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IoT, Communication and Automation technology (ICICAT), International Conference on

作者： M Devi Anjani Kumar Rai Avala Raji Reddy Gogineni Neelima Saif Obaid Senthilnathan Chidambaranathan Department of EEE New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Engineering & Applications GLA University Mathura Department of Mechanical Engineering CMR Technical Campus Hyderabad Telangana India Department of CSE GRIET Bachupally Hyderabad Telangana India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department of Architecture and Design Virtusa Corporation New Jersey USA

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

The application of deep learning techniques to design and develop smart grid systems present opportunities to counteract interoperability barriers ultimately improving the smart grid usability, reliability, stability and security. This paper highlights the fairly new robust Deep Learning techniques and how they can be used to solve various smart grid interoperation challenges. In this regard, following a step-by-step approach, we to detail on how data are collected and pre-processed, as well as, how deep learning models are selected and trained with corresponding performance metrics made on how the same models can be deployed in some prominent applications such as; predictive maintenance, energy consumption prediction, and anomaly detection, and lastly in cybersecurity. In addition, we continue the description of works in the field of grids standardization and protocol to achieve unification of communication between grid entities. The findings shown here prove that deep learning can enhance computational grid administration, security, and interaction ideals. There is still a need to address the issues related to the complexity of deep learning models as well as their response to dynamic gird systems in the literature; Future work should also consider the applicability of new architectures in modern grid challenges like integration of renewable energy systems and impact of EVs. In conclusion, the application of deep learning as part of the techniques offers a promising approach to improving smart grid systems and moving towards the progressive development of energy infrastructure with increased reliability and efficiency.

关键词： Deep learning Renewable energy sources Energy consumption Protocols Predictive models Data models Smart grids computer security Interoperability Anomaly detection

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Manufacturing A Low-Cost Telegram and Optical Character Recognition-Based Indoor Air Quality Monitoring Data Logger

Manufacturing A Low-Cost Telegram and Optical Character Reco...

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International Conference on Information Management and technology (ICIMTech)

作者： Andi Pramono Satrio Arif Budiman Muchammad Farchan Andi Baso Mappaturi Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Information System Computer Science Faculty Brawijaya University Malang Indonesia Department of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Architecture Department Faculty of Science and Technology Islamic State University of Maulana Malik Ibrahim Malang Indonesia

Urban air is frequently contaminated with CO, CO2, VOC, HCHO, PM 2.5, and PM 10. Rural regions are at a lower risk than those near roads and industrial areas that produce emissions. Air pollutants negatively affect its inhabitants' health, particularly the respiratory system. Most people will spend more time indoors, requiring a method for monitoring indoor air quality (IAQ). Urban communities need IAQ monitoring instruments as an early warning system for their health. This research seeks to produce inexpensive IAQ that can be utilized by all levels of society, particularly urban communities. This research utilizes a qualitative, design-thinking-based methodology. The research diverged from Empatize, which asserts that urban communities require a method for determining the local air quality. Moreover, it is determined at the define stage that IAQ monitoring, which includes a data log feature, is still comparatively expensive, so a solution is developed at the ideate stage. Creating a prototype is the next step in realizing the specified solution. In this investigation, they decided to use IAQ monitoring, which is relatively inexpensive but lacks log data storage. The author uses the ESP32-cam microcontroller to capture the screen display device, and the resulting image is sent to a telegram and processed with optical character recognition (OCR) to generate a data log. The results of prototype testing are utilized for prototype development.

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DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias

arXiv

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arXiv 2022年

作者： Zheng, Yu Gao, Chen Ding, Jingtao Yi, Lingling Jin, Depeng Li, Yong Wang, Meng Department of Electronic Engineering Tsinghua University Beijing China WeChat Technical Architecture Department Tencent Inc. Shenzhen China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Recommender systems are prone to be misled by biases in the data. Models trained with biased data fail to capture the real interests of users, thus it is critical to alleviate the impact of bias to achieve unbiased recommendation. In this work, we focus on an essential bias in micro-video recommendation, duration bias. Specifically, existing micro-video recommender systems usually consider watch time as the most critical metric, which measures how long a user watches a video. Since videos with longer duration tend to have longer watch time, there exists a kind of duration bias, making longer videos tend to be recommended more against short videos. In this paper, we empirically show that commonly-used metrics are vulnerable to duration bias, making them NOT suitable for evaluating micro-video recommendation. To address it, we further propose an unbiased evaluation metric, called WTG (short for Watch Time Gain). Empirical results reveal that WTG can alleviate duration bias and better measure recommendation performance. Moreover, we design a simple yet effective model named DVR (short for Debiased Video Recommendation) that can provide unbiased recommendation of micro-videos with varying duration, and learn unbiased user preferences via adversarial learning. Extensive experiments based on two real-world datasets demonstrate that DVR successfully eliminates duration bias and significantly improves recommendation performance with over 30% relative progress. Codes and datasets are released at https://***/tsinghua-fib-lab/WTG-DVR. © 2022, CC BY.

关键词： Watches

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A Deep Neural Network for Detecting Broken and Scattered Power Lines in Complex Aerial Images

A Deep Neural Network for Detecting Broken and Scattered Pow...

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International Conference on Artificial Intelligence and computer Information technology (AICIT)

作者： Qiugen Pei Yuxuan Lai Nianxing Wei Shiting Wu Enterprise Architecture and Digitalization Department Guangdong Power Grid Co. Ltd. Guangzhou China Dept. School of Computer Science Guangdong University of Technology Guangzhou China Huizhou Boluo Power Supply Bureau Guangdong Power Grid Co. Ltd. Huizhou China

ISBN: (数字)9798350365443

ISBN: (纸本)9798350365450

Object detection algorithms constitute a pivotal task in the realm of computer vision, aiming to precisely identify and locate objects within images across diverse scenarios. In power systems, especially in aerial images used to detect wire breaks or scattered anomalies, object detection faces significant challenges. The distribution of wires is often uneven, appearing in different sizes in high-resolution images, and the diversity of fault shapes and sizes makes it difficult for traditional object detection models to recognize accurately. Moreover, the detection of small targets also limits the precision of traditional methods. To address the issue of diversity in fault shapes and sizes in high-resolution images, which makes it difficult for traditional object detection models to recognize accurately, first, we collect fault images of wire breaks and scatter under common scenarios, constructing the “EWF _detection” (Electric wire fault) dataset, and propose a Pyramid Multi-scale Object Detection Model, establishing the Hybrid Spatial Attention Pooling module, HSAP, which significantly enhances the multi-scale feature extraction capability. It not only improves the model's ability to focus on key features, but also suppresses unnecessary features, enhancing the expressiveness of the network, and effectively adapting to complex backgrounds, variable lighting conditions, and different line scales and directions for transmission line detection needs. This improvement makes our model perform exceptionally well in detecting rare faults in wires and accurately locate fault positions. These achievements provide reliable technical support for wire anomaly detection in the power industry and indicate a broad prospect of application in future power scenarios. Our method not only improves detection accuracy, but also maintains efficient detection speed, reducing reliance on a large number of fault line images. These advantages make our model an important technical tool for pow

关键词： Adaptation models Image recognition Shape Lighting Object detection Feature extraction Power industry Robustness Maintenance Wire

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A methodological framework for futures studies: integrating normative backcasting approaches and descriptive case study design for strategic data-driven smart sustainable city planning

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Energy Informatics 2020年第1期3卷 1-42页

作者： Bibri, Simon Elias Department of Computer Science The Norwegian University of Science and Technology Sem Saelands veie 9 TrondheimNO–7491 Norway Department of Architecture and Planning The Norwegian University of Science and Technology Alfred Getz vei 3 Sentralbygg 1 5th floor TrondheimNO–7491 Norway

Originally proposed as an alternative to traditional energy planning methodology in the 1970s, backcasting is increasingly applied in futures studies related to sustainability, as it is viewed as a natural step in operationalizing sustainable development. This futures study is concerned with data-driven smart sustainable urbanism as an instance of sustainable urban development—a strategic approach to achieving the long-term goals of urban sustainability. This is at the core of backcasting, which typically defines criteria for a desirable (sustainable) future and builds a set of feasible and logical pathways between the state of the future and the present. This paper reviews, discusses, and justifies the methodological framework applied in the futures study. This aims to analyze, investigate, and develop a novel model for data-driven smart sustainable cities of the future as a form of transformative change towards sustainability. This paper corroborates that the backcasting approach—as applied in the futures study—is well-suited for long-term urban problems and sustainability solutions due to its normative, goal-oriented, and problem-solving character. It also suggests that case study research is the most effective way to underpin and increase the feasibility of future visions. Indeed, the case study approach as a research strategy facilitates the investigation and understanding of the underlying principles in the real-world phenomena involved in the construction of the future vision in the backcasting study. The novelty of this work lies in the integration of a set of principles underlying several normative backcasting approaches with descriptive case study design to devise a framework for strategic urban planning whose core objective is clarifying which city model is desired and working towards that goal. Visionary images of a long-term future based on normative backcasting can spur innovative thinking about and accelerate the movement towards sustainability. The p

关键词： Sustainable development

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The eco-city and its core environmental dimension of sustainability: green energy technologies and their integration with data-driven smart solutions

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Energy Informatics 2020年第1期3卷 1-26页

作者： Bibri, Simon Elias Department of Computer Science The Norwegian University of Science and Technology Sem Saelands veie 9 NO–7491 Trondheim Norway Department of Architecture and Planning The Norwegian University of Science and Technology Alfred Getz vei 3 Sentralbygg 1 5th floor NO–7491 Trondheim Norway

Ecological urbanism is seen today as one of the keys towards unlocking the quest for a low-carbon or fossil fuel–free society. Global and local policies promote and advocate the eco–city as the most environmentally sound model of sustainable urbanism. It is argued that the eco–city strategies and solutions are expected to deliver positive outcomes in terms of minimal demand on energy resources and thus minimal environmental impacts. Moreover, it has recently been suggested that the eco-city needs to embrace and leverage what advanced ICT has to offer, particularly with regard to sustainable energy systems, so as to improve and advance its contribution to the goals of environmental sustainability. This paper examines how the eco–city especially its core environmental dimension is practiced and justified in urban planning and development with respect to sustainable energy systems and their integration with data-driven smart technologies at the district level. To illuminate this urban phenomenon accordingly, a descriptive case study is adopted as a qualitative research methodology where the empirical basis is formed by urban planning and development documents combined with secondary data and scientific literature. To provide a theoretical foundation and produce a rationale for this study, this paper first provides a state–of–the–art review of the field of ecological urbanism in terms of its foundations, models, strategies, research issues, as well as data–driven smart technological trends. This study shows that the Eco-city District of Stockholm Royal Seaport uses green energy and data-driven smart technologies as the key strategies and solutions for achieving the environmental objectives of sustainable development in terms of lowering energy consumption and mitigating pollution. This entails conserving and decreasing the demand for energy through renewable resources (i.e., sun, wind, and water), bio–fuelled Combined Heat Power system, large-scale smart grid system, en

关键词： Sustainable development

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A Machine Vision-Based Person Detection under Low-Illuminance Conditions Using High Dynamic Range Imagery for Visual Surveillance System 3

A Machine Vision-Based Person Detection under Low-Illuminanc...

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3rd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Susa, Julie Ann B. Militante, Sammy V. Acoba, Aimee G. Evangelista, Ryan Soriente Lacatan, Luisito Lolong Lavina, Charlemagne G. Tanguilig, Bartolome T. Southern Luzon State University Dept. of Computer Engineering Quezon Lucban Philippines College of Engineering and Architecture University of Antique Antique Philippines Technological University of the Philippines Electronics Eng'g Technology Department Manila Philippines School of Graduate Studies Sulu State College Sulu Philippines College of Computing and Engineering Pamantasan Ng Cabuyao Laguna Philippines

ISBN: (纸本)9781665456647

The most difficult component of any computer vision application objects recognition and tracking. Video surveillance is a major study subject in computer vision in a dynamic environment, especially for security purposes. Video surveillance technology is critical in preventing crime, terrorism, and other threats. Several existing technologies can accurately monitor a person's mobility in interior environments. As well as regulated outdoor settings, object recognition and tracking at night, on the other hand, remain significant challenges for visual surveillance. The objects are usually farther away, inconspicuous, and have low brightness against the background. In this paper, detecting objects specifically people under low lighting conditions with the help of deep learning algorithms was used. For applications like in-car cameras and surveillance systems that operate in low light, these algorithms might improve picture identification performance. © 2022 IEEE.

关键词： Learning algorithms

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona

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Energy Informatics 2020年第1期3卷 1-42页

作者： Bibri, Simon Elias Krogstie, John Department of Computer Science The Norwegian University of Science and Technology Sem Saelands veie 9 TrondheimNO–7491 Norway Department of Architecture and Planning The Norwegian University of Science and Technology Alfred Getz vei 3 Sentralbygg 1 5th floor TrondheimNO–7491 Norway

The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this new urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to examine and compare London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to sustainability areas. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit, process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to urban operational functioning, management, and planning in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, whereas Barcelona has the best practices in the data-oriented competences, notably horizontal information platforms, operations centers, dashboards, training programs and educational institutes, innovation labs, research centers, and strategic planning offices. This research enhances the scholarly community’s current understand

关键词： Sustainable development

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Experimental Study on Gravity Currents with Internal Strati Cation in Semicircular Channels

SSRN

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SSRN 2022年

作者： Chiapponi, Luca Zemach, Tamar Petrolo, Diana Ungarish, Marius Longo, Sandro Department of Engineering and Architecture Università degli Studi di Parma Parco Area delle Scienze 181/A Parma43124 Italy Department of Computer Science Tel-Hai College Tel-Hai 1220800 Israel Department of Computer Science Technion Israel Institute of Technology Haifa32000 Israel

We present an experimental study of gravity currents (GCs) with internal stratification in a semicircular channel in the inertial–buoyancy and Boussinesq regime. Forty tests were performed with a denser volume of fluid (brine advancing in a fresh water ambient fluid) in lock-release. The lock contains an internal stratification, with a layer of density ρL overlain by a layer of density ρU, where ρa U L and ρa is the density of the ambient fluid. The experiments cover both a full depth and a partial depth configuration. The measurements consist of marking the front position of the currents as a function of the time from release, t. Two lateral video-cameras were used to detect the flow profile. Also local instantaneous density and velocity profiles data were collected in a fixed section downstream of the gate, in order to give further details and a better description of the physical phenomenon. The integral data of front speed and current thickness were compared to a theoretical model [1] based on (i) a Boussinesq flow;(ii) large Reynolds number;(iii) current advancing in shallow water conditions;and (iv) negligible ambient fluid dynamics. The system of equations and boundary conditions allows the prediction of the motion of the GC, in particular the position and speed of the nose xN (t) and uN (t). The agreement of theory and experiments is fairly good, especially for the full depth configuration. The partial depth configuration experiments are less accurately reproduced by the theory, and require a correction coefficient of the Froude number at the front, mimicking dissipation and mixing. A thorough investigation was carried out for density profiles, average velocities within the current, and buoyancy fluxes of a turbulent diffusive nature, with a quadrant analysis distinguishing the origin and direction of the fluxes. © 2022, The Authors. All rights reserved.

关键词： Gravity waves

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