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Advances of photothermal chemistry in photocatalysis,thermocatalysis,and synergetic photothermocatalysis for solar-to-fuel generation

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Nano research 2022年第12期15卷 9985-10005页

作者： Minmin Gao Tianxi Zhang Ghim Wei Ho Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore117583Singapore Department of Chemical and Biomolecular Engineering National University of Singapore4 Engineering Drive 4Singapore117585Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research(A*STAR)3 Research LinkSingapore117602Singapore

The urgency of reducing pollutants and greenhouse gas emissions while maintaining fuel supply for the development of society remains one of the greatest *** energy,a clean and sustainable energy resource,can be converted into fuels through solar-driven catalysis,and this provides an attractive solution for future energy *** current development of photothermal catalysis(PTC)based on the integration of solar thermal and photochemical contributions is becoming increasingly popular for full spectrum *** combination of the thermochemical and photochemical processes synergistically drives the catalytic reactions efficiently under relatively mild *** this review,the mechanisms of PTC are classified based on driving forces and the benefits of photothermal effects in different PTC reactions are ***,the techniques for differentiating and quantifying the various effects of PTC,including experimental designs,thermometry characterization techniques,and computational studies,are ***,the major determinant properties and architectural designs for efficient photothermal catalysts are ***,applications for fuel generation through water splitting and carbon dioxide reduction are ***,the current challenges and future directions of PTC are *** article aims to provide a comprehensive review of the current advances in PTC along with a guide for understanding the mechanisms and rational material designs to pursue solar fuel that would diversify and increase the sustainability of our energy supply.

关键词： photothermal chemistry photocatalysis thermocatalysis water splitting CO_(2)conversion solar fuel

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Exploring the Potential of AI-Driven Systems for Automating Data science

Exploring the Potential of AI-Driven Systems for Automating ...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Srivastava, Adarsh Katyal, Ashu Srisainath, R. Kshirsagar, Pranali S. Singh, Akhilendra Pratap Rajesh, A. Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350329773

this paper discusses the capacity of artificial intelligence (AI)-pushed structures for automating records technological know-how duties including records cleaning, function engineering, and version improvement. We argue that those systems provide a promising opportunity to automate the hard work-extensive and time-ingesting venture of data technological know-how, enabling records scientists to recognition on the more creative factors of the paintings. We begin by using offering a brief evaluation of AI and its capacity packages after which study the existing AI-driven data technology gear on the market. We then describe a few revolutionary applications of AI and discuss their capability to improve existing facts technology approaches. Ultimately, we talk the demanding situations and barriers of AI-driven structures and advocate approaches to address them. We finish by using emphasizing the importance of responsibly integrating AI into the statistics science field and highlighting the capacity of collaboration between AI and facts technological know-how specialists. © 2023 IEEE.

关键词： technology transfer

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Predicting Purchases and Personalizing the Customer Journey with Artificial Intelligence 11

Predicting Purchases and Personalizing the Customer Journey ...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Malik, Varun Mittal, Ruchi Chaudhry, Rishi Yadav, Suman Avdhesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Institute of Management Studies and Research Maharshi Dayanand University Haryana Rohtak India Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350357

A mastery of consumer behaviour analysis and the ability to personalise the customer experience are prerequisites for success in today's cutthroat business climate. In order to improve the customer experience and make purchase predictions, this research explores the world of artificial intelligence. Using a systematic three-step technique, this study aims to improve consumer purchase prediction and provide individualised customer experience. The dataset is preprocessed using Enhanced Z-score normalisation first. This will ensure uniformity and mitigate the impact of extreme cases. After a better collection of features is found using Recursive Feature Elimination (RFE), the dataset is optimised for classification tasks. Lastly, state-of-the-art technology is used in the research via the usage of Enhanced Long Short-Term Memory (LSTM) networks for classification and prediction. Better LSTM networks may be able to detect nuanced changes in customer behaviour, resulting in more accurate predictions. Our study aims to contribute to the advancement of AI applications in CRM by following this rigorous process. We want to help companies better their strategy, offer more personalised experiences, and boost the possibility of consumers completing a purchase by delivering relevant insights. © 2024 IEEE.

关键词： Long short-term memory

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Deep Learning based Advanced Image Recognition in Autonomous Vehicles in Industry 4.0 3

Deep Learning based Advanced Image Recognition in Autonomous...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Aruna, R. Manjula Devi, P. Suresh, D. Arul Pandiyan, M. Sathya, S. Geetha, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vtu Affiliated Amc Engineering College Department of Information Science and Engineering Banglore India Unnamalai College of Arts and Science Department of Computer Science Kovilpatti India Bannari Amman Institute of Technolgy Department of Information Science and Engineering Erode India Bannari Kongu Engineering College Department of Mathematics Erode India Dean of Academics and Research Jct College of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350375190

The automation with integration of numerous devices are important factors in the manufacturing and production sectors in Industry 4.0. This involves the use of autonomous vehicles for diverse applications. They are based on advanced image recognition systems. The efficient functioning of the system is achieved using deep learning techniques. These networks have the ability to automatically learn from complex data. These deep learning techniques are used for the detection of obstacles, lane following and object recognition. The various constraints in obtaining the optimum performance are achieved using ant colony optimization techniques. This helps in obtaining the overall efficiency of autonomous operations. They helps in evaluating optimum solutions through iterative updation of the artificial pheromones. They are used for the optimization of hyperparameters and training parameters. They helps in fine tuning of the model for various requirements of autonomous vehicles in Industry 4.0. They results in reduced computational costs with improved convergence. The integration of deep learning with ACO helps in overcoming the challenges in the deployment of advanced image recognition systems in autonomous vehicles. They helps in improving the perception capabilities of the vehicles. These autonomous vehicles provide adaptive solutions for various processes in the manufacturing and production sectors. © 2024 IEEE.

关键词： Image recognition

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Enhancing Road Safety Through computer Vision: A Comprehensive Approach Using Associative Rule Mining

Enhancing Road Safety Through Computer Vision: A Comprehensi...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Shoran, Preety Yadav, Meenakshi Indu Saxena, Esha Gandhi, Parul School of Sciences Christ University Delhi NCR India Galgotia College of Engineering and Technology Department of Information Technology Gr. Noida India Guru Tegh Bahadur Institute of Technology Department of Computer Science and Engineering Delhi India Manav Rachna International Institute of Research and Studies Department of Computer Applications Faridabad India

ISBN: (纸本)9798350388534

Ensuring road safety is of utmost importance in contemporary urban settings, requiring creative strategies to prevent accidents and efficiently manage traffic. In this paper, we present a solution that utilizes computer vision techniques, with a specific emphasis on using associative rule mining. Our approach combines face tracking and vehicle detection using associative rule mining (ARM) to gain a comprehensive picture of traffic dynamics and improve road safety. The face tracking component enables the detection of distracted drivers or pedestrians, providing valuable information about potential safety risks. Our research adds to the expanding knowledge in the field of intelligent transportation systems by introducing a new framework that tackles the complex difficulties of road safety. This framework combines face tracking, vehicle identification, and associative rule mining to achieve its objectives. The proposed method shows potential for developing road networks that are both safer and more efficient in the age of smart cities. © 2024 IEEE.

关键词： Intelligent systems

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DQNANFCT: design of a deep Q-learning network for augmented network forensics via integrated contextual trust operations

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International Journal of Information technology (Singapore) 2023年第5期15卷 2729-2739页

作者： Choudhary, Ashutosh Kumar Rahamatkar, Surendra Purbey, Suniti Research Scholar Computer Science & Engineering Department Amity School of Engineering & Technology Amity University Chhattisgarh India Department of IT GMR Institute of Technology AP Rajam India Amity School of Engineering & Technology Amity University Chhattisgarh India Department of CSE GMR Institute of Technology AP Rajam India

Network forensics helps analyze pre-emptive attacks and identify attackers using temporal behavior patterns. Most deep learning methods to design high-efficiency forensic models are too complex for real-time deployment. These context-insensitive models cannot scale to multitier networks. This text proposes a deep Q-Learning network for augmented network forensics via integrated contextual trust operations to address these issues. The model collects large-scale IP & network entity-specific traffic data from different network components and processes it using pattern analysis Neural Networks. (NN). This helps predict future traffic patterns, which deep Q-Learning Network (DQLN) processes compare to attack patterns. DQLN maps attack types to participating IPs and network entities. Contextual Trust Analysis (CTA) uses temporal behavioural patterns to estimate risk severity levels for these IPs and network entities. The model uses contextual trust analysis to predict future attacks that could degrade network performance and block IPs or restrict access. Thus, the model detects attacks 4.5% more accurately and 2.9% more precisely. The model's light-weighted DQLN & CTA operations improved detection speed by 8.3% compared to standard forensic methods, making it useful for many real-time network deployments. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Context Entity Forensics IP Network Patterns Q-Learning Temporal Trust

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Optimize the network of YOLOv8 for grape detection task 9

Optimize the network of YOLOv8 for grape detection task

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9th International Symposium on Advances in Electrical, Electronics, and computer engineering, ISAEECE 2024

作者： Wu, Di Ma, Zhuo Wang, Xin School of Computer Science and Technology Dalian University of Technology Dalian116023 China School of Mechanical Engineering Dalian University of Technology Dalian116023 China Chengdu Research Institute Dalian University of Technology Chengdu611900 China

ISBN: (纸本)9781510683303

The aim of the fruit and vegetable object detection task is to enable the picking robots to identify objects efficiently and accurately. Although there are extensive studies in improving the accuracy and speed of fruit and vegetable object detection task, the methods still lack of responsiveness to different requirements of picking robots and algorithms for picking different objects. In this paper, based on YOLOv8, Lightweighting Sequential Polarized Self-Attention YOLOv8s (LSPA_YOLOv8s) was proposed to improve the accuracy and speed in grape object detection task. The new approach has three critical designs: (1) Incorporating the ResNet and multiple attention mechanisms to improve the PSA, the DSPSA module is proposed to improve feature extraction;(2) Replace the convolution block in C2f with PConv to get the optimized C2f_PConv module, take advantage of the nature of partial convolution to cut the number of model parameters and memory accesses;(3) Replacement of some Conv blocks in YOLOv8s with Involution to further increase network speed. Experiments show that the method performs well on the grape object detection task. © 2024 SPIE.

关键词： Fruits

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Edge Computing for Distributed and Cooperative Media Algorithms in Connected Networks 15

Edge Computing for Distributed and Cooperative Media Algorit...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Grace, V. Lilly Gupta, Anchal Giri, Rakesh Kumar Dhope, Tanuja Satish Vittapu, Mani Sarma Dhabliya, Dharmesh G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Kurnool India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura India Sunrise University Computer Science & Engineering Alwar India College of Engineering Electronics and Communication Maharashtra Pune India Mallareddy Engineering College and Management Sciences Kistapur Village Computer Science and Engineering Medchal Telangana Hyderabad India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India

ISBN: (纸本)9798350370249

Side Computing is a rising technique used for connected networks' allotted and cooperative media algorithms. Part Computing refers to processing operations being carried out near the source of the facts, generally close to the edge of the network, in place in a centralized cloud. It allows information to be processed quickly, securely, and reliably in remote or low-connectivity locations, allowing dispensed computing operations inside connected networks. This distributed machine enables latency when working with significant facts sets and enhances security and reliability. Cooperating media algorithms, including laptop vision, audio processing, or natural language processing, are frequently optimized using aspect Computing in related networks. The advantages of facet Computing allow for progressed scalability, green helpful resource utilization, and better latency performance, mainly in areas that suffer from restrained or negative networking situations and bandwidth. Moreover, aspect computing enables enhanced privacy, protection, and personal revel when handling sensitive information and data stored within the cloud. © 2024 IEEE.

关键词： Computing Distributed Handling Networking Performance Scalability

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An Intelligent Reinforcement Learning–Based Method for Threat Detection in Mobile Edge Networks

An Intelligent Reinforcement Learning–Based Method for Thre...

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作者： Saeed, Muhammad Yousaf He, Jingsha Zhu, Nafei Farhan, Muhammad Dev, Soumyabrata Gadekallu, Thippa Reddy Almadhor, Ahmad College of Computer Science Beijing University of Technology Beijing China Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal Pakistan School of Computer Science University College Dublin Dublin Ireland Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India The College of Mathematics and Computer Science Zhejiang A&F University Hangzhou China Division of Research and Development Lovely Professional University Phagwara India Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia

Traditional techniques for detecting threats in mobile edge networks are limited in their ability to adapt to evolving threats. We propose an intelligent reinforcement learning (RL)–based method for real-time threat detection in mobile edge networks. Our approach enables an agent to continuously learn and adapt its threat detection capabilities based on feedback from the environment. Through experiments, we demonstrate that our technique outperforms traditional methods in detecting threats in dynamic edge network environments. The intelligent and adaptive nature of our RL-based approach makes it well suited for securing mission-critical edge applications with stringent latency and reliability requirements. We provide an analysis of threat models in multiaccess edge computing and highlight the role of on-device learning in enabling distributed threat intelligence across heterogeneous edge nodes. Our technique has the potential, significantly enhancing threat visibility and resiliency in next-generation mobile edge networks. Future work includes optimizing sample efficiency of our approach and integrating explainable threat detection models for trustworthy human–AI collaboration. © 2024 John Wiley & Sons Ltd.

关键词： Reinforcement learning

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Enhancing TVM VTA Simulator Performance Through SIMD Vectorization 10

Enhancing TVM VTA Simulator Performance Through SIMD Vectori...

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10th International Conference on Applied System Innovation, ICASI 2024

作者： Huang, Kuo-Ting Lin, Tzer-Ying Cheng, Po-Wen Chen, Peng-Sheng Advanced Institute of Manufacturing for High-tech Innovations National Chung Cheng University Department of Computer Science and Information Engineering Chia-Yi Taiwan Industrial Technology Research Institute Hsinchu Taiwan

ISBN: (纸本)9798350394924

In this paper, we enhance TVM VTA (Versatile Tensor Accelerator) simulator performance via SIMD vectorization. The VTA simulator is analyzed and identified the performance bottleneck of the whole execution. Subsequently, the hotspot of the VTA simulator was optimized using two SIMD ISAs: ARM Neon and ARM SVE. The approaches of using SIMD intrinsic functions and inline assembly were adopted in the modifications. We evaluate the execution performance of the proposed modifications running on a general-purpose CPU with SIMD instructions. For the tested benchmark programs, the experiment shows that the SIMD technique can significantly improve the execution performance of the VTA simulator, with a maximum speedup of 2.27 times. © 2024 IEEE.

关键词： Benchmarking

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