requirement of domain knowledge for feature extraction, and the neglect of lesion area information in skin images. This paper proposes a new genetic programming (GP) approach to automatically learn global and/or local features from skin images for classification. To achieve this, a new function set and a new terminal set have been developed. The proposed GP method can automatically and flexibly extract effective local/global features from different types of input images, thus providing a comprehensive description of skin images. A new region detection function has been developed to select the lesion areas from skin images for feature extraction. The performance of this approach is evaluated on three skin cancer image classification tasks, and compared with three GP methods and six non-GP methods. The experimental results show that the new approach achieves significantly better or similar performance in most cases. Further analysis validates the effectiveness of our parameter settings, visualizes the multiple region detection functions used in the individual evolved by the proposed approach, and demonstrates its good convergence ability.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"3 4","pages":"Pages 240-249"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143142846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SPADE: A spatial information assisted collision distance estimator for robotic arm 基于空间信息的机械臂碰撞距离估计方法

Journal of Automation and Intelligence Pub Date : 2024-12-01 DOI: 10.1016/j.jai.2024.11.001

Jiakang Zhou , Yue Cao , Yu-Xuan Ren , Steve Feng Shu

引用次数: 0

Adaptive neural network event-triggered secure formation control of nonholonomic mobile robots subject to deception attacks 欺骗攻击下非完整移动机器人的自适应神经网络事件触发安全编队控制

Journal of Automation and Intelligence Pub Date : 2024-12-01 DOI: 10.1016/j.jai.2024.10.002

Kai Wang , Wei Wu , Shaocheng Tong

引用次数: 0

Fast and robust strain signal processing for aircraft structural health monitoring 用于飞机结构健康监测的快速稳健应变信号处理技术

Journal of Automation and Intelligence Pub Date : 2024-09-01 DOI: 10.1016/j.jai.2024.07.001

Cong Wang , Xin Tan , Xiaobin Ren , Xuelong Li

{"title":"Fast and robust strain signal processing for aircraft structural health monitoring","authors":"Cong Wang , Xin Tan , Xiaobin Ren , Xuelong Li","doi":"10.1016/j.jai.2024.07.001","DOIUrl":"10.1016/j.jai.2024.07.001","url":null,"abstract":"<div><p>This work elaborates a fast and robust structural health monitoring scheme for copying with aircraft structural fatigue. The type of noise in structural strain signals is determined by using a statistical analysis method, which can be regarded as a mixture of Gaussian-like (tiny hairy signals) and impulse-like noise (single signals with anomalous movements in peak and valley areas). Based on this, a least squares filtering method is employed to preprocess strain signals. To precisely eliminate noise or outliers in strain signals, we propose a novel variational model to generate step signals instead of strain ones. Expert judgments are employed to classify the generated signals. Based on the classification labels, whether the aircraft is structurally healthy is accurately judged. By taking the generated step count vectors and labels as an input, a discriminative neural network is proposed to realize automatic signal discrimination. The network output means whether the aircraft structure is healthy or not. Experimental results demonstrate that the proposed scheme is effective and efficient, as well as achieves more satisfactory results than other peers.</p></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"3 3","pages":"Pages 160-168"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949855424000297/pdfft?md5=c3b2f08b402beb05f4d355921b723cb4&pid=1-s2.0-S2949855424000297-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141690455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prescribed finite-time stabilization of fuzzy neural networks with time-varying controller 带有时变控制器的模糊神经网络的规定有限时间稳定化

Journal of Automation and Intelligence Pub Date : 2024-09-01 DOI: 10.1016/j.jai.2024.07.003

Yufeng Zhou, Yawen Zhou, Peng Wan

{"title":"Prescribed finite-time stabilization of fuzzy neural networks with time-varying controller","authors":"Yufeng Zhou, Yawen Zhou, Peng Wan","doi":"10.1016/j.jai.2024.07.003","DOIUrl":"10.1016/j.jai.2024.07.003","url":null,"abstract":"<div><p>This paper investigates the exponential and prescribed finite-time stabilization with time-varying controller. First, the constraints of boundedness and differentiability on time delays are simultaneously relaxed, the Lipschitz condition for activation function is also relaxed. Second, different from the traditional Lyapunov function, two different time-varying Lyapunov functions are respectively constructed to achieve the exponential and prescribed finite-time stabilization. Significantly, the exponential convergence rate and the settling time are constants that can be given in advance and are not affected by system parameters and initial states. In addition, the time-varying controllers have good tolerance for disturbance caused by discontinuous functions and the disturbance is perfectly resolved and does not affect the control performance. Especially, the form of controllers is relatively simple and there is not necessary to design the fractional-order controllers for prescribed finite-time stabilization. Furthermore, the exponential and prescribed finite-time stabilization for FNNs without delay are respectively established via continuous time-varying state feedback control. Finally, examples show the effectiveness of the proposed control methods.</p></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"3 3","pages":"Pages 176-184"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949855424000315/pdfft?md5=1f477ba1b94b230df31209cb37a43f67&pid=1-s2.0-S2949855424000315-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141693695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AV-FDTI: Audio-visual fusion for drone threat identification AV-FDTI：用于无人机威胁识别的视听融合技术

Journal of Automation and Intelligence Pub Date : 2024-09-01 DOI: 10.1016/j.jai.2024.06.002

Yizhuo Yang, Shenghai Yuan, Jianfei Yang, Thien Hoang Nguyen, Muqing Cao, Thien-Minh Nguyen, Han Wang, Lihua Xie

{"title":"AV-FDTI: Audio-visual fusion for drone threat identification","authors":"Yizhuo Yang, Shenghai Yuan, Jianfei Yang, Thien Hoang Nguyen, Muqing Cao, Thien-Minh Nguyen, Han Wang, Lihua Xie","doi":"10.1016/j.jai.2024.06.002","DOIUrl":"10.1016/j.jai.2024.06.002","url":null,"abstract":"<div><p>In response to the evolving challenges posed by small unmanned aerial vehicles (UAVs), which have the potential to transport harmful payloads or cause significant damage, we present AV-FDTI, an innovative Audio-Visual Fusion system designed for Drone Threat Identification. AV-FDTI leverages the fusion of audio and omnidirectional camera feature inputs, providing a comprehensive solution to enhance the precision and resilience of drone classification and 3D localization. Specifically, AV-FDTI employs a CRNN network to capture vital temporal dynamics within the audio domain and utilizes a pretrained ResNet50 model for image feature extraction. Furthermore, we adopt a visual information entropy and cross-attention-based mechanism to enhance the fusion of visual and audio data. Notably, our system is trained based on automated Leica tracking annotations, offering accurate ground truth data with millimeter-level accuracy. Comprehensive comparative evaluations demonstrate the superiority of our solution over the existing systems. In our commitment to advancing this field, we will release this work as open-source code and wearable AV-FDTI design, contributing valuable resources to the research community.</p></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"3 3","pages":"Pages 144-151"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949855424000285/pdfft?md5=684e37e58a4ae5abf55addf8f81639b9&pid=1-s2.0-S2949855424000285-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal decarbonisation pathway for mining truck fleets 采矿卡车车队的最佳脱碳途径

Journal of Automation and Intelligence Pub Date : 2024-09-01 DOI: 10.1016/j.jai.2024.03.003

{"title":"Optimal decarbonisation pathway for mining truck fleets","authors":"","doi":"10.1016/j.jai.2024.03.003","DOIUrl":"10.1016/j.jai.2024.03.003","url":null,"abstract":"<div><p>The fossil fuel powered mining truck fleets can contribute up to 80% of total emissions in open pit mines. This study investigates the optimal decarbonisation pathway for mining truck fleets. Notably, our proposed pathway incorporates power generation, negative carbon technologies, and carbon trading. Technical, financial, and environmental models of decarbonisation technologies are established, capturing regional variations and time dynamic characteristics such as cost trends and carbon capture efficiency. The dynamic natures of characteristics pose challenges for using the cost-effective analyses approach to find the optimal decarbonisation pathway. To address this, we introduce a mixed-integer programming optimisation framework to find the decarbonisation pathway with minimum life cycle costs during the planning period. A case study for the optimal decarbonisation pathway of truck fleets in a South African coal mine is conducted to illustrate the applicability of the proposed model. Results indicate that the optimal decarbonisation pathway is significantly influenced by factors such as land cost, annual budget, and carbon trading prices. The proposed method provides invaluable guidance for transitioning towards a cleaner and more sustainable mining industry.</p></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"3 3","pages":"Pages 129-143"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949855424000170/pdfft?md5=4f517fd6eea714a6a50b1922abb059fc&pid=1-s2.0-S2949855424000170-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140270581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Containment control of heterogeneous multi-agent systems subject to Markovian randomly switching topologies and unbounded delays 受马尔可夫随机切换拓扑和无限制延迟影响的异构多代理系统的遏制控制

Journal of Automation and Intelligence Pub Date : 2024-09-01 DOI: 10.1016/j.jai.2024.06.001

引用次数: 0

Exponential stabilization of linear systems with feedback optimization and its application to microgrids control 带反馈优化的线性系统指数稳定及其在微电网控制中的应用

Journal of Automation and Intelligence Pub Date : 2024-09-01 DOI: 10.1016/j.jai.2024.07.002

Xiemin Mo, Tao Liu

引用次数: 0

ConGCNet: Convex geometric constructive neural network for Industrial Internet of Things ConGCNet：用于工业物联网的凸几何构造神经网络

Journal of Automation and Intelligence Pub Date : 2024-09-01 DOI: 10.1016/j.jai.2024.07.004

Jing Nan , Wei Dai , Chau Yuen , Jinliang Ding

{"title":"ConGCNet: Convex geometric constructive neural network for Industrial Internet of Things","authors":"Jing Nan , Wei Dai , Chau Yuen , Jinliang Ding","doi":"10.1016/j.jai.2024.07.004","DOIUrl":"10.1016/j.jai.2024.07.004","url":null,"abstract":"<div><p>The intersection of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) has garnered ever-increasing attention and research interest. Nevertheless, the dilemma between the strict resource-constrained nature of IIoT devices and the extensive resource demands of AI has not yet been fully addressed with a comprehensive solution. Taking advantage of the lightweight constructive neural network (LightGCNet) in developing fast learner models for IIoT, a convex geometric constructive neural network with a low-complexity control strategy, namely, ConGCNet, is proposed in this article via convex optimization and matrix theory, which enhances the convergence rate and reduces the computational consumption in comparison with LightGCNet. Firstly, a low-complexity control strategy is proposed to reduce the computational consumption during the hidden parameters training process. Secondly, a novel output weights evaluated method based on convex optimization is proposed to guarantee the convergence rate. Finally, the universal approximation property of ConGCNet is proved by the low-complexity control strategy and convex output weights evaluated method. Simulation results, including four benchmark datasets and the real-world ore grinding process, demonstrate that ConGCNet effectively reduces computational consumption in the modelling process and improves the model’s convergence rate.</p></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"3 3","pages":"Pages 169-175"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949855424000327/pdfft?md5=6981fac66e625428128ac9ad415f5fca&pid=1-s2.0-S2949855424000327-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141846488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0