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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Rajendiran, Gowtham Rethnaraj, Jebakumar Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350325799

Aeroponics is a type of soilless agriculture where plants are grown in a misted environment without the use of the soil. The process of yield prediction in aeroponics system for the lettuce crop is one of the key challenges because the crop yield is affected by variety of growth factors such as temperature, humidity, light and nutrient levels. Hence, to predict the lettuce crop yield in aeroponics, the machine learning models were utilized and these models can be trained on the historical data on crop yields and environmental variables. Here, in this work, the Random Forest (RF) regression is chosen as the machine learning model for yield prediction. The data used in this work were collected from the aeroponic lettuce farming tower. The purpose of the research work is to investigate the potential of the random forest model for efficient prediction of the lettuce crop yield in aeroponics system. The RF model can produce accurate prediction results of 92% which is better than the other regression algorithms in terms of forecasting lettuce crop production. Comparing this to the MAE of other regression models, which may be anywhere from 0.098 to 3.76 of the average reported yield, and it is observed that RF has a much more acceptable MAE. Hence, this work provides valuable information for farmers who are interested in utilizing this type of indoor farming to produce the high-yield of lettuce crops. © 2023 IEEE.

关键词： Lettuce

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LTL cross entropy optimisation for quadcopter task orchestration

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Cyber-Physical Systems 2023年第3期9卷 273-312页

作者： Banks, Christopher Coogan, Samuel Egerstedt, Magnus School of Interactive Computing Georgia Institute of Technology AtlantaGA United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Samueli School of Engineering University of California-Irvine IrvineCA United States

This paper presents a task orchestration framework for multi-agent systems utilising linear temporal logic (LTL) and cross entropy optimisation, a stochastic optimisation technique used for rare-event sampling. We define task orchestration as a combination of task decomposition, allocation and planning for a quadcopter or team of quadcopters given a high-level specification. Specifically, we consider tasks that are complex and consist of environment constraints, system constraints, or both, that must be satisfied. We first approach motion planning for the single agent case where transition systems for the environment allow tasks to be developed as linear temporal logic (LTL) specifications. Trajectories are then generated via motion primitives for a single quadcopter and optimised via cross entropy to ensure optimal satisfaction of a cost function. We extend this work to the multi-agent case where a team of homogeneous quadcopters are considered to satisfy an LTL specification. In order to provide faster computations and initial cost-agnostic sampling, we formulate the online version of multi-agent task allocation via cross entropy for tasks specified in LTL specifications. The results of this framework are verified in simulation and experimentally with a team of quadcopters. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Multi agent systems

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Monochromatic Image Dehazing Using Enhanced Feature Extraction Techniques in Deep Learning

Monochromatic Image Dehazing Using Enhanced Feature Extracti...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Doshi, Nisarg Bhavsar, Sagar Rajeswari, D. Srinivasan, R. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603203 India School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur603203 India

ISBN: (纸本)9798350334357

Images photographed in foggy weather usually have poor visibility. To mitigate this problem researchers have come up with various image dehazing techniques. Now, more than ever, high-quality images that can be used to glean maximum information from autonomous systems are in high demand. This research work uses different Deep Learning (DL) architectures to draw out essential details from the picture and localize the information recovered to reduce the haze from the picture. The paper investigates to remove the hazes from the dehazed images using DL techniques. The first task of this proposed work attempts three pre-processing techniques namely, air light estimation, contextual regularization and boundary constraint. The second task of this work is to identify the suitable DL model to extract clear images from dehazed images. Evaluation metrics are PSNR value and SSIM value are used to estimate the values of dehazed images compared with clear images. Experimental results proves that AOD-Net outperforms good result with respect to PSNR value. © 2023 IEEE.

关键词： Image enhancement

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A recurrent neural network architecture for android mobile data analysis for detecting malware infected data

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Soft computing 2024年第21期28卷 12917-12928页

作者： Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Garg, Shruti Department of ECE Saveetha School of Engineering Simats Chennai602105 India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Simats Chennai602105 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Applied Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technological Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India

One of the latest modern communication devices is a mobile device seriously affected by multiple malware. Malware is a virus software installed automatically by hackers on various computing devices. Malware corrupts the system software, kills *.exe files, and tries to access user-sensitive data from the device. Around the world, 80% of people use smartphones, and 70% use Android phones. All Android phones are installed with anti-virus software but not used properly. Thus, it creates a pathway for malware to enter Android mobile devices and deploy vulnerabilities. Several earlier methods have focused on using the malware detection method by analyzing static data, which cannot identify the dynamic behavior of the malware. Thus, this paper implemented one of the deep learning algorithms, RNN, for identifying and detecting malware activities in Android mobile devices statically and dynamically. An IoT network architecture is proposed for experimenting with the RNN algorithms verifying the performance. The results of the RNN method show that it is better than other models in malware detection in mobile devices. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Android malware

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ONNXPruner: ONNX-Based General Model Pruning Adapter

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5806-5817页

作者： Ren, Dongdong Li, Wenbin Ding, Tianyu Wang, Lei Fan, Qi Huo, Jing Pan, Hongbing Gao, Yang Nanjing University School of Electronic Science and Engineering China Nanjing University State Key Laboratory for Novel Software Technology School of Intelligence Science and Technology China Microsoft Corporation Applied Sciences Group RedmondWA98052 United States University of Wollongong School of Computing and Information Technology NSW2522 Australia

Recent advancements in model pruning have focused on developing new algorithms and improving upon benchmarks. However, the practical application of these algorithms across various models and platforms remains a significant challenge. To address this challenge, we propose ONNXPruner, a versatile pruning adapter designed for the ONNX format models. ONNXPruner streamlines the adaptation process across diverse deep learning frameworks and hardware platforms. A novel aspect of ONNXPruner is its use of node association trees, which automatically adapt to various model architectures. These trees clarify the structural relationships between nodes, guiding the pruning process, particularly highlighting the impact on interconnected nodes. Furthermore, we introduce a tree-level evaluation method. By leveraging node association trees, this method allows for a comprehensive analysis beyond traditional single-node evaluations, enhancing pruning performance without the need for extra operations. Experiments across multiple models and datasets confirm ONNXPruner's strong adaptability and increased efficacy. Our work aims to advance the practical application of model pruning. © 1979-2012 IEEE.

关键词： Adaptation Models Training Filters Deep Learning Computational Modeling Software Interoperability Load Modeling Software Algorithms Electronic Mail General Model Pruning ONNX Tree Level Evaluation Deep Neural Network Model Pruning Deep Learning Model Architecture Deep Learning Framework Hardware Platform Superior Performance Deep Neural Network Feature Maps Py Torch Image Net Training Images Sparse Matrix Root Node Type Of Operation Single Node Deep Neural Network Model Validation Images Extra Components Importance Of Filtering Pruning Method Squeeze Net

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A Theoretical Study on the Properties of Submicron b-Si Nanostructures for Improved Absorption in b-Si Thermophotovoltaic Cells

A Theoretical Study on the Properties of Submicron b-Si Nano...

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International Conference on Renewable Energy and E-mobility, ICREEM 2022

作者： Chai, Jasman Y. H. Wong, Basil T. Juodkazis, Saulius Department of Mechanical Engineering School of Engineering Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Center School of Science Swinburne University of Technology Hawthorn Australia

ISBN: (纸本)9789819959457

Black silicon (b-Si) is a nanostructured surface modification with excellent optical properties and is an attractive candidate for optoelectronic applications. In this work, a preliminary simulation study is done to determine the optical properties of b-Si layers on silicon photovoltaic cells for thermophotovoltaic applications. Finite-difference time-domain method was used to simulate the optical responses of b-Si photovoltaic cells. Black silicon nanostructures of about 50 to 650 nm in diameter and 200 to 1800 nm in height were studied. The optical properties improved with decreasing nanostructure diameter and increasing height. The light trapping properties in these nanostructures were discussed and were attributed to the refractive index gradients of the b-Si layer. © Institute of technology PETRONAS Sdn Bhd (Universiti Teknologi PETRONAS) 2024.

关键词： Refractive index

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OH-DRL: An AoI-Guaranteed Energy-Efficient Approach for UAV-Assisted IoT Data Collection

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5009-5022页

作者： Yang, Bowen Yu, Yao Hao, Xin Yeoh, Phee Lep Zhang, Junxiong Guo, Lei Li, Yonghui Northeastern University School of Computer Science and Engineering Ministry of Education Northeastern University and Key Laboratory of Intelligent Computing in Medical Image Shenyang110819 China Deakin University School of Information Technology VIC3216 Australia University of the Sunshine Coast School of Science Technology and Engineering Australia Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing400065 China University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

In this paper, we propose a hierarchical optimization approach that guarantees the maximum age of information (AoI) for unmanned aerial vehicle (UAV) assisted Internet-of-Things (IoT) data collection. Our model is based on an energy-efficient simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) beamforming model. We formulate the optimization to minimize the UAV flight energy consumption subject to a maximum average AoI threshold by optimizing the UAV trajectory, IoT device scheduling, and STAR-RIS beamforming. To solve this, we develop an optimization-based hierarchical deep reinforcement learning (OH-DRL) algorithm that decomposes the formulated problem into an inter-cluster UAV visiting policy and STAR-RIS-based intra-cluster IoT scheduling policy. In OH-DRL, we jointly optimize the two policies in a high-level loop and a low-level loop, respectively. In the high-level loop, we design an AoI-guided DRL algorithm to determine the AoI-guaranteed UAV hovering position with minimal flight distance. In the low-level loop, a semidefinite relaxation (SDR)-based optimization algorithm further reduces the UAV’s flying time by minimizing the average AoI. Simulation results validate that OH-DRL achieves better convergence performance and energy-saving efficiency across different network scales. Compared to the state-of-the-art DRL algorithm, OH-DRL reduces the UAV flight energy consumption by 14.4% and decreases the number of training episodes required for convergence by 66%. © 2025 IEEE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Knowledge-enriched joint-learning model for implicit emotion cause extraction

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CAAI Transactions on Intelligence technology 2023年第1期8卷 118-128页

作者： Chenghao Wu Shumin Shi Jiaxing Hu Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application *** lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local ***,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event *** address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different *** on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is *** authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause *** on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

关键词： emotion cause extraction external knowledge fusion implicit emotion recognition joint learning

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FedRKG: A Privacy-Preserving Federated Recommendation Framework via Knowledge Graph Enhancement 18th

FedRKG: A Privacy-Preserving Federated Recommendation Framew...

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18th International Conference on Green, Pervasive, and Cloud computing, GPC 2023

作者： Yao, Dezhong Liu, Tongtong Cao, Qi Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computing Science University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9789819998951

Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recently, Graph Neural Networks (GNN) have gained popularity in recommendation tasks due to their ability to capture high-order interactions between users and items. However, privacy concerns prevent the global sharing of the entire user-item graph. To address this limitation, some methods create pseudo-interacted items or users in the graph to compensate for missing information for each client. Unfortunately, these methods introduce random noise and raise privacy concerns. In this paper, we propose FedRKG, a novel federated recommendation system, where a global knowledge graph (KG) is constructed and maintained on the server using publicly available item information, enabling higher-order user-item interactions. On the client side, a relation-aware GNN model leverages diverse KG relationships. To protect local interaction items and obscure gradients, we employ pseudo-labeling and Local Differential Privacy (LDP). Extensive experiments conducted on three real-world datasets demonstrate the competitive performance of our approach compared to centralized algorithms while ensuring privacy preservation. Moreover, FedRKG achieves an average accuracy improvement of 4% compared to existing federated learning baselines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Recommender systems

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An indoor delivery robot based on YOLOv8 and ROS 7

An indoor delivery robot based on YOLOv8 and ROS

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2024 7th International Conference on Intelligent Manufacturing and Automation, IMA 2024

作者： Sun, Zhijin Fan, Jianan Lu, Jialin Zhang, Lei Li, Xiaoyang Deng, Baoru School of Electronic Information Engineering Zhuhai College of Science and Technology Zhuhai China School of Mechanical Engineering Zhuhai College of Science and Technology Zhuhai China School of Computing Zhuhai College of Science and Technology Zhuhai China

Aiming at the problems of inflexible movement, poor operation efficiency, low space utilization in the storage area and poor compatibility of placed items, intelligent route planning ability and weak intelligent obstacle avoidance ability in the current indoor delivery robot operation process, this paper takes ROS combined with YOLOv8 as the intelligent driving core, uses the A∗ algorithm to make the robot go to the target position, plan a reasonable route, take STM32 as the intelligent control core, and use the Mecanum wheel as the power take-off wheel. Based on this, an indoor delivery robot based on ROS and the McNum wheel motion scheme is designed. This paper designs the robot as a whole, designs the mechanical structure and functional modules of the robot, and simulates and analyzes the obstacle avoidance scheme and key functions of intelligent driving. Experiments have proved that the design of the delivery robot can not only achieve efficient and accurate distribution, but also reduce energy consumption to a certain extent, and provide new ideas for the intelligent transformation and development of the logistics industry. © Published under licence by IOP Publishing Ltd.

关键词： Energy utilization

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