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prediction Dynamics in Cotton Aphid Using Unmanned Aerial Vehicle Multispectral Images and Vegetation Indices

IEEE ACCESS 2023年 11卷 5908-5918页

作者： Jiang, Pingan Zhou, Xuelin Liu, Tonglai Guo, Xiaohu Ma, Deying Zhang, Cong Li, Yan Liu, Shuangyin Xinjiang Agr Univ Coll Grassland Sci Urumqi 830052 Peoples R China Zhongkai Univ Agr & Engn Coll Informat Sci & Technol Guangzhou 510225 Peoples R China Xinjiang Agr Univ Coll Agr Urumqi 830052 Peoples R China Xinjiang Agr Univ Coll Resource & Environm Urumqi 830052 Peoples R China Kunming Univ Sci & Technol Fac Land & Resources Engn Kunming 650221 Peoples R China Zhuhai Orbita Aerosp Sci & Technol Co Ltd Zhuhai 519080 Peoples R China

Cotton harvest can be increased by having real-time information on the state of cotton aphid populations. However, traditional cotton aphid monitoring relies on ground sample methods supported by models such as linear regression, resulting in low forecast accuracy. Therefore, this paper purposes to enhance the precision of the remote sensing prediction model by investigating the cotton aphid prediction model construction approach. We explored the effectiveness of the XGBoost algorithm combined with the GWO algorithm and SVR method for cotton aphid prediction relying on vegetation indices derived from UAV multispectral photography. Originally, 12 indices related to cotton aphids were calculated by UAV multispectral reflectance. Additionally, the optimal index combination for pest prediction was determined utilizing analysis of correction and two-way ANOVA, combined with the XGBoost algorithm. Furthermore, a pest prevalence prediction model for cotton aphids was constructed via the SVR methodology associated with the optimal catalog combination, and the model was optimized using the GWO algorithm. Compared with the seven algorithms, experimental results demonstrate that the MSE and MAE of the XGBoostGWO-SVR model are reduced by 90.20% and 70.36% (SVR), 90.14% and 70.26% (XGBoost-SVR), 7.47% and 0.14% (XGBoost-GA-SVR), 5.80% and 0.11% (XGBoost-PSO-SVR), 12.06% and 58.95% (LR), and 84.77% and 89.22% (BPNN), whereas the R-2 is increased by 22.5% (SVR and XGBoost-SVR), 0.3% (LR), and 12.51% (BPNN). The R-2 of the prediction model of XGBoost-SVR combined with GWO, PSO, and GA is not significantly different. Among these models, the XGBoost-GWO-SVR obtained the highest R-2 of 0.980 and the lowest MAE of 2.838.

关键词： Cotton Predictive models prediction algorithms Autonomous aerial vehicles Vegetation mapping Vehicle dynamics Statistics Social factors Remote sensing Reflectivity Crops Agriculture Cotton aphid support vector regression unmanned aerial vehicle XGBoost algorithm integrated pest management grey wolf optimizer

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Reinforcement Learning for Rate-Distortion Optimized Hierarchical prediction Structure

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IEEE ACCESS 2023年 11卷 20240-20253页

作者： Lee, Jung-Kyung Kim, Nayoung Kang, Je-Won Ewha Womans Univ Dept Elect & Elect Engn Seoul 03760 South Korea Ewha Womans Univ Grad Program Smart Factory Seoul 03760 South Korea

Video coding standards use a prediction structure to arrange video frames and exploit temporal correlations. In this aspect, it is crucial to resolve complicated temporal dependencies among frames to improve coding efficiency because the coding of a preceding frame affects the rate-distortion (R-D) performance of the subsequent frames. Previous algorithms have attempted to address the problem using handcrafted features or analytical models even though natural videos display various temporal characteristics. In this paper, we propose a reinforcement learning (RL)-based decision algorithm to build the optimal hierarchical prediction structure under a random-access configuration (RA-HPS) in Versatile Video Coding (VVC). Our goal is to maximize coding efficiency by selecting a series of optimal group of pictures (GOP) structures for coding. Accordingly, we formulate an adaptive GOP selection algorithm with a binary tree to represent a policy. We generate an optimal binary tree to minimize the sum of the R-D costs among all plausible binary trees. A new RL policy representation is defined, and the optimal policy is obtained by a sequential update. The tree grows with a hierarchical state-action and a reward sequence in each node. For efficient learning, the proposed technique uses a deep Q-network architecture to capture the temporal correlation between frames, which helps learn the policy of the tree-based RL framework effectively. Experimental results demonstrate that the proposed technique achieves a significant Bjontegaard-Delta (BD)-rate reduction compared with state-of-the-art GOP size-selection algorithms.

关键词： Encoding Streaming media Video sequences Reinforcement learning prediction algorithms Optimization High efficiency video coding Video coding hierarchical B prediction adaptive GOP rate-distortion optimization deep Q-network VVC

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Knowledge-Driven Accurate Opponent Trajectory prediction for Gun-Dominated Autonomous Air Combat

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2023年第5期20卷 251-263页

作者： Dong, Yiqun Ma, Jinyi Wang, Can Ai, Jianliang Fudan Univ Dept Aeronaut & Astronaut Shanghai 200433 Peoples R China

The autonomous air combat (AAC) technique has been a lasting topic for decades. Accurate opponent trajectory prediction provides a fundamental basis for decision making in AAC. In this work, we propose a knowledge-driven scheme for the opponent trajectory prediction problem in one-versus-one gun-dominated within-visual-range (WVR) air combat. A dedicated air combat engagement database is first constructed via skilled human pilots flying WVR air combat. Two baseline algorithms following rule-based and learning-based paradigms are developed and optimized. The knowledge-driven scheme begins with defining handcrafted features extracted from the opponent history movements, and principal components analysis is adopted to compress/refine the features. A generalized regression neural network is then developed to compensate for the residual of the rule-based trajectory prediction method, wherein the refined features are used as inputs, and the residual compensation are used as outputs. Via extensive simulation tests, the proposed scheme shows a more accurate performance as compared with the two other baseline algorithms. To demonstrate the applicability of the proposed scheme, an automatic gun-firing strategy for commencing gun attack in AAC is also illustrated, which justifies the proposed scheme.

关键词： Autonomous Air Combat Convolutional Neural Network prediction algorithms Projectiles Air Traffic Management Gaussian Mixture Models Natural Language Processing Flight Simulation Civil Aviation Aircrew

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A Review of Remaining Useful Life prediction Approaches for Mechanical Equipment

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IEEE SENSORS JOURNAL 2023年第24期23卷 29991-30006页

作者： Zhang, Yangyang Fang, Liqing Qi, Ziyuan Deng, Huiyong Army Engn Univ Shijiazhuang Campus Shijiazhuang 050003 Hebei Peoples R China

The precise maintenance and scientific management of large and complex mechanical equipment are of great significance for ensuring the safe operation of equipment and improving economic efficiency. Prognostics and health management (PHM), as an advanced method of equipment maintenance and management, has received widespread attention. Remaining useful life (RUL) prediction is one of the key technologies of PHM and has become a research hotspot and challenge in the field of equipment PHM. First, PHM and its key technology, i.e., RUL prediction (RULP), are briefly introduced. Its approaches are divided into statistical model-based approaches, artificial intelligence (AI) approaches, physics model-based approaches, and hybrid prognostic approaches. Then, the development branches and the current research status of each type of approach are sorted out, and the corresponding advantages and disadvantages are summarized. Finally, the future technological development direction and challenges faced are analyzed, providing some reference for the development and application of PHM in the future, as well as in-depth research on RULP approaches.

关键词： Prognostics and health management Maintenance engineering Monitoring Artificial intelligence Predictive models prediction algorithms Biological system modeling Artificial intelligence (AI) digital twin hybrid prognostic approaches mechanical equipment physics model prognostics and health management (PHM) remaining useful life (RUL) statistical model

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Spatiotemporal Consistency Learning From Momentum Cues for Human Motion prediction

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第9期33卷 4577-4587页

作者： Chen, Haipeng Hu, Jiahui Zhang, Wenyin Su, Pengxiang Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Linyi Univ Sch Informat Sci & Engn Linyi 276000 Peoples R China Nanchang Univ Sch Software Nanchang 330031 Peoples R China

Extrapolating future human motion based on the historical human pose sequence is the foundation of various intelligent applications. Numerous deep learning-based algorithms have been designed to address this task, achieving state-of-the-art performance on different human motion benchmark datasets. However, most existing methods employ three-dimensional coordinates of joints to demonstrate dynamic motion contexts implicitly. Unfortunately, it remains challenging in capturing motion information from the pose sequence. In this paper, we advocate explicitly describing dynamic contexts via the momentum of human motion mechanic space, as the momentum of a joint is explicit, temporal consistent, and can provide abundant information to the model. In addition, the single-stream methods play a dominant role in the field of human motion prediction. They usually capture motion information via the strategy of continuous or sparse sampling, which might obviate global or detailed local information. Therefore, we present a simple yet effective dual-stream method that can consider both the detailed and global temporal information through a combination of continuous and sparse sampling. The proposed dual-stream paradigm enables the improvement of computational efficiency and the short-term prediction accuracy concurrently. Furthermore, we present a novel temporal attention-based graph convolutional network (TA-GCN) to derive a spatiotemporally consistent motion representation, which can adequately consider the rationality of human body topology. Extensive experiments on two large motion prediction benchmark datasets (i.e., Human 3.6M and CMU Mocap) show that our algorithm achieves state-of-the-art performance both qualitatively and quantitatively.

关键词： Predictive models Spatiotemporal phenomena Dynamics Biological system modeling Computational modeling Hidden Markov models prediction algorithms Human motion prediction momentum cues dual-stream temporal attention-based method graph convolutional network

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Context-Aware Worker Recruitment for Mobile Crowd Sensing Based on Mobility prediction

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IEEE ACCESS 2023年 11卷 92353-92364页

作者： Ngo, Quan T. Yoon, Seokhoon Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 44610 South Korea

Opportunistic worker (OW) selection is a challenging problem in mobile crowd sensing (MCS), where tasks are assigned to individuals to be completed seamlessly during their daily routines without any deviation from their usual routes. In this paper, we propose a novel framework named context-aware worker recruitment based on a mobility prediction model (CAMP) to address the OW selection problem in MCS. Unlike previous approaches that relied on worker mobility prediction models with limited accuracy or utility-based selection methods neglecting task distribution differences across locations, CAMP introduces a two-phase strategy for OW selection. In the first phase, we leverage a recurrent neural network-based prediction model specifically designed to forecast volunteer workers' future locations with higher precision. This enhanced mobility prediction ensures more effective task assignments in the MCS system. In the second phase, CAMP employs a weighted-utility algorithm that takes into account the varying task distribution throughout the day across different locations. The key novelty of the CAMP framework lies in its combination of an accurate multi-output RNN model for predicting worker mobility and a unique weighted-utility worker selection algorithm that considers variations in task distribution across different locations and sensing cycles. To validate the effectiveness of the CAMP framework, we extensively evaluate it using real-world GPS data, specifically the Crawdad Roma/Taxi dataset. The results demonstrate that CAMP outperforms existing approaches, delivering a higher number of completed tasks while adhering to the same budget constraints.

关键词： Task analysis Sensors prediction algorithms Resource management Predictive models Urban areas Schedules Context awareness Human factors Crowdsensing Human mobility prediction opportunistic worker selection mobile crowd sensing task allocation

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A Holistic Approach on Airfare Price prediction Using Machine Learning Techniques

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IEEE ACCESS 2023年 11卷 46627-46643页

作者： Kalampokas, Theofanis Tziridis, Konstantinos Kalampokas, Nikolaos Nikolaou, Alexandros Vrochidou, Eleni Papakostas, George A. Int Hellen Univ Dept Comp Sci MLV Res Grp Kavala 65404 Greece

Globalization of markets involves new strategies and price policies from professionals that contribute to global competitiveness. Airline companies are changing tickets' prices very often considering a variety of factors based on their proprietary rules and algorithms that are searching for the most suitable price policy. Recently, Artificial Intelligence (AI) models are exploited for the latter task, due to their compactness, fast adaptability, and many potentials in data generalization. This paper represents an analysis of airfare price prediction towards finding similarities in the pricing policies of different Airline companies by using AI Techniques. More specifically, a set of effective features is extracted from 136.917 data flights of Aegean, Turkish, Austrian and Lufthansa Airlines for six popular international destinations. The extracted set of features is then used to conduct a holistic analysis from the perspective of the end user who seeks the most affordable ticket cost, considering a destination-based evaluation including all airlines, and an airline-based evaluation including all destinations. For the latter cause, AI models from three different domains and a total of 16 model architectures are considered to resolve the airfare price prediction problem: Machine Learning (ML) with eight state-of-the-art models, Deep Learning (DL) with six CNN models and Quantum Machine Learning (QML) with two models. Experimental results reveal that at least three models from each domain, ML, DL, and QML, are able to achieve accuracies between 89% and 99% in this regression problem, for different international destinations and airline companies.

关键词： Airline industry Atmospheric modeling Predictive models Companies Feature extraction prediction algorithms Pricing Airfare price artificial intelligence deep learning machine learning prediction model pricing models regression quantum machine learning

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GRA-GCN: Dense Granule Protein prediction in Apicomplexa Protozoa Through Graph Convolutional Network

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第3期20卷 1963-1970页

作者： Shi, Haoyuan Feng, Haisong Lu, Zhenxiao Xue, Wei Yang, Congshan Yue, Zhenyu Anhui Agr Univ Sch Informat & Comp Hefei 230036 Anhui Peoples R China Anhui Agr Univ Sch Anim Sci & Technol Hefei 230036 Anhui Peoples R China

Dense granule proteins (GRAs) are secreted by Apicomplexa protozoa, which are closely related to an extensive variety of farm animal diseases. Predicting GRAs is an integral part in prevention and treatment of parasitic diseases. Considering that biological experiment approach is time-consuming and labor-intensive, computational method is a superior choice. Hence, developing an effective computational method for GRAs prediction is of urgency. In this paper, we present a novel computational method named GRA-GCN through graph convolutional network. In terms of the graph theory, the GRAs prediction can be regarded as a node classification task. GRA-GCN leverages k-nearest neighbor algorithm to construct the feature graph for aggregating more informative representation. To our knowledge, this is the first attempt to utilize computational approach for GRAs prediction. Evaluated by 5-fold cross-validations, the GRA-GCN method achieves satisfactory performance, and is superior to four classic machine learning-based methods and three state-of-the-art models. The analysis of the comprehensive experiment results and a case study could offer valuable information for understanding complex mechanisms, and would contribute to accurate prediction of GRAs. Moreover, we also implement a web server at http://***/GRAGCN/index/, for facilitating the process of using our model.

关键词： Proteins Feature extraction Task analysis Convolution Amino acids Protein sequence prediction algorithms Dense granule protein prediction deep learning graph convolution network k-nearest neighbor web server

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Multi-Task Allocation in Mobile Crowd Sensing With Mobility prediction

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第2期22卷 1081-1094页

作者： Zhang, Jinyi Zhang, Xinglin South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Guangdong Peoples R China

Mobile crowd sensing (MCS) is a popular sensing paradigm that leverages the power of massive mobile workers to perform various location-based sensing tasks. To assign workers with suitable tasks, recent research works investigated mobility prediction methods based on probabilistic and statistical models to estimate the worker's moving behavior, based on which the allocation algorithm is designed to match workers with tasks such that workers do not need to deviate from their daily routes and tasks can be completed as many as possible. In this paper, we propose a new multi-task allocation method based on mobility prediction, which differs from the existing works by (1) making use of workers' historical trajectories more comprehensively by using the fuzzy logic system to obtain more accurate mobility prediction and (2) designing a global heuristic searching algorithm to optimize the overall task completion rate based on the mobility prediction result, which jointly considers workers' and tasks' spatiotemporal features. We evaluate the proposed prediction method and task allocation algorithm using two real-world datasets. The experimental results validate the effectiveness of the proposed methods compared against baselines.

关键词： Task analysis Resource management Sensors Trajectory prediction algorithms Predictive models Time factors Mobile crowd sensing multi-task allocation fuzzy control mobility prediction

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A Fusion Deep Learning Model via Sequence-to-Sequence Structure for Multiple-Road-Segment Spot Speed prediction

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IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE 2023年第1期15卷 230-243页

作者： Li, Dongyi Wang, Jianjun Changan Univ Key Lab Transport Ind Management Control & Cycle Sch Transportat Engn Xian 710064 Peoples R China

Forecasting the future average speed fluctuation is conducive to timely perceiving the degree of urban road congestion. Nevertheless, a new challenge has arisen in accurately predicting the spot speed on an urban road network area composed by multiple road segments. To surmount this, an intelligent method for spot speed estimation, namely, the fusion deep learning model with sequence-to-sequence structure (Seq2Seq-FDL), is proposed. It is a sequence-to-sequence learning structure that covers the encoding context vector. The unidirectional convolutional long short-term memory (ConvLSTM) and bidirectional ConvLSTM networks are used to capture traffic short-term and periodic spatiotemporal synchronization characteristics, respectively. Finally, the prediction results are computed by linear feature fusion. Furthermore, the black-box effect of the proposed model is explained by the least absolute shrinkage and selection operator regression model based on Granger causality, and the rationality of the model is verified with an example of spot speed in a road network of Guangzhou. The experimental results were shown to indicate that the traffic spatiotemporal characteristics significantly contributed to the prediction effect of the Seq2Seq-FDL model. The prediction accuracy was superior to a convolutional neural network-connected LSTM network and a ConvLSTM network with or without a bidirectional LSTM; the proposed model presented good applicability in the multiple-time-step prediction of spot speed for multiple road segments.

关键词： Roads Spatiotemporal phenomena Predictive models Data models Biological system modeling Support vector machines prediction algorithms

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