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Artificial Intelligence Applications for Sustainable Urban Living
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Artificial Intelligence Applications for Sustainable Urban Living

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 38671

Special Issue Editors

Dr. Haoran Wei

E-Mail Website
Guest Editor
Department of ECE, University of Texas at Dallas, Richardson, TX 75081, USA
Interests: artificial intelligence; audio and music processing; image and video processing; multimodal
Special Issues, Collections and Topics in MDPI journals
Dr. Zhendong Wang

E-Mail Website
Guest Editor
Department of ECE, University of Texas at Dallas, Richardson, TX 75081, USA
Interests: artificial intelligence; computer vision; heterogeneous computing; memory; autonomous driving
Dr. Yuchao Chang

E-Mail Website
Guest Editor
1. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2. CIX Technology (Shanghai) Co., Ltd., Shanghai 201203, China
Interests: artificial intelligence and machine learning for wireless, green Internet of Things system; modeling and algorithm design
Special Issues, Collections and Topics in MDPI journals
Dr. Zhenghua Huang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
Interests: artificial intelligence; computer vision and medical image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

All global cities are in the process of transforming from classical cities to sustainable smart cities. In this process, people face many urgent challenges in sustainable urban living, such as urban safety, urban living quality, urban energy usage, urban traffic management, urban information security, and so on. In dealing with these urgent challenges in sustainable urban living, Artificial Intelligence (AI)-based applications play important roles. State-of-the-art AI-based technologies in image processing, video processing, speech and audio processing, music processing, natural language processing, multimodality processing, Internet-of-Things, edge computing, autonomous deriving, heterogeneous computing, wireless networks, and smart healthcare could be helpful in adding intelligence to urban living and will provide better solutions to deal with challenges in sustainable urban living.

The aim of this Special Issue is to present a multidisciplinary state-of-the-art reference regarding theoretical and real-world challenges, and innovative solutions by inviting high-quality research papers for AI applications in sustainable urban living.

The topics of interest for this Special Issue include but are not limited to:

  • Image classification, denoising, segmentation, object detection and tracking;
  • Video surveillance, video object detection, video object tracking, and video denoising applications;
  • Speech recognition (ASR), speech synthesis (TTS), speech denoising, and speaker ID applications for urban living;
  • Artificial-Intelligence-based music composition, analytics, recommendation, and instruction applications;
  • Natural language processing (NLP) applications for urban living;
  • Multimodality applications for urban living;
  • Artificial-Intelligence-based content generation applications for sustainable urban living;
  • Optical flow estimation for sustainable urban living;
  • Autonomous driving techniques’ applications;
  • IoT applications in urban living;
  • Edge computing models and lite deep learning models for real-time applications;
  • Heterogeneous computing for smart urban living;
  • Human–computer Interaction applications for smart and sustainable urban living;
  • Medical image processing and smart healthcare applications for urban living;
  • Wireless Artificial Intelligence applications for sustainable urban living;
  • Other Artificial Intelligence applications in the transformation of classical cities to smart cities.

Dr. Haoran Wei
Dr. Zhendong Wang
Dr. Yuchao Chang
Dr. Zhenghua Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial Intelligence
  • deep learning
  • machine learning
  • data mining
  • reinforcement learning
  • computer vision
  • NLP
  • heterogeneous computing
  • autonomous driving
  • video surveillance
  • medical image processing
  • speech and audio processing
  • music processing
  • healthcare monitoring system
  • wireless artificial intelligence
  • smart cities
  • smart home
  • Internet of Things

Published Papers (21 papers)

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Editorial

Jump to: Research, Review

4 pages, 178 KiB  
Editorial
Introducing the Special Issue on Artificial Intelligence Applications for Sustainable Urban Living
by Haoran Wei, Zhendong Wang, Yuchao Chang and Zhenghua Huang
Sustainability 2022, 14(20), 13631; https://doi.org/10.3390/su142013631 - 21 Oct 2022
Viewed by 937
Abstract
All global cities are in the process of transforming from classical cities to sustainable smart cities [...] Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)

Research

Jump to: Editorial, Review

15 pages, 3373 KiB  
Article
DSRA-DETR: An Improved DETR for Multiscale Traffic Sign Detection
by Jiaao Xia, Meijuan Li, Weikang Liu and Xuebo Chen
Sustainability 2023, 15(14), 10862; https://doi.org/10.3390/su151410862 - 11 Jul 2023
Cited by 1 | Viewed by 1171
Abstract
Traffic sign detection plays an important role in improving the capabilities of automated driving systems by addressing road safety challenges in sustainable urban living. In this paper, we present DSRA-DETR, a novel approach focused on improving multiscale detection performance. Our approach integrates the [...] Read more.
Traffic sign detection plays an important role in improving the capabilities of automated driving systems by addressing road safety challenges in sustainable urban living. In this paper, we present DSRA-DETR, a novel approach focused on improving multiscale detection performance. Our approach integrates the dilated spatial pyramid pooling model (DSPP) and the multiscale feature residual aggregation module (FRAM) to aggregate features at various scales. These modules excel at reducing feature noise and minimizing loss of low-level features during feature map extraction. Additionally, they enhance the model’s capability to detect objects at different scales, thereby improving the accuracy and robustness of traffic sign detection. We evaluate the performance of our method on two widely used datasets, the GTSDB and CCTSDB, and achieve impressive average accuracies (APs) of 76.13% and 78.24%, respectively. Compared with other well-known algorithms, our method shows a significant improvement in detection accuracy, demonstrating its superiority and generality. Our proposed method shows great potential for improving the performance of traffic sign detection for autonomous driving systems and will help in the development of safe and efficient autonomous driving technologies. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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14 pages, 1697 KiB  
Article
Capturing Dynamic Interests of Similar Users for POI Recommendation Using Self-Attention Mechanism
by Xinhua Fan, Yixin Hua, Yibing Cao and Xinke Zhao
Sustainability 2023, 15(6), 5034; https://doi.org/10.3390/su15065034 - 13 Mar 2023
Viewed by 1263
Abstract
The integration of location-based social networks and POI recommendation systems has the potential to enhance the urban experience by facilitating the exploration of new and relevant locales. The deployment of graph neural networks (GNNs) drives the development of POI recommendations, but this approach [...] Read more.
The integration of location-based social networks and POI recommendation systems has the potential to enhance the urban experience by facilitating the exploration of new and relevant locales. The deployment of graph neural networks (GNNs) drives the development of POI recommendations, but this approach also brings with it the challenge of over-smoothing, where information propagation between nodes in the graph can lead to an excessive homogenization of the data. In prior works that utilized GNNs for POI recommendation, the bipartite graphs constructed from users and POIs as nodes failed to incorporate temporal dynamics, limiting the scope of the analysis to only spatial structure information. To circumvent this issue, the incorporation of a temporal component can be introduced during the aggregation process of graph convolution. In light of these considerations, the present study proposes a novel regionalized temporal GCN (RST-GCN) recommendation model that leverages self-attention mechanism to capture various levels of temporal information to better reflect the dynamic changes of time. By combining the graph’s spatial structure with geospatial features, similar users are distributed into distinct regional subgraphs, effectively avoiding the influence of non-similar users. The efficacy of the proposed model has been demonstrated through empirical evaluations conducted on two real-world datasets. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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18 pages, 854 KiB  
Article
An Alternative Rural Housing Management Tool Empowered by a Bayesian Neural Classifier
by Mingzhi Song, Zheng Zhu, Peipei Wang, Kun Wang, Zhenqi Li, Cun Feng and Ming Shan
Sustainability 2023, 15(3), 1785; https://doi.org/10.3390/su15031785 - 17 Jan 2023
Viewed by 1131
Abstract
In developing countries, decision-making regarding old rural houses significantly relies on expert site investigations, which are criticized for being resource-demanding. This paper aims to construct an efficient Bayesian classifier for house safety and habitability risk evaluations, enabling people with none-civil-engineering backgrounds to make [...] Read more.
In developing countries, decision-making regarding old rural houses significantly relies on expert site investigations, which are criticized for being resource-demanding. This paper aims to construct an efficient Bayesian classifier for house safety and habitability risk evaluations, enabling people with none-civil-engineering backgrounds to make judgements comparable with experts so that house risk levels can be checked regularly at low costs. An initial list of critical risk factors for house safety and habitability was identified with a literature review and verified by expert discussions, field surveys, and Pearson’s Chi-square test of independence with 864 questionnaire samples. The model was constructed according to the causal mechanism between the verified factors and quantified using Bayesian belief network parameter learning. The model reached relatively high accuracy rates, ranging from 91.3% to 100.0% under different situations, including crosschecks with unused expert judgement samples with full input data, crosschecks with unused expert judgement samples with missing input data, and those involving local residents’ judgement. Model sensitivity analyses revealed walls; purlins and roof trusses; and foundations as the three most critical factors for safety and insulation and waterproofing; water and electricity; and fire safety for habitability. The identified list of critical factors contributes to the rural house evaluation and management strategies for developing countries. In addition, the established Bayesian classifier enables regular house checks on a regular and economical basis. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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18 pages, 915 KiB  
Article
An Intelligent Question-Answering Model over Educational Knowledge Graph for Sustainable Urban Living
by Yutong Fang, Jianzhi Deng, Fengming Zhang and Hongyan Wang
Sustainability 2023, 15(2), 1139; https://doi.org/10.3390/su15021139 - 07 Jan 2023
Cited by 3 | Viewed by 1534
Abstract
With the development of education informatization and the accumulation of massive educational resources and teaching data in urban environments, educational knowledge graphs that provide good conditions for developing data-driven intelligent education have been proposed. Based on such educational knowledge graphs, the question-answering method [...] Read more.
With the development of education informatization and the accumulation of massive educational resources and teaching data in urban environments, educational knowledge graphs that provide good conditions for developing data-driven intelligent education have been proposed. Based on such educational knowledge graphs, the question-answering method can provide students with immediate coaching and significantly increase their learning interest and productivity. However, there is little research on knowledge graph question-answering focused on the educational field. Students tend to consult complex questions that require reasoning; however, the existing QA system cannot satisfy their complex information needs. To help improve sustainable learning efficiency, we propose a novel intelligent question-answering model applied in smart cities, which can reason over the educational knowledge graph to locate the answers to given questions. Our approach uses a highly expressive bilinear graph neural network technology to perform forward reasoning, utilizing the contextual information between graph nodes to improve reasoning ability. On this basis, we propose two-teacher knowledge distillation. We construct two distinct teacher networks by combining forward and backward reasoning, then incorporate the intermediate supervision signals from the two networks to guide the reasoning process, thereby mitigating the phenomenon of spurious path reasoning. Extensive experiments on the MOOC Q&A dataset prove the effectiveness of our approach. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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21 pages, 8370 KiB  
Article
Fault Prediction Recommender Model for IoT Enabled Sensors Based Workplace
by Mudita Uppal, Deepali Gupta, Amena Mahmoud, M. A. Elmagzoub, Adel Sulaiman, Mana Saleh Al Reshan, Asadullah Shaikh and Sapna Juneja
Sustainability 2023, 15(2), 1060; https://doi.org/10.3390/su15021060 - 06 Jan 2023
Cited by 9 | Viewed by 2235
Abstract
Industry 5.0 benefits from advancements being made in the field of machine learning and the Internet of Things. Different sensors have been installed in a variety of IoT devices present in different industries such as transportation, healthcare, manufacturing, agriculture, etc. The sensors present [...] Read more.
Industry 5.0 benefits from advancements being made in the field of machine learning and the Internet of Things. Different sensors have been installed in a variety of IoT devices present in different industries such as transportation, healthcare, manufacturing, agriculture, etc. The sensors present in these devices should automatically predict errors due to the extensive use of sensors in urban living. To ensure the integrity, precision, security, dependability and fidelity of sensor nodes, it is, therefore, necessary to foresee faults before they occur. Additionally, as more data is being collected by these devices every day, cloud computing becomes more necessary for sustainable urban living. The proposed model emphasizes solution recommendations for faults that occurred in real-life smart devices to mitigate faults at an early stage, which is a key requirement in today’s smart offices. The proposed model monitors the real-time health of IoT devices through an ML algorithm to make devices more efficient and increase the quality of life. Through the use of K-Nearest Neighbor, Decision Tree, Gaussian Naive Bayes and Random Forest approach, the proposed fault prediction recommender model has been evaluated and Random Forest shows the highest accuracy compared to other classifiers. Several performance indicators such as recall, accuracy, F1 score and precision were utilized to examine the performance of the model. The results have demonstrated the effectiveness of ML techniques applied to sensors in predicting faults in smart offices with Random Forest being observed as the best technique with a maximum accuracy of 94.27%. In future, deep learning can also be applied to bigger datasets to provide more accurate results. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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25 pages, 10719 KiB  
Article
Updated Prediction of Air Quality Based on Kalman-Attention-LSTM Network
by Hao Zhou, Tao Wang, Hongchao Zhao and Zicheng Wang
Sustainability 2023, 15(1), 356; https://doi.org/10.3390/su15010356 - 26 Dec 2022
Cited by 9 | Viewed by 2729
Abstract
The WRF-CMAQ (Weather research and forecast-community multiscale air quality) simulation system is commonly used as the first prediction model of air pollutant concentration, but its prediction accuracy is not ideal. Considering the complexity of air quality prediction and the high-performance advantages of deep [...] Read more.
The WRF-CMAQ (Weather research and forecast-community multiscale air quality) simulation system is commonly used as the first prediction model of air pollutant concentration, but its prediction accuracy is not ideal. Considering the complexity of air quality prediction and the high-performance advantages of deep learning methods, this paper proposes a second prediction method of air pollutant concentration based on the Kalman-attention-LSTM (Kalman filter, attention and long short-term memory) model. Firstly, an exploratory analysis is made between the actual environmental measurement data from the monitoring site and the first forecast data from the WRF-CMAQ model. An air quality index (AQI) was used as a measure of air pollution degree. Then, the Kalman filter (KF) is used to fuse the actual environmental measurement data from the monitoring site and the first forecast results from the WRF-CMAQ model. Finally, the long short-term memory (LSTM) model with the attention mechanism is used as a single factor prediction model for an AQI prediction. In the prediction of O3 which is the main pollutant affecting the AQI, the results show that the second prediction based on the Kalman-attention-LSTM model features a better fitting effect, compared with the six models. In the first prediction (from the WRF-CMAQ model), for the RNN, GRU, LSTM, attention-LSTM and Kalman-LSTM, SE improved by 83.26%, 51.64%, 43.58%, 45%, 26% and 29%, respectively, RMSE improved by 83.16%, 51.52%, 43.21%, 44.59%, 26.07% and 28.32%, respectively, MAE improved by 80.49%, 56.96%, 46.75%, 49.97%, 26.04% and 27.36%, respectively, and R-Square improved by 85.3%, 16.4%, 10.3%, 11.5%, 2.7% and 3.3%, respectively. However, the prediction results for the Kalman-attention-LSTM model proposed in this paper for other five different pollutants (SO2, NO2, PM10, PM2.5 and CO) all have smaller SE, RMSE and MAE, and better R-square. The accuracy improvement is significant and has good application prospects. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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22 pages, 5209 KiB  
Article
Zero-Shot Video Grounding for Automatic Video Understanding in Sustainable Smart Cities
by Ping Wang , Li Sun, Liuan Wang and Jun Sun
Sustainability 2023, 15(1), 153; https://doi.org/10.3390/su15010153 - 22 Dec 2022
Cited by 1 | Viewed by 1270
Abstract
Automatic video understanding is a crucial piece of technology which promotes urban sustainability. Video grounding is a fundamental component of video understanding that has been evolving quickly in recent years, but its use is restricted due to the high labeling costs and typical [...] Read more.
Automatic video understanding is a crucial piece of technology which promotes urban sustainability. Video grounding is a fundamental component of video understanding that has been evolving quickly in recent years, but its use is restricted due to the high labeling costs and typical performance limitations imposed by the pre-defined training dataset. In this paper, a novel atom-based zero-shot video grounding (AZVG) method is proposed to retrieve the segments in the video that correspond to a given input sentence. Although it is training-free, the performance of AZVG is competitive to the weakly supervised methods and better than unsupervised SOTA methods on the Charades-STA dataset. The method can support flexible queries as well as different video content. It can play an important role in a wider range of urban living applications. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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15 pages, 10492 KiB  
Article
CGAN-Assisted Renovation of the Styles and Features of Street Facades—A Case Study of the Wuyi Area in Fujian, China
by Lei Zhang, Liang Zheng, Yile Chen, Lei Huang and Shihui Zhou
Sustainability 2022, 14(24), 16575; https://doi.org/10.3390/su142416575 - 10 Dec 2022
Cited by 4 | Viewed by 1741
Abstract
With the development of society and the economy, the unified planning of architectural styles has become a significant problem in the balance between urban expansion and the protection of traditional buildings in villages and towns. This also allows people to re-examine the appearance [...] Read more.
With the development of society and the economy, the unified planning of architectural styles has become a significant problem in the balance between urban expansion and the protection of traditional buildings in villages and towns. This also allows people to re-examine the appearance of and quality of life, experienced by those in traditional village buildings. This research employs a conditional generative adversarial network (CGAN) to develop a generative technique for designing building facades in villages and cities. The provided results can be used to develop schemes and as design references for building facade design, enhancing the design efficiency of building facades. Simultaneously, we utilized this model for the rehabilitation of building facades in villages and towns, as well as in the visual design of rural tourism products, demonstrating its practical usefulness and design-related potential. We took villages and towns in the Wuyishan area of China as an example and carried out model training, image generation, and a comparison of the derivation results of different assumed buildings and product contours. The research shows that: (1) CGAN can be used to produce and supply reference schemes for conventional civil construction facade design in rural and urban areas. (2) In terms of adaptability, CGAN may develop architectural facade design schemes with a reference value for the hypothetical experimental building facades, and it can play a role in other design domains, as well. (3) The construction of this method is not only applicable to villages and towns in the World Heritage es Cities Programme, but can be further promoted and used in the future for cities and villages that have a demand for architectural style consistency. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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12 pages, 2763 KiB  
Article
Neural Network Based on Multi-Scale Saliency Fusion for Traffic Signs Detection
by Haohao Zou, Huawei Zhan and Linqing Zhang
Sustainability 2022, 14(24), 16491; https://doi.org/10.3390/su142416491 - 09 Dec 2022
Cited by 6 | Viewed by 1118
Abstract
Aiming at recognizing small-scale and complex traffic signs in the driving environment, a traffic sign detection algorithm YOLO-FAM based on YOLOv5 is proposed. Firstly, a new backbone network, ShuffleNet-v2, is used to reduce the algorithm’s parameters, realize lightweight detection, and improve detection speed. [...] Read more.
Aiming at recognizing small-scale and complex traffic signs in the driving environment, a traffic sign detection algorithm YOLO-FAM based on YOLOv5 is proposed. Firstly, a new backbone network, ShuffleNet-v2, is used to reduce the algorithm’s parameters, realize lightweight detection, and improve detection speed. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) structure is introduced to capture multi-scale context information, so as to obtain more feature information and improve detection accuracy. Finally, location information is added to the channel attention using the Coordinated Attention (CA) mechanism, thus enhancing the feature expression. The experimental results show that compared with YOLOv5, the mAP value of this method increased by 2.27%. Our approach can be effectively applied to recognizing traffic signs in complex scenes. At road intersections, traffic planners can better plan traffic and avoid traffic jams. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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14 pages, 2249 KiB  
Article
Understanding User Preferences in Location-Based Social Networks via a Novel Self-Attention Mechanism
by Lei Shi, Jia Luo, Peiying Zhang, Hongqi Han, Didier El Baz, Gang Cheng and Zeyu Liang
Sustainability 2022, 14(24), 16414; https://doi.org/10.3390/su142416414 - 08 Dec 2022
Cited by 2 | Viewed by 1373
Abstract
The check-in behaviors of users are ubiquitous in location-based social networks in urban living. Understanding user preferences is critical to improving the recommendation services of social platforms. In addition, great quality of recommendation is also beneficial to sustainable urban living since the user [...] Read more.
The check-in behaviors of users are ubiquitous in location-based social networks in urban living. Understanding user preferences is critical to improving the recommendation services of social platforms. In addition, great quality of recommendation is also beneficial to sustainable urban living since the user can easily find the point of interest (POI) to visit, which avoids unnecessary consumption, such as a longer time taken for searching or driving. To capture user preferences from their check-in behaviors, advanced methods transform historical records into graph structure data and further leverage graph deep learning-based techniques to learn user preferences. Despite their effectiveness, existing graph deep learning-based methods are limited to the capture of the deep graph’s structural information due to inherent limitations, such as the over-smoothing problem in graph neural networks, further leading to suboptimal performance. To address the above issues, we propose a novel method built on Transformer architecture named spatiotemporal aware transformer (STAT) via a novel graphically aware attention mechanism. In addition, a new temporally aware sampling strategy is developed to reduce the computational cost and enable STAT to deal with large graphs. Extensive experiments on real-world datasets have demonstrated the superiority of the STAT compared to state-of-the-art POI recommendation methods. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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23 pages, 3702 KiB  
Article
Improving Temporal Event Scheduling through STEP Perpetual Learning
by Jiahua Tang, Du Zhang, Xibin Sun and Haiou Qin
Sustainability 2022, 14(23), 16178; https://doi.org/10.3390/su142316178 - 03 Dec 2022
Viewed by 1164
Abstract
Currently, most machine learning applications follow a one-off learning process: given a static dataset and a learning algorithm, generate a model for a task. These applications can neither adapt to a dynamic and changing environment, nor accomplish incremental task performance improvement continuously. STEP [...] Read more.
Currently, most machine learning applications follow a one-off learning process: given a static dataset and a learning algorithm, generate a model for a task. These applications can neither adapt to a dynamic and changing environment, nor accomplish incremental task performance improvement continuously. STEP perpetual learning, by continuous knowledge refinement through sequential learning episodes, emphasizes the accomplishment of incremental task performance improvement. In this paper, we describe how a personalized temporal event scheduling system SmartCalendar, can benefit from STEP perpetual learning. We adopt the interval temporal logic to represent events’ temporal relationships and determine if events are temporally inconsistent. To provide strategies that approach user preferences for handling temporal inconsistencies, we propose SmartCalendar to recognize, resolve and learn from temporal inconsistencies based on STEP perpetual learning. SmartCalendar has several cornerstones: similarity measures for temporal inconsistency; a sparse decomposition method to utilize historical data; and a loss function based on cross-entropy to optimize performance. The experimental results on the collected dataset show that SmartCalendar incrementally improves its scheduling performance and substantially outperforms comparison methods. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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15 pages, 2045 KiB  
Article
Water Column Detection Method at Impact Point Based on Improved YOLOv4 Algorithm
by Jiaowei Shi, Shiyan Sun, Zhangsong Shi, Chaobing Zheng and Bo She
Sustainability 2022, 14(22), 15329; https://doi.org/10.3390/su142215329 - 18 Nov 2022
Viewed by 976
Abstract
For a long time, the water column at the impact point of a naval gun firing at the sea has mainly depended on manual detection methods for locating, which has problems such as low accuracy, subjectivity and inefficiency. In order to solve the [...] Read more.
For a long time, the water column at the impact point of a naval gun firing at the sea has mainly depended on manual detection methods for locating, which has problems such as low accuracy, subjectivity and inefficiency. In order to solve the above problems, this paper proposes a water column detection method based on an improved you-only-look-once version 4 (YOLOv4) algorithm. Firstly, the method detects the sea antenna through the Hoffman line detection method to constrain the sensitive area in the current detection image so as to improve the accuracy of water column detection; secondly, density-based spatial clustering of applications with noise (DBSCAN) + K-means clustering algorithm is used to obtain a better prior bounding box, which is input into the YOLOv4 network to improve the positioning accuracy of the water column; finally, the convolutional block attention module (CBAM) is added in the PANet structure to improve the detection accuracy of the water column. The experimental results show that the above algorithm can effectively improve the detection accuracy and positioning accuracy of the water column at the impact point. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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28 pages, 6537 KiB  
Article
Research on Sustainable Reuse of Urban Ruins Based on Artificial Intelligence Technology: A Study of Guangzhou
by Qi Duan, Lihui Qi, Renyu Cao and Peng Si
Sustainability 2022, 14(22), 14812; https://doi.org/10.3390/su142214812 - 10 Nov 2022
Cited by 2 | Viewed by 2168
Abstract
In recent years, with the continuous deepening of the urbanization process, the problem of urban ruins (URs) has become prominent. This significantly affects the happiness of residents around the URs, the overall image of the city, and the environment, and it has become [...] Read more.
In recent years, with the continuous deepening of the urbanization process, the problem of urban ruins (URs) has become prominent. This significantly affects the happiness of residents around the URs, the overall image of the city, and the environment, and it has become an important issue in urban construction. At present, the types of urban ruins mainly include industrial ruins, abandoned urban buildings, and war sites. Generally, methods such as demolition and reconstruction of original buildings or upgrading and transformation are used to reuse URs, and some of them have achieved fruitful results. However, the current renovation of URs is based on fragmented renovation strategies for different URs without a systematic and universally applicable renovation methodology. With the development of artificial intelligence, technologies such as Generative Adversarial Network (GAN), Easy DL, and Natural Language Processing (NLP) can provide technical support for urban ruin reconstruction, from design to operation. Specifically in the present study, the ten representative URs in Guangzhou are first evaluated by the Analytic Hierarchy Process and then combined with AI methods, such as the adversarial generative networks and big data applications, into the reuse design of URs. Finally, a complete research system is established to implement URs’ projects, which provides a clearer systematic planning strategy for the reuse of URs in the future. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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31 pages, 4470 KiB  
Article
Revealing the Impact of Urban Form on COVID-19 Based on Machine Learning: Taking Macau as an Example
by Yile Chen, Liang Zheng, Junxin Song, Linsheng Huang and Jianyi Zheng
Sustainability 2022, 14(21), 14341; https://doi.org/10.3390/su142114341 - 02 Nov 2022
Cited by 2 | Viewed by 2182
Abstract
The COVID-19 pandemic has led to a re-examination of the urban space, and the field of planning and architecture is no exception. In this study, a conditional generative adversarial network (CGAN) is used to construct a method for deriving the distribution of urban [...] Read more.
The COVID-19 pandemic has led to a re-examination of the urban space, and the field of planning and architecture is no exception. In this study, a conditional generative adversarial network (CGAN) is used to construct a method for deriving the distribution of urban texture through the distribution hotspots of the COVID-19 epidemic. At the same time, the relationship between urban form and the COVID-19 epidemic is established, so that the machine can automatically deduce and calculate the appearance of urban forms that are prone to epidemics and may have high risks, which has application value and potential in the field of planning and design. In this study, taking Macau as an example, this method was used to conduct model training, image generation, and comparison of the derivation results of different assumed epidemic distribution degrees. The implications of this study for urban planning are as follows: (1) there is a correlation between different urban forms and the distribution of epidemics, and CGAN can be used to predict urban forms with high epidemic risk; (2) large-scale buildings and high-density buildings can promote the distribution of the COVID-19 epidemic; (3) green public open spaces and squares have an inhibitory effect on the distribution of the COVID-19 epidemic; and (4) reducing the volume and density of buildings and increasing the area of green public open spaces and squares can help reduce the distribution of the COVID-19 epidemic. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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19 pages, 7424 KiB  
Article
Deep USRNet Reconstruction Method Based on Combined Attention Mechanism
by Long Chen, Shuiping Zhang, Haihui Wang, Pengjia Ma, Zhiwei Ma and Gonghao Duan
Sustainability 2022, 14(21), 14151; https://doi.org/10.3390/su142114151 - 30 Oct 2022
Cited by 1 | Viewed by 1231
Abstract
Single image super-resolution (SISR) based on deep learning is a key research problem in the field of computer vision. However, existing super-resolution reconstruction algorithms often improve the quality of image reconstruction through a single network depth, ignoring the problems of reconstructing image texture [...] Read more.
Single image super-resolution (SISR) based on deep learning is a key research problem in the field of computer vision. However, existing super-resolution reconstruction algorithms often improve the quality of image reconstruction through a single network depth, ignoring the problems of reconstructing image texture structure and easy overfitting of network training. Therefore, this paper proposes a deep unfolding super-resolution network (USRNet) reconstruction method under the integrating channel attention mechanism, which is expected to improve the image resolution and restore the high-frequency information of the image. Thus, the image appears sharper. First, by assigning different weights to features, focusing on more important features and suppressing unimportant features, the details such as image edges and textures are better recovered, and the generalization ability is improved to cope with more complex scenes. Then, the CA (Channel Attention) module is added to USRNet, and the network depth is increased to better express high-frequency features; multi-channel mapping is introduced to extract richer features and enhance the super-resolution reconstruction effect of the model. The experimental results show that the USRNet with integrating channel attention has a faster convergence rate, is not prone to overfitting, and can be converged after 10,000 iterations; the average peak signal-to-noise ratios on the Set5 and Set12 datasets after the side length enlarged by two times are, respectively, 32.23 dB and 29.72 dB, and are dramatically improved compared with SRCNN, SRMD, PAN, and RCAN. The algorithm can generate high-resolution images with clear outlines, and the super-resolution effect is better. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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16 pages, 3131 KiB  
Article
Global Attention Super-Resolution Algorithm for Nature Image Edge Enhancement
by Zhihao Zhang, Zhitong Su, Wei Song and Keqing Ning
Sustainability 2022, 14(21), 13865; https://doi.org/10.3390/su142113865 - 25 Oct 2022
Cited by 1 | Viewed by 1273
Abstract
Single-image super-resolution (SR) has long been a research hotspot in computer vision, playing a crucial role in practical applications such as medical imaging, public security and remote sensing imagery. However, all currently available methods focus on reconstructing texture details, resulting in blurred edges [...] Read more.
Single-image super-resolution (SR) has long been a research hotspot in computer vision, playing a crucial role in practical applications such as medical imaging, public security and remote sensing imagery. However, all currently available methods focus on reconstructing texture details, resulting in blurred edges and incomplete structures in the reconstructed images. To address this problem, an edge-enhancement-based global attention image super-resolution network (EGAN) combining channel- and self-attention mechanisms is proposed for modeling the hierarchical features and intra-layer features in multiple dimensions. Specifically, the channel contrast-aware attention (CCA) module learns the correlations between the intra-layer feature channels and enhances the contrast in the feature maps for richer features in the edge structures. The cyclic shift window multi-head self-attention (CS-MSA) module captures the long-range dependencies between layered features and captures more valuable features in the global information network. Experiments are conducted on five benchmark datasets for × 2, × 3 and × 4 SR. The experimental results show that for × 4 SR, our network improves the average PSNR by 0.12 dB, 0.19 dB and 0.12 dB over RCAN, HAN and NLSN, respectively, and can reconstruct a clear and complete edge structure. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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22 pages, 4754 KiB  
Article
Exploring the Visual Guidance of Motor Imagery in Sustainable Brain–Computer Interfaces
by Cheng Yang, Lei Kong, Zhichao Zhang, Ye Tao and Xiaoyu Chen
Sustainability 2022, 14(21), 13844; https://doi.org/10.3390/su142113844 - 25 Oct 2022
Cited by 2 | Viewed by 996
Abstract
Motor imagery brain–computer interface (MI-BCI) systems hold the possibility of restoring motor function and also offer the possibility of sustainable autonomous living for individuals with various motor and sensory impairments. When utilizing the MI-BCI, the user’s performance impacts the system’s overall accuracy, and [...] Read more.
Motor imagery brain–computer interface (MI-BCI) systems hold the possibility of restoring motor function and also offer the possibility of sustainable autonomous living for individuals with various motor and sensory impairments. When utilizing the MI-BCI, the user’s performance impacts the system’s overall accuracy, and concentrating on the user’s mental load enables a better evaluation of the system’s overall performance. The impacts of various levels of abstraction on visual guidance of mental training in motor imagery (MI) may be comprehended. We proposed hypotheses about the effects of visually guided abstraction on brain activity, mental load, and MI-BCI performance, then used the event-related desynchronization (ERD) value to measure the user’s brain activity, extracted the brain power spectral density (PSD) to measure the brain load, and finally classified the left- and right-handed MI through a support vector machine (SVM) classifier. The results showed that visual guidance with a low level of abstraction could help users to achieve the highest brain activity and the lowest mental load, and the highest accuracy rate of MI classification was 97.14%. The findings imply that to improve brain–computer interaction and enable those less capable to regain their mobility, visual guidance with a low level of abstraction should be employed when training brain–computer interface users. We anticipate that the results of this study will have considerable implications for human-computer interaction research in BCI. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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22 pages, 4202 KiB  
Article
MEC-Enabled Fine-Grained Task Offloading for UAV Networks in Urban Environments
by Sicong Yu, Huiji Zheng and Caihong Ma
Sustainability 2022, 14(21), 13809; https://doi.org/10.3390/su142113809 - 25 Oct 2022
Cited by 1 | Viewed by 1400
Abstract
In recent years, with the continuous development of information technology, the amount of data generated and hosted by cloud service platforms in urban environments is unprecedented. Mobile edge computing (MEC) is combined with UAV networks to better realize the ability to provide nearby [...] Read more.
In recent years, with the continuous development of information technology, the amount of data generated and hosted by cloud service platforms in urban environments is unprecedented. Mobile edge computing (MEC) is combined with UAV networks to better realize the ability to provide nearby services to a large number of terminal devices in cities. Unmanned aerial vehicles (UAVs) are highly maneuverable and inexpensive and are good carriers for carrying MEC platforms. In UAV edge networks, we usually face the problem of fine-grained task offloading based on relevant features of urban environments. We need to address high energy consumption and task processing delays to help achieve urban sustainability goals. Therefore, we combine the software definition network (SDN) technology and, on this basis, we propose two task offloading strategies based on an improved EFO intelligent algorithm for different user scales. At the same time, we run the proposed offloading system in the UAV sensor. The experiment shows that, compared with the traditional strategy, the unloading efficiency of the proposed method can be improved by about 10%. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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19 pages, 3394 KiB  
Article
Under the Background of AI Application, Research on the Impact of Science and Technology Innovation and Industrial Structure Upgrading on the Sustainable and High-Quality Development of Regional Economies
by Bowen Li, Fangxin Jiang, Hongjie Xia and Jiawei Pan
Sustainability 2022, 14(18), 11331; https://doi.org/10.3390/su141811331 - 09 Sep 2022
Cited by 6 | Viewed by 2205
Abstract
In the opening year of the 14th Five-Year Plan, China has made significant progress in upgrading its industrial structure and improving the quality of economic growth based on the goal of technological self-sufficiency and self-improvement, and more and more artificial intelligence is being [...] Read more.
In the opening year of the 14th Five-Year Plan, China has made significant progress in upgrading its industrial structure and improving the quality of economic growth based on the goal of technological self-sufficiency and self-improvement, and more and more artificial intelligence is being used in the market. Artificial intelligence is playing an important role in the innovation and market construction of the economy. This manuscript constructs a spatial Durbin model by measuring the level of science and technology innovation and sustainable high-quality economic development of 283 prefecture-level cities in China from 2010–2019, and explores the effects and mechanisms of science and technology innovation in promoting the sustainable high-quality economic development of Chinese cities under the background of AI application. It is found that China’s science and technology innovation not only promotes the improvement of economic quality in the region, but also has positive spatial spillover, leading to the improvement of economic quality in neighboring regions. In combination with this established background, this manuscript introduces the variable of industrial structure upgrading and explores its mechanism of action in this field. Research shows that industrial structure upgrading is an important transmission path for science and technology innovation to promote sustainable and high-quality economic development. At the same time, considering the impact of the interaction between science and technology innovation and industrial structure upgrading on the sustainable and high-quality development of regional economy, this manuscript also constitutes an innovative study. Therefore, the government should continuously promote science and technology innovation and industrial structure upgrading, take advantage of China’s mega-market, make full use of the spatial spillover effect, guide the effective allocation of innovation resources, promote the orderly flow and reasonable allocation of innovation factors, and improve the institutional mechanism for promoting the market-oriented application of independent innovation results to support sustainable and high-quality economic development. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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Review

Jump to: Editorial, Research

29 pages, 2107 KiB  
Review
Security Issues and Solutions for Connected and Autonomous Vehicles in a Sustainable City: A Survey
by Zhendong Wang, Haoran Wei, Jianda Wang, Xiaoming Zeng and Yuchao Chang
Sustainability 2022, 14(19), 12409; https://doi.org/10.3390/su141912409 - 29 Sep 2022
Cited by 15 | Viewed by 4880
Abstract
Connected and Autonomous Vehicles (CAVs) combine technologies of autonomous vehicles (AVs) and connected vehicles (CVs) to develop quicker, more reliable, and safer traffic. Artificial Intelligence (AI)-based CAV solutions play significant roles in sustainable cities. The convergence imposes stringent security requirements for CAV safety [...] Read more.
Connected and Autonomous Vehicles (CAVs) combine technologies of autonomous vehicles (AVs) and connected vehicles (CVs) to develop quicker, more reliable, and safer traffic. Artificial Intelligence (AI)-based CAV solutions play significant roles in sustainable cities. The convergence imposes stringent security requirements for CAV safety and reliability. In practice, vehicles are developed with increased automation and connectivity. Increased automation increases the reliance on the sensor-based technologies and decreases the reliance on the driver; increased connectivity increases the exposures of vehicles’ vulnerability and increases the risk for an adversary to implement a cyber-attack. Much work has been dedicated to identifying the security vulnerabilities and recommending mitigation techniques associated with different sensors, controllers, and connection mechanisms, respectively. However, there is an absence of comprehensive and in-depth studies to identify how the cyber-attacks exploit the vehicles’ vulnerabilities to negatively impact the performance and operations of CAVs. In this survey, we set out to thoroughly review the security issues introduced by AV and CV technologies, analyze how the cyber-attacks impact the performance of CAVs, and summarize the solutions correspondingly. The impact of cyber-attacks on the performance of CAVs is elaborated from both viewpoints of intra-vehicle systems and inter-vehicle systems. We pointed out that securing the perception and operations of CAVs would be the top requirement to enable CAVs to be applied safely and reliably in practice. Additionally, we suggested to utilize cloud and new AI methods to defend against smart cyber-attacks on CAVs. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
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