Journal of Automation and Intelligence最新文献_第6页

Optimal integration of solar home systems and appliance scheduling for residential homes under severe national load shedding 在国家严重削峰填谷的情况下，住宅太阳能系统与家电调度的优化整合

Journal of Automation and Intelligence Pub Date : 2023-11-01 DOI: 10.1016/j.jai.2023.12.001

Sakhile Twala , Xianming Ye , Xiaohua Xia , Lijun Zhang

{"title":"Optimal integration of solar home systems and appliance scheduling for residential homes under severe national load shedding","authors":"Sakhile Twala , Xianming Ye , Xiaohua Xia , Lijun Zhang","doi":"10.1016/j.jai.2023.12.001","DOIUrl":"10.1016/j.jai.2023.12.001","url":null,"abstract":"<div><p>In developing countries like South Africa, users experienced more than 1 030 hours of load shedding outages in just the first half of 2023 due to inadequate power supply from the national grid. Residential homes that cannot afford to take actions to mitigate the challenges of load shedding are severely inconvenienced as they have to reschedule their demand involuntarily. This study presents optimal strategies to guide households in determining suitable scheduling and sizing solutions for solar home systems to mitigate the inconvenience experienced by residents due to load shedding. To start with, we predict the load shedding stages that are used as input for the optimal strategies by using the K-Nearest Neighbour (KNN) algorithm. Based on an accurate forecast of the future load shedding patterns, we formulate the residents’ inconvenience and the loss of power supply probability during load shedding as the objective function. When solving the multi-objective optimisation problem, four different strategies to fight against load shedding are identified, namely (1) optimal home appliance scheduling (HAS) under load shedding; (2) optimal HAS supported by solar panels; (3) optimal HAS supported by batteries, and (4) optimal HAS supported by the solar home system with both solar panels and batteries. Among these strategies, appliance scheduling with an optimally sized 9.6 kWh battery and a 2.74 kWp panel array of five 550 Wp panels, eliminates the loss of power supply probability and reduces the inconvenience by 92% when tested under the South African load shedding cases in 2023.</p></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"2 4","pages":"Pages 227-238"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949855423000497/pdfft?md5=5604f1ab7a7a94164212467990d73d89&pid=1-s2.0-S2949855423000497-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138993256","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

Leveraging on few-shot learning for tire pattern classification in forensics 利用少射学习在法医轮胎模式分类中的应用

Journal of Automation and Intelligence Pub Date : 2023-08-01 DOI: 10.1016/j.jai.2023.08.002

Lijun Jiang , Syed Ariff Syed Hesham , Keng Pang Lim , Changyun Wen

{"title":"Leveraging on few-shot learning for tire pattern classification in forensics","authors":"Lijun Jiang , Syed Ariff Syed Hesham , Keng Pang Lim , Changyun Wen","doi":"10.1016/j.jai.2023.08.002","DOIUrl":"https://doi.org/10.1016/j.jai.2023.08.002","url":null,"abstract":"<div><p>This paper presents a novel approach for tire-pattern classification, aimed at conducting forensic analysis on tire marks discovered at crime scenes. The classification model proposed in this study accounts for the intricate and dynamic nature of tire prints found in real-world scenarios, including accident sites. To address this complexity, the classifier model was developed to harness the meta-learning capabilities of few-shot learning algorithms (learning-to-learn). The model is meticulously designed and optimized to effectively classify both tire patterns exhibited on wheels and tire-indentation marks visible on surfaces due to friction. This is achieved by employing a semantic segmentation model to extract the tire pattern marks within the image. These marks are subsequently used as a mask channel, combined with the original image, and fed into the classifier to perform classification. Overall, The proposed model follows a three-step process: (i) the Bilateral Segmentation Network is employed to derive the semantic segmentation of the tire pattern within a given image. (ii) utilizing the semantic image in conjunction with the original image, the model learns and clusters groups to generate vectors that define the relative position of the image in the test set. (iii) the model performs predictions based on these learned features.</p><p>Empirical verification demonstrates usage of semantic model to extract the tire patterns before performing classification increases the overall accuracy of classification by <span><math><mo>∼</mo></math></span>4%.</p></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"2 3","pages":"Pages 146-151"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49743363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reachable set estimation for discrete-time Markovian jump neural networks with unified uncertain transition probability 具有统一不确定转移概率的离散马尔可夫跳跃神经网络的可达集估计

Journal of Automation and Intelligence Pub Date : 2023-08-01 DOI: 10.1016/j.jai.2023.09.002

Yufeng Tian , Wengang Ao , Peng Shi

引用次数: 0

Adaptive recurrent neural network for uncertainties estimation in feedback control system 反馈控制系统中不确定性估计的自适应递归神经网络

Journal of Automation and Intelligence Pub Date : 2023-08-01 DOI: 10.1016/j.jai.2023.07.001

Adel Merabet , Saikrishna Kanukollu , Ahmed Al-Durra , Ehab F. El-Saadany

{"title":"Adaptive recurrent neural network for uncertainties estimation in feedback control system","authors":"Adel Merabet , Saikrishna Kanukollu , Ahmed Al-Durra , Ehab F. El-Saadany","doi":"10.1016/j.jai.2023.07.001","DOIUrl":"https://doi.org/10.1016/j.jai.2023.07.001","url":null,"abstract":"<div><p>In this paper, a recurrent neural network (RNN) is used to estimate uncertainties and implement feedback control for nonlinear dynamic systems. The neural network approximates the uncertainties related to unmodeled dynamics, parametric variations, and external disturbances. The RNN has a single hidden layer and uses the tracking error and the output as feedback to estimate the disturbance. The RNN weights are online adapted, and the adaptation laws are developed from the stability analysis of the controlled system with the RNN estimation. The used activation function, at the hidden layer, has an expression that simplifies the adaptation laws from the stability analysis. It is found that the adaptive RNN enhances the tracking performance of the feedback controller at the transient and steady state responses. The proposed RNN based feedback control is applied to a DC–DC converter for current regulation. Simulation and experimental results are provided to show its effectiveness. Compared to the feedforward neural network and the conventional feedback control, the RNN based feedback control provides good tracking performance.</p></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"2 3","pages":"Pages 119-129"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49743931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-intrusive soil carbon content quantification methods using machine learning algorithms: A comparison of microwave and millimeter wave radar sensors 使用机器学习算法的非侵入式土壤碳含量定量方法:微波和毫米波雷达传感器的比较

Journal of Automation and Intelligence Pub Date : 2023-08-01 DOI: 10.1016/j.jai.2023.09.001

Di An , YangQuan Chen

{"title":"Non-intrusive soil carbon content quantification methods using machine learning algorithms: A comparison of microwave and millimeter wave radar sensors","authors":"Di An , YangQuan Chen","doi":"10.1016/j.jai.2023.09.001","DOIUrl":"https://doi.org/10.1016/j.jai.2023.09.001","url":null,"abstract":"<div><p>Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification, making it a significant carbon source for soil. Applying biochar to soil is a carbon-negative process that helps combat climate change, sustain soil biodiversity, and regulate water cycling. However, quantifying soil carbon content conventionally is time-consuming, labor-intensive, imprecise, and expensive, making it difficult to accurately measure in-field soil carbon’s effect on storage water and nutrients. To address this challenge, this paper for the first time, reports on extensive lab tests demonstrating non-intrusive methods for sensing soil carbon and related smart biochar applications, such as differentiating between biochar types from various biomass feedstock species, monitoring soil moisture, and biochar water retention capacity using portable microwave and millimeter wave sensors, and machine learning. These methods can be scaled up by deploying the sensor in-field on a mobility platform, either ground or aerial. The paper provides details on the materials, methods, machine learning workflow, and results of our investigations. The significance of this work lays the foundation for assessing carbon-negative technology applications, such as soil carbon content accounting. We validated our quantification method using supervised machine learning algorithms by collecting real soil mixed with known biochar contents in the field. The results show that the millimeter wave sensor achieves high sensing accuracy (up to 100%) with proper classifiers selected and outperforms the microwave sensor by approximately 10%–15% accuracy in sensing soil carbon content.</p></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"2 3","pages":"Pages 152-166"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49743550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MsFireD-Net: A lightweight and efficient convolutional neural network for flame and smoke segmentation MsFireD-Net:用于火焰和烟雾分割的轻量级高效卷积神经网络

Journal of Automation and Intelligence Pub Date : 2023-08-01 DOI: 10.1016/j.jai.2023.08.003

F.M. Anim Hossain, Youmin Zhang

{"title":"MsFireD-Net: A lightweight and efficient convolutional neural network for flame and smoke segmentation","authors":"F.M. Anim Hossain, Youmin Zhang","doi":"10.1016/j.jai.2023.08.003","DOIUrl":"https://doi.org/10.1016/j.jai.2023.08.003","url":null,"abstract":"<div><p>With the rising frequency and severity of wildfires across the globe, researchers have been actively searching for a reliable solution for early-stage forest fire detection. In recent years, Convolutional Neural Networks (CNNs) have demonstrated outstanding performances in computer vision-based object detection tasks, including forest fire detection. Using CNNs to detect forest fires by segmenting both flame and smoke pixels not only can provide early and accurate detection but also additional information such as the size, spread, location, and movement of the fire. However, CNN-based segmentation networks are computationally demanding and can be difficult to incorporate onboard lightweight mobile platforms, such as an Uncrewed Aerial Vehicle (UAV). To address this issue, this paper has proposed a new efficient upsampling technique based on transposed convolution to make segmentation CNNs lighter. This proposed technique, named Reversed Depthwise Separable Transposed Convolution (RDSTC), achieved F1-scores of 0.78 for smoke and 0.74 for flame, outperforming U-Net networks with bilinear upsampling, transposed convolution, and CARAFE upsampling. Additionally, a Multi-signature Fire Detection Network (MsFireD-Net) has been proposed in this paper, having 93% fewer parameters and 94% fewer computations than the RDSTC U-Net. Despite being such a lightweight and efficient network, MsFireD-Net has demonstrated strong results against the other U-Net-based networks.</p></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"2 3","pages":"Pages 130-138"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49743915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Matrix pencil based robust control for feedforward systems with event-triggered communications and sensor/actuator faults 具有事件触发通信和传感器/执行器故障的前馈系统的矩阵铅笔鲁棒控制

Journal of Automation and Intelligence Pub Date : 2023-08-01 DOI: 10.1016/j.jai.2023.08.001

Hefu Ye , Marios M. Polycarpou , Changyun Wen

{"title":"Matrix pencil based robust control for feedforward systems with event-triggered communications and sensor/actuator faults","authors":"Hefu Ye , Marios M. Polycarpou , Changyun Wen","doi":"10.1016/j.jai.2023.08.001","DOIUrl":"https://doi.org/10.1016/j.jai.2023.08.001","url":null,"abstract":"<div><p>In this paper we address the issue of output-feedback robust control for a class of feedforward nonlinear systems. Essentially different from the related literature, the feedback/input signals are corrupted by additive noises and can only be transmitted intermittently due to the consideration of event-triggered communications, which bring new challenges to the control design. With the aid of matrix pencil based design procedures, regulating the output to near zero is globally solved by a non-conservative dynamic low-gain controller which requires only an <em>a priori</em> information on the upper-bound of the growth rate of nonlinearities. Theoretical analysis shows that the closed-loop system is input-to-state stable with respect to the sampled errors and additive noise. In particular, the observer and controller designs have a dual architecture with a single dynamic scaling parameter whose update law can be obtained by calculating the generalized eigenvalues of matrix pencils offline, which has an advantage in the sense of improving the system convergence rate.</p></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"2 3","pages":"Pages 139-145"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49757540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

2-D distributed pose estimation of multi-agent systems using bearing measurements 基于方位测量的多智能体系统二维分布姿态估计

Journal of Automation and Intelligence Pub Date : 2023-05-01 DOI: 10.1016/j.jai.2023.06.002

Xu Fang , Jitao Li , Xiaolei Li , Lihua Xie

引用次数: 0

Distributed computations for large-scale networked systems using belief propagation 基于信念传播的大规模网络系统分布式计算

Journal of Automation and Intelligence Pub Date : 2023-05-01 DOI: 10.1016/j.jai.2023.06.003

Qianqian Cai , Zhaorong Zhang , Minyue Fu

引用次数: 0

Practical prescribed-time tracking control for uncertain strict-feedback systems with guaranteed performance under unknown control directions 未知控制方向下性能保证的不确定严格反馈系统的实用规定时间跟踪控制

Journal of Automation and Intelligence Pub Date : 2023-05-01 DOI: 10.1016/j.jai.2023.02.001

Zhou Yang , Yujuan Wang , Frank L. Lewis

引用次数: 1