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1st International Conference on Artificial Intelligence for Smart Community, AISC 2020

作者： Farooq, M.U. Mohamad Saad, M.N. Saleh, Y. Daud Khan, S. Universiti Teknologi PETRONAS Tronoh Malaysia Electrical Engineering Department Faculty of Engineering and Islamic Architecture Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science National University of Technology Islamabad Pakistan

ISBN: (纸本)9789811621826

At high-density crowd gatherings, people naturally escape from the region where any unexpected event happens. Escape in high-density crowds appears as a divergence pattern in the scene and timely detecting divergence patterns can save many human lives. In this paper, we propose to physically capture crowd normal and divergence motion patterns (or motion shapes) in form of images and train a shallow convolution neural network (CNN) on motion shape images for divergence behavior detection. Crowd motion pattern shape is obtained by extracting ridges of Lagrangian Coherent Structure (LCS) from the Finite-Time Lyapunov Exponent (FTLE) field and convert ridges into the grey-scale image. We also propose a divergence localization algorithm to pinpoint anomaly location(s). Experimentation is carried out on synthetic crowd datasets simulating normal and divergence behaviors at the high-density crowd. Comparison with state-of-the-art methods shows our method can obtain better accuracy for both divergence behavior detection and localization problems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion estimation

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A Minimized Thickness of MIMO Antenna Based on Thin Film Substrates for UWB application

A Minimized Thickness of MIMO Antenna Based on Thin Film Sub...

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2023 International Electrical Engineering Congress, iEECON 2023

作者： Aroonmitr, Pongsathorn Rakluea, Chawalit Loedhammacakra, Wisit Rakluea, Paitoon Tangjitjetsada, Maleeya Mahatthanajatuphat, Chatree Akkaraekthalin, Prayoot Thianthong, Tawan Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Pathum Thani12110 Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronics and Telecommunication Engineering Nonthaburi11000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand Siam Airport Ground Services Co. Ltd. Bangkok10210 Thailand

ISBN: (纸本)9798350332360

This paper presents a compact MIMO antenna for ultra-wideband (UWB) applications. It is created based on a thin film for an antenna substrate to minimize the antenna thickness, resulting in the antenna being utilized well on a curved surface. The film thickness is about 0.056 mm was used to design and cooperate with the rectangular slot antenna and rectangular stub. In addition, the antenna cover frequency range from 3.1 GHz to 10.6 GHz with the S11 and S21 below-10 dB and-15 dB, respectively. The antenna will be fed by 50 Ω coplanar waveguide for a transmission line. Moreover, an Envelope Correlation Coefficient (ECC) isn't more than 0.05 throughout the frequency range. The antenna radiation pattern is bidirectional all bands and the maximum gain is 4.6 dBi. The proposed antenna can be applied to the WBAN WPAN and WLAN, UWB communication systems as well. © 2023 IEEE.

关键词： Thin films

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A Quasi FSS Reflector with Dualband Cross Dipole for 5G Base Station Antenna Application

A Quasi FSS Reflector with Dualband Cross Dipole for 5G Base...

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2023 International Electrical Engineering Congress, iEECON 2023

作者： Loedhammacakra, Wisit Tangjitjetsada, Maleeya Rakluea, Chawalit Wongsin, Norakamon Jangjing, Thinnawat Chudpooti, Nonchanutt Mahatthanajatuphat, Chatree Thianthong, Tawan Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Pathum Thani12110 Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronics and Telecommunication Engineering Nonthaburi11000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand Siam Airport Ground Services Co. Ltd. Bangkok10210 Thailand

ISBN: (纸本)9798350332360

This article describes the application of a quasi-frequency selective surface (FSS) reflector to a cross-dipole antenna for a 5G base station communication system. The quasi-FSS is composed of a square patch resonator with a resonance frequency of 1800 MHz and a perfect electric conductor (PEC) that reflects electromagnetic waves of all frequencies. Moreover, it can support and reflect two linear polarizations (±45°) of a dual-band cross-dipole antenna with dual operating frequencies of 700 MHz and 1800 MHz. At 700 MHz and 1800 MHz, the impedance characteristic of the antenna with reflector is approximately 26.83% and 34.86 % for Sll and S22 ≤-10 dB (±45°). The measured peak gain of the antenna is around 5.74 dBi at 775MHz and 11.88 dBi at 1850MHz. With a quasi-FSS reflector, the proposed antenna can also create a directional radiation pattern and facilitate the utilization of a 5G mobile communication base station. © 2023 IEEE.

关键词： Frequency selective surfaces

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Manufacturing A Low-Cost Telegram and Optical Character Recognition-Based Indoor Air Quality Monitoring Data Logger 8

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

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8th International Conference on Information Management and technology, ICIMTech 2023

作者： Pramono, Andi Budiman, Satrio Arif Farchan, Muchammad Mappaturi, Andi Baso School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia Brawijaya University Information System Computer Science Faculty Malang65145 Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Electrical Engineering Surabaya60111 Indonesia Islamic State University of Maulana Malik Ibrahim Faculty of Science and Technology Architecture Department Malang Indonesia

ISBN: (纸本)9798350326093

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. © 2023 IEEE.

关键词： Industrial emissions

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Experiments and modeling on thermal performance evaluation of standalone and M-cycle based desiccant air-conditioning systems

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Energy Reports 2024年 11卷 1445-1454页

作者： Bhat, Showkat Ahmad Sajjad, Uzair Hussain, Imtiyaz Yan, Wei-Mon Raza, Hafiz M.U. Ali, Hafiz Muhammad Sultan, Muhammad Omar, Hani Azam, Muhammad Waheed Bozzoli, Fabio Huang, Nen-Fu Center of Innovative Incubator National Tsing Hua University Hsinchu300044 Taiwan Department of Computer Science National Tsing Hua University Hsinchu300044 Taiwan Department of Energy and Refrigerating Air-Conditioning Engineering National Taipei University of Technology Taipei10608 Taiwan Research Center of Energy Conservation for New Generation of Residential Commercial and Industrial Sectors National Taipei University of Technology Taipei10608 Taiwan Department of Agricultural Engineering Bahauddin Zakariya University Bosan Road Multan60800 Pakistan Dhahran31261 Saudi Arabia Faculty of Information Technology Zarqa University Zarqa Jordan Department of Architecture and Engineering University of Parma Parco Area delle Science 181/A Parma43124 Italy IRC-Renewable Energy and Power Systems King Fahd University of Petroleum and Minerals Dammam Dhahran Saudi Arabia

This study aims to evaluate the performance of standalone and Maisotsenko-cycle based desiccant air-conditioning systems with silica gel through experimental and explainable artificial intelligence methods. In order to examine the desiccant systems' performance, deep neural networks have been constructed based on the optimal hyper-parameters provided by the Bayesian surrogacy. The influential factors, such as regeneration air temperature and relative humidity, primary air temperature and relative humidity, among other system features like regeneration temperature and humidity, are chosen as the inputs using feature engineering and correlation heat maps. The results indicated that with the right framework and training data, the proposed model can forecast the performance of the standalone and M-cycle-based desiccant air conditioning systems with a great accuracy (i.e. R2 = 0.99). In addition, regeneration temperature and humidity, and inlet regeneration air temperature are considered to be the primary parameters that affect the thermal performance of Maisotsenko-cycle and stand-alone desiccant systems. The regeneration conditions are shown to have a positive correlation with the primary and regeneration outlet temperatures while having a negative correlation with the primary and regeneration outlet relative humidity. The primary inlet relative humidity has the greatest impact on the primary outlet relative humidity while the regeneration inlet temperature has the highest impact on the regeneration outlet relative humidity of the system. In addition, the primary inlet relative humidity is found to have positive correlations with the outlet regeneration and primary relative humidity and outlet regeneration temperature. Also, the primary air inlet temperature is found to be directly associated with the outlet primary relative humidity and temperature for the considered systems. © 2024 The Authors

关键词： Air conditioning

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A Real-Time Pedestrian Social Distancing Risk Alert System for COVID-19

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computer Systems Science & Engineering 2023年第10期47卷 937-954页

作者： Zhihan Liu Xiang Li Siqi Liu Wei Li Xiangxu Meng Jing Jia College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Department of Clinical Laboratory Harbin Medical University Cancer HospitalHarbinHeilongjiang150081China Faculty of Arts Design and ArchitectureSchool of Built EnvironmentThe University of New South WalesSydneyNSW 2052Australia

The COVID-19 virus is usually spread by small droplets when talking,coughing and sneezing,so maintaining physical distance between people is necessary to slow the spread of the *** World Health Organization(WHO)recommends maintaining a social distance of at least six *** this paper,we developed a real-time pedestrian social distance risk alert system for COVID-19,whichmonitors the distance between people in real-time via video streaming and provides risk alerts to the person in charge,thus avoiding the problem of too close social distance between pedestrians in public *** design a lightweight convolutional neural network architecture to detect the distance between people more *** addition,due to the limitation of camera placement,the previous algorithm based on flat view is not applicable to the social distance calculation for cameras,so we designed and developed a perspective conversion module to reduce the image in the video to a bird’s eye view,which can avoid the error caused by the elevation view and thus provide accurate risk indication to the *** selected images containing only person labels in theCOCO2017 dataset to train our *** experimental results show that our network model achieves 82.3%detection accuracy and performs significantly better than other mainstream network architectures in the three metrics of Recall,Precision and mAP,proving the effectiveness of our system and the efficiency of our technology.

关键词： Convolutional neural network pedestrian detection social distancing COVID-19

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Crop Yield Identification Using CNN

Crop Yield Identification Using CNN

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Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), International Conference on

作者： G. Yogapriya R. Shalini Kumari S. Suganthi K. Agalya T. Elangovan D. Selva Pandian School of Environment Architecture and Design SRM Institute of Science and Technology Ramapuram Chennai Department of Architecture Periyar Maniammai Institute of science and Technology Thanjavuri Tamilnadu Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Department of Computer Science and Engineering Karpagam Academy of Higher Education Eachanari Coimbatore

The number of people living in the world is continuously rising, which means that there must be an increase in crop production. The estimation of agricultural yields as well as the monitoring of the growth of crops are very significant for the overall economic development of a nation. The prediction of crop production is highly difficult since it is dependent on a wide variety of factors, including the genotype of the crop, environmental factors, management strategies, and the relationships between these elements. Deep learning is gaining relevance in environmental monitoring, crop type segmentation, and crop yield estimation applications as a result of recent advancements in image classification achieved by the utilization of deep Convolutional Neural Networks. Convolutional neural networks, or CNNs, are a type of deep learning approach that has shown remarkable performance in picture classification tasks. In this study, CNNs are utilized to construct a model for crop production prediction.

关键词： Deep learning Training Neural networks Crops Production Predictive models Prediction algorithms

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Application of intelligence in solving architectural problems in the field of housing

Application of intelligence in solving architectural problem...

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2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022

作者： Keyanfar, Alireza Meh, Liyana Rabbani, Reihaneh Shahid Beheshti University Department of Electrical and Computer Engineering Tehran Iran Politecnico di Torino Department of Architecture and Design Torino Italy Amirkabir University of Technology School of Material Engineering & Advanced Processes Department of Textile Engineering Tehran Iran

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

Housing is not a static artifact and contributes to the physical and psychological state of the residents. The impact of the housing effect depends on the residents' quality of life and satisfaction. A review of housing journals indicates that optimal housing is not accessible for most low-income households due to the lack of financial support for tenants due to the landlords' absolute power, dwellers with physical or mental disabilities, and elderlies. Therefore, preferably, they look for a home that is as satisfying as possible. If unavoidable, a housing relocation might bring the housing situation more in line with their preferences. This paper proposes ways to obtain adaptive housing responsive to users' preferences using intelligent technologies to optimize the residents' quality of life. The work shows that implementing sensors for managing a healthy environment, smart carpet as an interface between the environment and the ventilation system, facial emotion recognition, intelligent weather detection, smart curtains, mobile apps, and adjusting interior lighting and color-changing may be valuable to offer a comfortable, user-friendly home environment and facilitates the house choosing for the user by considering their personalities and behavior patterns. © 2022 IEEE.

关键词： Intelligent buildings

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Multi-Target Classification Using Deep Learning Models for Automotive Applications

Multi-Target Classification Using Deep Learning Models for A...

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Soumya. A Linga Reddy Cenkeramaddi Chalavadi Vishnu Yash Vinod Lanjewar Krishna Mohan C Department of Computer Science and Engineering Indian Institute of Technology Hyderabad India Department of Information and Communication Technology University of Agder Grimstad Norway Department of Architecture and Regional Planning Indian Institute of Technology Kharagpur India

This paper presents a multi-perspective convolutional neural network (CNN) that extracts the class of objects supporting intelligent transportation systems. The proposed model is the visual geometry group (VGG) backbone network with custom feature extraction blocks, that use multilayer prediction heads. The model addresses both multi-class and multi-object classification tasks utilizing the automotive object detection dataset. The model is designed with multiple prediction heads to classify different objects in an image and enable object count prediction. A publicly available automotive object detection dataset with 19800 images and labels has been utilized. The dataset consists of five primary types of objects: Persons, Trucks, Motorbikes, Cars, and Cyclists. On the dataset, pre-trained models such as VGG, Resenet, EfficientNet, and DenseNet were tested and their classification performance was evaluated. The experimental results illustrate the superiority of the proposed VGG backbone deep learning CNN model in comparison to other pre-trained models.

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Boosting Solar Energy Production of a Building using New Architectural Design

Boosting Solar Energy Production of a Building using New Arc...

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Computational Intelligence and Sustainable Engineering Solutions (CISES), International Conference on

作者： Ladan Khalvati Zahra Ebrahimi Sheykhshabani Afshin Balal Negin Arbabzadeh Department of Design Texas Tech University Lubbock USA Architecture Department Pars Institute of Art and Architecture Tehran Iran Department of Electrical and Computer Engineering Texas Tech University Lubbock USA College of Architecture Iran University of Science and Technology Tehran Iran

Fossil fuels and air pollution are significant problems nowadays, causing many countries to increase their investments in renewable energy development. As the world's energy demand grows, governments are looking into and developing alternative energy sources like solar energy. Solar energy is one of the most promising solutions to address the above problem. In addition, the development of sustainable design in architecture has become a crucial factor in the construction industry as the world faces the challenges of climate change and diminishing fossil fuel reserves. Architects are now seeking innovative ways to incorporate sustainable and renewable energy sources into their designs. Solar energy, as a sustainable and clean energy source, has gained significant attention in recent years. It offers an opportunity to reduce the dependence on non-renewable energy sources and lower carbon emissions. Additionally, the use of solar technology in architectural designs can enhance the aesthetic appeal of buildings while providing energy efficiency. Building-integrated photovoltaic systems have emerged as a popular solution that allows for the integration of photovoltaic cells into the structure of buildings. In this paper, a new architectural design for a building in Tehran is proposed, which leads to the increase of generated solar energy. Needless to say, the designs must be beautiful to look at, functional, and good for the environment. Grasshopper and rhino are utilized to demonstrate the design. In addition, System Advisor Model (SAM) is implemented to study the economic analysis of the solar photovoltaic system planted on the building. The simulation result indicates that with the new design, the power production is increased from 21437 kWh to 69484 kWh. In addition, by using the proposed system in comparison with fossil fuel power plants, a huge amount of air pollution is will be removed from the air.

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