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IEEE Forum on Integrated and Sustainable Transportation System (FISTS)

作者： Pengfei Chen Petros Ioannou Maged Dessouky Genevieve Giuliano Department of Electrical Engineering University of Southern California Los Angeles USA Department of Industrial & System Engineering University of Southern California Los Angeles USA Price School of Public Policy University of Southern California Los Angeles USA

The efficient supply of goods and transport of materials are important factors for sustainability in any urban environment where traffic and environmental issues also need to be addressed. In this paper we developed a centrally coordinated approach for routing freight in urban environments where traffic loads are unbalanced in time and space in an effort to improve mobility and reduce cost. We assume that freight is moved by trucks using the road network and truck fleets consist of a mix of diesel and electric trucks. We formulated the routing problem as an optimization problem with several constraints and we use a co-simulation load balancing approach to generate routes for trucks that reduce the overall cost. We use a simulation test of a road network in the Los Angeles/Long Beach Metropolitan areas that includes two major ports to demonstrate the results.

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BEYOND ALGORITHMIC FAIRNESS: A GUIDE TO DEVELOP AND DEPLOY ETHICAL AI-ENABLED DECISION-SUPPORT TOOLS

arXiv

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arXiv 2024年

作者： Gonzalez, Rosemarie Santa Piansky, Ryan Bae, Sue M. Biddle, Justin Molzahn, Daniel H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States School of Public Policy Georgia Institute of Technology United States

The integration of artificial intelligence (AI) and optimization hold substantial promise for improving the efficiency, reliability, and resilience of engineered systems. Due to the networked nature of many engineered systems, ethically deploying methodologies at this intersection poses challenges that are distinct from other AI settings, thus motivating the development of ethical guidelines tailored to AI-enabled optimization. This paper highlights the need to go beyond fairness-driven algorithms to systematically address ethical decisions spanning the stages of modeling, data curation, results analysis, and implementation of optimization-based decision support tools. Accordingly, this paper identifies ethical considerations required when deploying algorithms at the intersection of AI and optimization via case studies in power systems as well as supply chain and logistics. Rather than providing a prescriptive set of rules, this paper aims to foster reflection and awareness among researchers and encourage consideration of ethical implications at every step of the decision-making process. Copyright © 2024, The Authors. All rights reserved.

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DOMINO++: Domain-Aware Loss Regularization for Deep Learning Generalizability 26th

DOMINO++: Domain-Aware Loss Regularization for Deep Learning...

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26th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2023

作者： Stolte, Skylar E. Volle, Kyle Indahlastari, Aprinda Albizu, Alejandro Woods, Adam J. Brink, Kevin Hale, Matthew Fang, Ruogu Gainesville United States Torch Technologies LLC ShalimarFL United States Center for Cognitive Aging and Memory McKnight Brain Institute UF Gainesville United States Department of Clinical and Health Psychology College of Public Health and Health Professions UF Gainesville United States Department of Neuroscience College of Medicine UF Gainesville United States United States Air Force Research Laboratory Eglin Air Force Base ValparaisoFL United States Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering UF Gainesville United States Department of Computer Information and Science and Engineering Herbert Wertheim College of Engineering UF Gainesville United States Department of Mechanical and Aerospace Engineering Herbert Wertheim College of Engineering UF Gainesville United States

ISBN: (纸本)9783031439001

Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model’s training data. DL models that perform well on in-domain test data could struggle on OOD data. Overcoming this discrepancy is essential to the reliable deployment of DL. Proper model calibration decreases the number of spurious connections that are made between model features and class outputs. Hence, calibrated DL can improve OOD generalization by only learning features that are truly indicative of the respective classes. Previous work proposed domain-aware model calibration (DOMINO) to improve DL calibration, but it lacks designs for model generalizability to OOD data. In this work, we propose DOMINO++, a dual-guidance and dynamic domain-aware loss regularization focused on OOD generalizability. DOMINO++ integrates expert-guided and data-guided knowledge in its regularization. Unlike DOMINO which imposed a fixed scaling and regularization rate, DOMINO++ designs a dynamic scaling factor and an adaptive regularization rate. Comprehensive evaluations compare DOMINO++ with DOMINO and the baseline model for head tissue segmentation from magnetic resonance images (MRIs) on OOD data. The OOD data consists of synthetic noisy and rotated datasets, as well as real data using a different MRI scanner from a separate site. DOMINO++’s superior performance demonstrates its potential to improve the trustworthy deployment of DL on real clinical data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Magnetic resonance

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Dynamic Load Frequency Control for Small Signal Stability Analysis in Steam Plant with Reheater 21

Dynamic Load Frequency Control for Small Signal Stability An...

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21st IEEE International Symposium on Intelligent Systems and Informatics, SISY 2023

作者： Shahgholian, Ghazanfar Fathollahi, Arman Mosavi, Amirhosein Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark John von Neumann Faculty of Informatics Budapest Hungary Ludovika University of Public Service Budapest Hungary

ISBN: (纸本)9798350343366

Steam energy plants are the fundamental source of electricity in the world, which has a large share of renewable energy. Load changes impact the frequency of electrical networks. Frequency stabilization is very important based on the boosting influence of renewable power sources in energy systems. The main task of load frequency control is to keep the system frequency according to the determined nominal value and to maintain the correct amount of exchange energy between the control areas. Maintaining the output power and actual frequency of the interconnected energy system and controlling the variation in power along the connection link between control areas are the central objectives of load frequency control (LFC). In this work, the purpose is to analyze and investigate load frequency control in a two-area energy system. Each area contains a steam turbine with a reheater. The system equations are expressed in state space and analyzed in MATLAB software. The simulation outcomes demonstrate the impact of changes in the gain parameters of the equivalent power system model and high-pressure coefficient in an area. © 2023 IEEE.

关键词： Thermoanalysis

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ENHANCING SITUATIONAL AWARENESS IN SURVEILLANCE: LEVERAGING DATA VISUALIZATION TECHNIQUES FOR MACHINE LEARNING-BASED VIDEO ANALYTICS OUTCOMES

arXiv

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arXiv 2023年

作者： Ardabili, Babak Rahimi Yao, Shanle Pazho, Armin Danesh Bourque, Lauren Tabkhi, Hamed Department of Public Policy University of North Carolina at Charlotte CharlotteNC28223 United States Department of Electrical and Computer Engineering University of North Carolina at Charlotte CharlotteNC28223 United States

The pervasive deployment of surveillance cameras produces a massive volume of data, requiring nuanced interpretation. This study thoroughly examines data representation and visualization techniques tailored for AI surveillance data within current infrastructures. It delves into essential data metrics, methods for situational awareness, and various visualization techniques, highlighting their potential to enhance safety and guide urban development. This study is built upon real-world research conducted in a community college environment, utilizing eight cameras over eight days. This study presents tools like the Occupancy Indicator, Statistical Anomaly Detection, Bird's Eye View, and Heatmaps to elucidate pedestrian behaviors, surveillance, and public safety. Given the intricate data from smart video surveillance, such as bounding boxes and segmented images, we aim to convert these computer vision results into intuitive visualizations and actionable insights for stakeholders, including law enforcement, urban planners, and social scientists. The results emphasize the crucial impact of visualizing AI surveillance data on emergency handling, public health protocols, crowd control, resource distribution, predictive modeling, city planning, and informed decision-making. Copyright © 2023, The Authors. All rights reserved.

关键词： Data visualization

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Stimulating creativity and grit of high school students with creative STEM activities: an RCT with noncompliance

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Quality and Quantity 2024年第1期59卷 605-634页

作者： Ballerini, Veronica Dominici, Alice Ferracane, Martina Francesca Menchetti, Fiammetta Noirjean, Silvia Department of Statistics Computer Science Applications “G. Parenti” University of Florence Florence Italy Department of Economics European University Institute Fiesole Italy Dondena Centre for Research on Social Dynamics and Public Policy Bocconi University Milan Italy School of Computing Engineering & amp Digital Technologies Teesside University Middlesbrough United Kingdom Robert Schuman Centre for Advanced Studies European University Institute Fiesole Italy

Creativity and grit are widely recognized as vital components for the success and prosperity of individuals, even more so for younger people who will have to deal with the complex challenges connected to the digital era. Yet, whether these skills can be learnt or are innate traits is still subject to debate, which is further complicated by the difficulty of defining and, in turn, assessing these concepts. In this study, we challenge the idea that creativity and grit cannot be learnt and show that creative pedagogy activities, such as those offered by FabLabs, are powerful tools to enhance these non-cognitive skills. We conduct a randomized controlled trial in which 710 students from five Italian high schools are randomly assigned to creative STEM courses. The courses are delivered by FabLabs, small-scale workshops that offer access to tools for digital fabrication and employ a hands-on pedagogical approach expected to impact students’ creativity and grit positively. Assignment to the courses is at the class level: only students in classes randomly selected to join the Fablab activities can participate, and they may decide whether to enrol or not on a voluntary basis. We address noncompliance by adopting an instrumental variable approach. We use two modalities to measure creativity: a self-assessment through the Short Scale of Creative Self and an assessment made by an independent expert using an index of creativity developed by the authors. While the effect on self-assessed creativity is not significant, results show that the external, independent assessment of students’ creativity can capture a significant effect of Fablabs’ courses. It also emerges that FabLab activities have a positive significant effect on students’ grit, measured through the Duckworth scale. The encouraging results obtained for a relatively small sample of students should prompt a replication of the experiment on a broader scale. © The Author(s) 2024.

关键词： Causal inference Creative pedagogy FabLabs Instrumental variables Noncompliance

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Intelligent Web Spam Detection in the Consumer Internet of Things

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Wang, Rong Zhuang, Xu Zhu, Xiaogang Bashir, Ali Kashif Dabel, Maryam M. Al Yu, Keping College of Computer Science Sichuan Normal University Chengdu China OPPO Electronics Corporation Chengdu China School of Public Policy and Administration Nanchang University Nanchang Jiangxi China Department of Computing and Mathematics Manchester Metropolitan University UK Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Graduate School of Science and Engineering Hosei University Tokyo Japan

The Consumer Internet of Things (CIoT) integrates the advantage of Internet of Things (IoT) technologies to provide convenience in consumers’ daily lives. With the rapid development of the CIoT, data collected from consumer smart devices has increased exponentially. In the CIoT, web pages, as internet information carriers, offer spammers opportunities to conduct security attacks, which could harm the CIoT systems. Inspired by this challenge, this paper introduces an intelligent feature extraction method, page2vec, and a new classification algorithm, RFiRF, to detect web spam in the CIoT. page2vec is based on a score propagation model, which calculates each page’s goodness and badness scores through the links of a web graph. It is observed that different scoring functions can produce different web page features. Based on this observation, 20 to 30 scoring functions are designed and incorporated into page2vec. This way, page2vec can automatically extract web page features from a web graph. Unfortunately, page2vec also brings a high dimensionality problem when constructing binary classifiers for spam detection. To address this problem, a new classification algorithm, called Random Forest in Random Forest (RFiRF), is proposed. RFiRF replaces a random forest’s meta-classifier (decision tree) with a random forest. It divides the sparse data space into dense sub-spaces by randomly sampling training instances and features. Experiments on two benchmark datasets show that 1) page2vec features are much more predictive than the raw link-based and node2vec features, and 2) RFiRF outperforms some classification algorithms in most cases when facing the high dimensionality problem. We hope this paper can give peers valuable insights into the CIoT security. IEEE

关键词： Feature extraction

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Minimising cybersecurity risk exposures in industrial control system environments: a techno-human vulnerability analysis approach

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Journal of Cyber Security Technology 2024年

作者： Ani, Uchenna Daniel Watson, Jeremy He, Hongmei Radanliev, Petar Epiphaniou, Gregory School of Computer Science and Mathematics Keele University Staffordshire United Kingdom Department of Science Technology Engineering and Public Policy University College London London United Kingdom School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom Coventry United Kingdom

Organisations operating IoT-enabled industrial control systems (ICSs) are concerned about growing cybersecurity risks and impacts to their systems. Cyber-attacks on ICSs demonstrate that technology alone is neither a problem nor a solution to the growing cybersecurity issues affecting these systems. As socio-technical systems, ICSs encompass the functions and interactions of social and technological system elements to enable and/or sustain industrial processes. Thus, a more effective cybersecurity risk management process needs to consider human and technology factors, especially for high-value industrial process targets. Combining critical reviews and gap analysis of existing vulnerability assessment methods with conceptual modelling, a Vulnerability Analysis Critical Impact Point Process (VACIP) methodology is proposed which considers both human and technological vulnerabilities within a cyber-physical system environment to inform an improved insight about attack criticality and impacts. VACIP is validated using a simulated industrial mini testbed;showing that it can enable practicable support for security vulnerability discovery, impact criticality analysis, weak link identification, and prioritised controls. Its novelty is demonstrated in its combination of technology and human vulnerability evaluations in the minimisation of system security exposures. It provides a useful guide for adopting effective cybersecurity risk assessment and exposure reduction strategies. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Risk analysis

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Plow-Shaped Wideband Antenna for mm-wave 5G Communication

Plow-Shaped Wideband Antenna for mm-wave 5G Communication

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Kiani, Saad Hassan Rafique, Umair Alibakhshikenari, Mohammad Falcone, Fransisco Dalarsson, Mariana IIC University of Technology Department of Electrical Engineering Phnom Penh Cambodia Sapienza University Department of Information Engineering Electronics and Telecommunications Rome00184 Italy Universidad Carlos III de Madrid Department of Signal Theory and Communications Leganés28911 Spain Public University of Navarre Electric Electronic and Communication Engineering Department Pamplona31006 Spain Public University of Navarre Institute of Smart Cities Pamplona31006 Spain KTH Royal Institute of Technology School of Electrical Engineering and Computer Science StockholmSE 100-44 Sweden

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

In this paper, a simple planar plow-shaped wideband antenna is presented for mm-wave 5G devices. The proposed antenna is designed on a thin substrate with an overall size of 10×12 mm2. The results show that the antenna offers 49.5% fractional bandwidth ranging from 22.2 GHz to 36.8 GHz. Moreover, the radiation efficiency of the antenna is >90% throughout the operating bandwidth, with a peak gain of 3.3 dBi. Through its simple structure and high performance characteristics, it can be observed that the proposed antenna can be termed as a potential candidate for future mm-wave 5G communication devices. © 2022 IEEE.

关键词： Radiation efficiency

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Automotive Seat Surface Defect Detection Method Based on Improved YOLOv5 Algorithm

Automotive Seat Surface Defect Detection Method Based on Imp...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Juchao Zhang Changlin Yu Gang Wang Chunling Xu Zaixiang Pang Zhongbo Sun Department of Control Science and Engineering Changchun University of Technology Changchun China Department of Mechanical and Electrical Engineering Changchun University of Technology Changchun China Department of Public and Basic Computer Education Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

A method for detecting surface defects of car seats based on improved YOLOv5 is proposed to address the current issues of low efficiency, high missed detection rate, and difficult cost control in manual inspection of surface defects of car seats. In addition, in order to solve the imbalance problem of various defect images collected by industrial cameras, the methods of translation, cropping and adding noise are used to pre-enhance the images. Train the manually annotated dataset using an improved Yolov5 model, which uses K-means++ clustering anchor boxes to enhance adaptability to the dataset in this paper. At the same time, add a BiFormer module to the network to reduce the loss of small target defects and enhance the network’s feature fusion and extraction capabilities. The experimental results show that the mAP of the whole is increased from 67.1% to 78.2%, and the model can meet the requirements of the identification of car seat surface defects.

关键词： YOLO Deep learning Training Machine vision Noise Inspection Feature extraction

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