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A Resilient BIRCH-Based Smart Framework for Real-Time IoT Data Clustering 认领 引用
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作者 Prabhat Das Dibya Jyoti Bora +2 位作者 Sajal Saha Cheng-Chi Lee Hirak Mazumdar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期864-898,共35页
Real-time data processing is essential in the evolving landscape of IoT applications,ensuring efficiency,reliability,and adaptability.However,conventional clustering algorithms often face difficulties in managing high... Real-time data processing is essential in the evolving landscape of IoT applications,ensuring efficiency,reliability,and adaptability.However,conventional clustering algorithms often face difficulties in managing highfrequency,continuous IoT data streams due to limited adaptability and high computational overhead.To address these challenges,this study proposes a resilient adaptation of the BIRCH(Balanced Iterative Reducing and Clustering using Hierarchies)algorithm,tailored specifically for streaming IoT data.The enhanced approach dynamically recalculates clusters and determines the optimal number of clusters using the KneeLocator method.Unlike the original batchoriented BIRCH,the modified version processes data incrementally,enabling continuous adaptation to changing data distributions.The proposed method was validated on benchmark IoT datasets and compared against K-Means,DBSCAN,standard BIRCH,and other state-of-the-art streaming-based clustering algorithms.Results consistently show that the modified BIRCH outperforms existing approaches in execution speed,memory efficiency,scalability,and clustering accuracy.In addition,the algorithm has been deployed within a web-based application featuring interactive visualization and anomaly detection,highlighting its practical relevance for smart city and industrial IoT scenarios.To promote reproducibility and future research,the complete framework and source code have been made publicly available. 展开更多
关键词 IoT applications clustering algorithms smart city applications real-time data processing industrial IoT real-time clustering
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Conditional Generative Adversarial Network-Based Travel Route Recommendation 认领 引用
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作者 Sunbin Shin Luong Vuong Nguyen +3 位作者 Grzegorz J.Nalepa Paulo Novais Xuan Hau Pham Jason J.Jung 《Computers, Materials & Continua》 SCIE EI 2026年第1期1178-1217,共40页
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of... Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence. 展开更多
关键词 Travel route recommendation conditional generative adversarial network heterogeneous information network anchor-and-expand algorithm
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Clustering-based recommendation method with enhanced grasshopper optimisation algorithm 认领 引用 被引量：1
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作者 Zihao Zhao Yingchun Xia +7 位作者 Wenjun Xu Hui Yu Shuai Yang Cheng Chen Xiaohui Yuan Xiaobo Zhou Qingyong Wang Lichuan Gu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第2期494-509,共16页
In the era of big data,personalised recommendation systems are essential for enhancing user engagement and driving business growth.However,traditional recommendation algorithms,such as collaborative filtering,face sig... In the era of big data,personalised recommendation systems are essential for enhancing user engagement and driving business growth.However,traditional recommendation algorithms,such as collaborative filtering,face significant challenges due to data sparsity,algorithm scalability,and the difficulty of adapting to dynamic user preferences.These limitations hinder the ability of systems to provide highly accurate and personalised recommendations.To address these challenges,this paper proposes a clustering-based recommendation method that integrates an enhanced Grasshopper Optimisation Algorithm(GOA),termed LCGOA,to improve the accuracy and efficiency of recommendation systems by optimising cluster centroids in a dynamic environment.By combining the K-means algorithm with the enhanced GOA,which incorporates a Lévy flight mechanism and multi-strategy co-evolution,our method overcomes the centroid sensitivity issue,a key limitation in traditional clustering techniques.Experimental results across multiple datasets show that the proposed LCGOA-based method significantly outperforms conventional recommendation algorithms in terms of recommendation accuracy,offering more relevant content to users and driving greater customer satisfaction and business growth. 展开更多
关键词 collaborative recommendation Grasshopper Optimization Algorithm(GOA) K‐means clustering Lévy flight
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A Wearable Stethoscope for Accurate Real-Time Lung Sound Monitoring and Automatic Wheezing Detection Based on an AI Algorithm 认领 引用
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作者 Kyoung-Ryul Lee Taewi Kim +12 位作者 Sunghoon Im Yi Jae Lee Seongeun Jeong Hanho Shin Hana Cho Sang-Heon Park Minho Kim Jin Goo Lee Dohyeong Kim Gil-Soon Choi Daeshik Kang SungChul Seo Soo Hyun Lee 《Engineering》 SCIE EI CSCD 2025年第10期116-129,共14页
The various bioacoustics signals obtained with auscultation contain complex clinical information that has been traditionally used as biomarkers,however,they are not extensively used in clinical studies owing to their ... The various bioacoustics signals obtained with auscultation contain complex clinical information that has been traditionally used as biomarkers,however,they are not extensively used in clinical studies owing to their spatiotemporal limitations.In this study,we developed a wearable stethoscope for wireless,skinattachable,low-power,continuous,real-time auscultation using a lung-sound-monitoring-patch(LSMP).LSMP can monitor respiratory function through a mobile app and classify normal and adventitious breathing by comparing their unique acoustic characteristics.The human heart and breathing sounds from humans can be distinguished from complex sound signals consisting of a mixture of bioacoustic signals and external noise.The performance of the LSMP sensor was further demonstrated in pediatric patients with asthma and elderly chronic obstructive pulmonary disease(COPD)patients where wheezing sounds were classified at specific frequencies.In addition,we developed a novel method for counting wheezing events based on a two-dimensional convolutional neural network deep-learning model constructed de novo and trained with our augmented fundamental lung-sound data set.We implemented a counting algorithm to identify wheezing events in real-time regardless of the respiratory cycle.The artificial intelligence-based adventitious breathing event counter distinguished>80%of the events(especially wheezing)in long-term clinical applications in patients with COPD. 展开更多
关键词 Wearable stethoscope Lung sound Real-time monitoring Automatic wheeze detection AI algorithm
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Data Empowerment in Precision Marketing: Algorithm Recommendations and Their Associated Risks 认领 引用
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作者 Di Zhou 《Proceedings of Business and Economic Studies》 2025年第1期111-118,共8页
This paper examines the impact of algorithmic recommendations and data-driven marketing on consumer engagement and business performance.By leveraging large volumes of user data,businesses can deliver personalized cont... This paper examines the impact of algorithmic recommendations and data-driven marketing on consumer engagement and business performance.By leveraging large volumes of user data,businesses can deliver personalized content that enhances user experiences and increases conversion rates.However,the growing reliance on these technologies introduces significant risks,including privacy violations,algorithmic bias,and ethical concerns.This paper explores these challenges and provides recommendations for businesses to mitigate associated risks while optimizing marketing strategies.It highlights the importance of transparency,fairness,and user control in ensuring responsible and effective data-driven marketing. 展开更多
关键词 Data-driven marketing Algorithmic recommendations Privacy and ethics
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GBiDC-PEST:A novel lightweight model for real-time multiclass tiny pest detection and mobile platform deployment 认领 引用 被引量：1
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作者 Weiyue Xu Ruxue Yang +2 位作者 Raghupathy Karthikeyan Yinhao Shi Qiong Su 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2025年第7期2749-2769,共21页
Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has b... Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings. 展开更多
关键词 mobile counting real-time processing pest detection tiny object identification algorithm deployment
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Predicting the CME arrival time based on the recommendation algorithm 认领 引用 被引量：4
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作者 Yu-Rong Shi Yan-Hong Chen +9 位作者 Si-Qing Liu Zhu Liu Jing-Jing Wang Yan-Mei Cui Bingxian Luo Tian-Jiao Yuan Feng Zheng Zisiyu Wang Xin-Ran He Ming Li 《Research in Astronomy and Astrophysics》 SCIE CAS CSCD 2021年第8期59-74,共16页
CME is one of the important events in the sun-earth system as it can induce geomagnetic disturbance and an associated space environment effect.It is of special significance to predict whether CME will reach the Earth ... CME is one of the important events in the sun-earth system as it can induce geomagnetic disturbance and an associated space environment effect.It is of special significance to predict whether CME will reach the Earth and when it will arrive.In this paper,we firstly built a new multiple association list for 215 different events with 18 characteristics including CME features,eruption region coordinates and solar wind parameters.Based on the CME list,we designed a novel model based on the principle of the recommendation algorithm to predict the arrival time of CMEs.According to the two commonly used calculation methods in the recommendation system,cosine distance and Euclidean distance,a controlled trial was carried out respectively.Every feature has been found to have its own appropriate weight.The error analysis indicates the result using the Euclidean distance similarity is much better than that using cosine distance similarity.The mean absolute error and root mean square error of test data in the Euclidean distance are 11.78 and 13.77 h,close to the average level of other CME models issued in the CME scoreboard,which verifies the effectiveness of the recommendation algorithm.This work gives a new endeavor using the recommendation algorithm,and is expected to induce other applications in space weather prediction. 展开更多
关键词 Sun:coronal mass ejections(CMEs) method:recommendation algorithm
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Real-Time Spreading Thickness Monitoring of High-core Rockfill Dam Based on K-nearest Neighbor Algorithm 认领 引用 被引量：4
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作者 Denghua Zhong Rongxiang Du +2 位作者 Bo Cui Binping Wu Tao Guan 《Transactions of Tianjin University》 EI CAS 2018年第3期282-289,共8页
During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and... During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and the overallquality of the entire dam. Currently, the method used to monitor and controlspreading thickness during the dam construction process is artificialsampling check after spreading, which makes it difficult to monitor the entire dam storehouse surface. In this paper, we present an in-depth study based on real-time monitoring and controltheory of storehouse surface rolling construction and obtain the rolling compaction thickness by analyzing the construction track of the rolling machine. Comparatively, the traditionalmethod can only analyze the rolling thickness of the dam storehouse surface after it has been compacted and cannot determine the thickness of the dam storehouse surface in realtime. To solve these problems, our system monitors the construction progress of the leveling machine and employs a real-time spreading thickness monitoring modelbased on the K-nearest neighbor algorithm. Taking the LHK core rockfilldam in Southwest China as an example, we performed real-time monitoring for the spreading thickness and conducted real-time interactive queries regarding the spreading thickness. This approach provides a new method for controlling the spreading thickness of the core rockfilldam storehouse surface. 展开更多
关键词 Core rockfill dam Dam storehouse surface construction Spreading thickness K-nearest neighbor algorithm Real-time monitor
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Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering 认领 引用 被引量：2
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作者 Xiaoyao Zheng Baoting Han Zhen Ni 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第2期486-500,共15页
Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions ... Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism routes.Inspired by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on decomposition.The method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage method.The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method.The neighborhood is determined according to the weight of the subproblem for crossover mutation.Finally,Pareto layering is used to improve the updating efficiency and population diversity of the solution.The two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same solutions.Compared with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)metrics.Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better distribution.It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists. 展开更多
关键词 Evolutionary algorithm multi-objective optimization Pareto optimization tourism route recommendation two-stage decomposition
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Recommendation Algorithm Integrating CNN and Attention System in Data Extraction 认领 引用 被引量：1
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作者 Yang Li Fei Yin Xianghui Hui 《Computers, Materials & Continua》 SCIE EI 2023年第5期4047-4063,共17页
With the rapid development of the Internet globally since the 21st century,the amount of data information has increased exponentially.Data helps improve people’s livelihood and working conditions,as well as learning ... With the rapid development of the Internet globally since the 21st century,the amount of data information has increased exponentially.Data helps improve people’s livelihood and working conditions,as well as learning efficiency.Therefore,data extraction,analysis,and processing have become a hot issue for people from all walks of life.Traditional recommendation algorithm still has some problems,such as inaccuracy,less diversity,and low performance.To solve these problems and improve the accuracy and variety of the recommendation algorithms,the research combines the convolutional neural networks(CNN)and the attention model to design a recommendation algorithm based on the neural network framework.Through the text convolutional network,the input layer in CNN has transformed into two channels:static ones and non-static ones.Meanwhile,the self-attention system focuses on the system so that data can be better processed and the accuracy of feature extraction becomes higher.The recommendation algorithm combines CNN and attention system and divides the embedding layer into user information feature embedding and data name feature extraction embedding.It obtains data name features through a convolution kernel.Finally,the top pooling layer obtains the length vector.The attention system layer obtains the characteristics of the data type.Experimental results show that the proposed recommendation algorithm that combines CNN and the attention system can perform better in data extraction than the traditional CNN algorithm and other recommendation algorithms that are popular at the present stage.The proposed algorithm shows excellent accuracy and robustness. 展开更多
关键词 Data extraction recommendation algorithm CNN algorithm attention model
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Application of the asynchronous advantage actor–critic machine learning algorithm to real-time accelerator tuning 认领 引用 被引量：5
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作者 Yun Zou Qing-Zi Xing +4 位作者 Bai-Chuan Wang Shu-Xin Zheng Cheng Cheng Zhong-Ming Wang Xue-Wu Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2019年第10期133-141,共9页
This paper describes a real-time beam tuning method with an improved asynchronous advantage actor–critic(A3C)algorithm for accelerator systems.The operating parameters of devices are usually inconsistent with the pre... This paper describes a real-time beam tuning method with an improved asynchronous advantage actor–critic(A3C)algorithm for accelerator systems.The operating parameters of devices are usually inconsistent with the predictions of physical designs because of errors in mechanical matching and installation.Therefore,parameter optimization methods such as pointwise scanning,evolutionary algorithms(EAs),and robust conjugate direction search are widely used in beam tuning to compensate for this inconsistency.However,it is difficult for them to deal with a large number of discrete local optima.The A3C algorithm,which has been applied in the automated control field,provides an approach for improving multi-dimensional optimization.The A3C algorithm is introduced and improved for the real-time beam tuning code for accelerators.Experiments in which optimization is achieved by using pointwise scanning,the genetic algorithm(one kind of EAs),and the A3C-algorithm are conducted and compared to optimize the currents of four steering magnets and two solenoids in the low-energy beam transport section(LEBT)of the Xi’an Proton Application Facility.Optimal currents are determined when the highest transmission of a radio frequency quadrupole(RFQ)accelerator downstream of the LEBT is achieved.The optimal work points of the tuned accelerator were obtained with currents of 0 A,0 A,0 A,and 0.1 A,for the four steering magnets,and 107 A and 96 A for the two solenoids.Furthermore,the highest transmission of the RFQ was 91.2%.Meanwhile,the lower time required for the optimization with the A3C algorithm was successfully verified.Optimization with the A3C algorithm consumed 42%and 78%less time than pointwise scanning with random initialization and pre-trained initialization of weights,respectively. 展开更多
关键词 Real-time beam tuning Parameter optimization Asynchronous advantage actor–critic algorithm Low-energy beam transport
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DMGNN:A Dual Multi-Relational GNN Model for Enhanced Recommendation 认领 引用
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作者 Siyue Li Tian Jin +3 位作者 Erfan Wang Ranting Tao Jiaxin Lu Kai Xi 《Computers, Materials & Continua》 SCIE EI 2025年第8期2331-2353,共23页
In the era of exponential growth of digital information,recommender algorithms are vital for helping users navigate vast data to find relevant items.Traditional approaches such as collaborative filtering and contentba... In the era of exponential growth of digital information,recommender algorithms are vital for helping users navigate vast data to find relevant items.Traditional approaches such as collaborative filtering and contentbasedmethods have limitations in capturing complex,multi-faceted relationships in large-scale,sparse datasets.Recent advances in Graph Neural Networks(GNNs)have significantly improved recommendation performance by modeling high-order connection patterns within user-item interaction networks.However,existing GNN-based models like LightGCN and NGCF focus primarily on single-type interactions and often overlook diverse semantic relationships,leading to reduced recommendation diversity and limited generalization.To address these challenges,this paper proposes a dual multi-relational graph neural network recommendation algorithm based on relational interactions.Our approach constructs two complementary graph structures:a User-Item Interaction Graph(UIIG),which explicitly models direct user behaviors such as clicks and purchases,and a Relational Association Graph(RAG),which uncovers latent associations based on user similarities and item attributes.The proposed Dual Multi-relational Graph Neural Network(DMGNN)features two parallel branches that perform multi-layer graph convolutional operations,followed by an adaptive fusion mechanism to effectively integrate information from both graphs.This design enhances the model’s capacity to capture diverse relationship types and complex relational patterns.Extensive experiments conducted on benchmark datasets—including MovieLens-1M,Amazon-Electronics,and Yelp—demonstrate thatDMGNN outperforms state-of-the-art baselines,achieving improvements of up to 12.3%in Precision,9.7%in Recall,and 11.5%in F1 score.Moreover,DMGNN significantly boosts recommendation diversity by 15.2%,balancing accuracy with exploration.These results highlight the effectiveness of leveraging hierarchical multi-relational information,offering a promising solution to the challenges of data sparsity and relation heterogeneity in recommendation systems.Our work advances the theoretical understanding of multi-relational graph modeling and presents practical insights for developing more personalized,diverse,and robust recommender systems. 展开更多
关键词 Recommendation algorithm graph neural network multi-relational graph relational interaction
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Real-time dispatch strategy for microgrid considering source-load uncertainty:a tailored TD3 reinforcement learning approach 认领 引用
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作者 Shenpeng Xiang Mohan Lin +3 位作者 Zhe Chen Pingliang Zeng Xiangjin Wang Diyang Gong 《Global Energy Interconnection》 EI CSCD 2025年第6期905-917,共13页
The integration of large-scale-distributed new energy resources has led to heightened source‒load uncertainty.As energy prosumers,microgrids urgently require enhanced real-time regulation capabilities over controllabl... The integration of large-scale-distributed new energy resources has led to heightened source‒load uncertainty.As energy prosumers,microgrids urgently require enhanced real-time regulation capabilities over controllable resources amid uncertain environments,rendering real-time and rapid decision-making a critical issue.This paper proposes a tailored twin delayed deep deterministic policy gradient(TD3)reinforcement learning algorithm that explicitly accounts for source‒load uncertainty.First,following an expert experience-based methodology,Gaussian process regression was implemented using the radial basis function covariance with historical source and load data.The parameters were adaptively adjusted by maximum likelihood estimation to generate the expected curves of demand and wind‒solar power generation,along with their 95%confidence regions,which were treated as representative uncertainty scenarios.Second,the traditional scheduling model was transformed into a deep reinforcement learning(DRL)environment through a Markov process.To minimize the total operational cost of the microgrid,the tailored TD3 algorithm was applied to formulate rapid intraday scheduling decisions.Finally,simulations were conducted using real historical data from an actual region in Zhejiang province,China,to verify the efficacy of the proposed method.The results demonstrate the potential of the algorithm for achieving economic scheduling for microgrids. 展开更多
关键词 Microgrid Deep reinforcement learning Tailored TD3 algorithm Intraday real-time scheduling Gaussian process regression
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A Mixed Real-time Algorithm for the Forward Kinematics of Stewart Parallel Manipulator 认领 引用
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作者 王孙安 万亚民 《Journal of Electronic Science and Technology of China》 2006年第2期173-180,共8页
Aimed at the real-time forward kinematics solving problem of Stewart parallel manipulator in the control course, a mixed algorithm combining immune evolutionary algorithm and numerical iterative scheme is proposed. Fi... Aimed at the real-time forward kinematics solving problem of Stewart parallel manipulator in the control course, a mixed algorithm combining immune evolutionary algorithm and numerical iterative scheme is proposed. Firstly taking advantage of simpleness of inverse kinematics, the forward kinematics is transformed to an optimal problem. Immune evolutionary algorithm is employed to find approximate solution of this optimal problem in manipulator＇s workspace. Then using above solution as iterative initialization, a speedy numerical iterative scheme is proposed to get more precise solution. In the manipulator running course, the iteration initialization can be selected as the last period position and orientation. Because the initialization is closed to correct solution, solving precision is high and speed is rapid enough to satisfy real-time requirement. This mixed forward kinematics algorithm is applied to real Stewart parallel manipulator in the real-time control course. The examination result shows that the algorithm is very efficient and practical. 展开更多
关键词 stewart parallel manipulator forward kinematics immune evolutionary algorithm numerical iterative scheme real-time control
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Application of A* Algorithm for Real-time Path Re-planning of an Unmanned Surface Vehicle Avoiding Underwater Obstacles 认领 引用 被引量：10
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作者 Thanapong Phanthong Toshihiro Maki +2 位作者 Tamaki Ura Takashi Sakamaki Pattara Aiyarak 《Journal of Marine Science and Application》 2014年第1期105-116,共12页
This paper describes path re-planning techniques and underwater obstacle avoidance for unmanned surface vehicle(USV) based on multi-beam forward looking sonar(FLS). Near-optimal paths in static and dynamic environment... This paper describes path re-planning techniques and underwater obstacle avoidance for unmanned surface vehicle(USV) based on multi-beam forward looking sonar(FLS). Near-optimal paths in static and dynamic environments with underwater obstacles are computed using a numerical solution procedure based on an A* algorithm. The USV is modeled with a circular shape in 2 degrees of freedom(surge and yaw). In this paper, two-dimensional(2-D) underwater obstacle avoidance and the robust real-time path re-planning technique for actual USV using multi-beam FLS are developed. Our real-time path re-planning algorithm has been tested to regenerate the optimal path for several updated frames in the field of view of the sonar with a proper update frequency of the FLS. The performance of the proposed method was verified through simulations, and sea experiments. For simulations, the USV model can avoid both a single stationary obstacle, multiple stationary obstacles and moving obstacles with the near-optimal trajectory that are performed both in the vehicle and the world reference frame. For sea experiments, the proposed method for an underwater obstacle avoidance system is implemented with a USV test platform. The actual USV is automatically controlled and succeeded in its real-time avoidance against the stationary undersea obstacle in the field of view of the FLS together with the Global Positioning System(GPS) of the USV. 展开更多
关键词 underwater obstacle avoidance real-time pathre-planning A＊ algorithm sonar image unmanned surface vehicle
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Personalized fund recommendation with dynamic utility learning 认领 引用
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作者 Jiaxin Wei Jia Liu 《Financial Innovation》 2025年第1期1558-1584,共27页
This study introduces a fund recommendation system based on the ε-greedy algorithm and an incremental learning framework.This model simulates the interaction process when customers browse the web-pages of fund produc... This study introduces a fund recommendation system based on the ε-greedy algorithm and an incremental learning framework.This model simulates the interaction process when customers browse the web-pages of fund products.Customers click on their preferred fund products when visiting a fund recommendation web-page.The system collects customer click sequences to continually estimate and update their utility function.The system generates product lists using the ε-greedy algorithm,where each product on the list has the probability of 1-ε of being selected as an exploitation strategy,and the probability of ε is chosen as the exploration strategy.We perform a series of numerical tests to evaluate the estimation performance with different values of ε. 展开更多
关键词 Personalized fund recommendation ε-greedy algorithm Dynamic utility learning
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Real-Time Dynamic Multiobjective Path Planning:A Case Study 认领 引用
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作者 Hongle Li SeongKi Kim 《Computers, Materials & Continua》 SCIE EI 2025年第12期5571-5594,共24页
Path planning is a fundamental component in robotics and game artificial intelligence that considerably influences the motion efficiency of robots and unmanned aerial vehicles,as well as the realism and immersion of v... Path planning is a fundamental component in robotics and game artificial intelligence that considerably influences the motion efficiency of robots and unmanned aerial vehicles,as well as the realism and immersion of virtual environments.However,traditional algorithms are often limited to single-objective optimization and lack real-time adaptability to dynamic environments.This study addresses these limitations through a proposed realtime dynamic multiobjective(RDMO)path-planning algorithm based on an enhanced A* framework.The proposed algorithm employs a queue-based structure and composite multiheuristic functions to dynamically manage game tasks and compute optimal paths under changing-map-connectivity conditions in real time.Simulation experiments are conducted using real-world road network data and benchmarked against mainstream hybrid approaches based on genetic algorithms(GAs)and simulated annealing(SA).The results show that the computational speed of the RDMO algorithm is 88 and 73 times faster than that of the GA-and SA-based solutions,respectively,while the total planned path length is reduced by 58%and 33%,respectively.In addition,the RDMO algorithm also shows excellent responsiveness to dynamic changes in map connectivity and can achieve real-time replanning with a minimal computational overhead.The research results prove that the RDMO algorithm provides a robust and efficient solution for multiobjective path planning in games and robotics applications and has a great application potential in improving system performance and user experience in related fields in the future. 展开更多
关键词 Multiobjective optimization path planning real-time system dynamic environments A*algorithm game artificial intelligence
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Novel Real-Time Seam Tracking Algorithm Based on Vector Angle and Least Square Method 认领 引用 被引量：1
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作者 Guanhao Liang Qingsheng Luo +1 位作者 Zhuo Ge Xiaoqing Guan 《Journal of Beijing Institute of Technology》 EI CAS 2017年第2期150-157,共8页
Real-time seam tracking can improve welding quality and enhance welding efficiency during the welding process in automobile manufacturing.However,the teaching-playing welding process,an off-line seam tracking method,i... Real-time seam tracking can improve welding quality and enhance welding efficiency during the welding process in automobile manufacturing.However,the teaching-playing welding process,an off-line seam tracking method,is still dominant in automobile industry,which is less flexible when welding objects or situation change.A novel real-time algorithm consisting of seam detection and generation is proposed to track seam.Using captured 3D points,space vectors were created between two adjacent points along each laser line and then a vector angle based algorithm was developed to detect target points on the seam.Least square method was used to fit target points to a welding trajectory for seam tracking.Furthermore,the real-time seam tracking process was simulated in MATLAB/Simulink.The trend of joint angles vs.time was logged and a comparison between the off-line and the proposed seam tracking algorithm was conducted.Results show that the proposed real-time seam tracking algorithm can work in a real-time scenario and have high accuracy in welding point positioning. 展开更多
关键词 real-time seam tracking real-time seam detection laser scanner vector angle leastsquare method algorithm research
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Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context 认领 引用 被引量：1
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作者 Weihua Liu Haoyang Wan Boyuan Yan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第4期239-258,共20页
With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.He... With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.Hence,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand effectively.On the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user interest.In the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of candidates.At last,a diverse approach is used to generate a Top-K recommendation list for users.And through a case study,we illustrate the accuracy and diversity of the proposed method. 展开更多
关键词 Recommendation algorithm user contexts short video temporal contextual information
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Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches——A Systematic Literature Review and Mapping Study 认领 引用 被引量：1
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作者 Francisco JoséGarcía-Penlvo Andrea Vázquez-Ingelmo Alicia García-Holgado 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第8期1023-1051,共29页
The exponential use of artificial intelligence(AI)to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed.While AI is a powerfulmeans to discover interes... The exponential use of artificial intelligence(AI)to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed.While AI is a powerfulmeans to discover interesting patterns and obtain predictive models,the use of these algorithms comes with a great responsibility,as an incomplete or unbalanced set of training data or an unproper interpretation of the models’outcomes could result in misleading conclusions that ultimately could become very dangerous.For these reasons,it is important to rely on expert knowledge when applying these methods.However,not every user can count on this specific expertise;non-AIexpert users could also benefit from applying these powerful algorithms to their domain problems,but they need basic guidelines to obtain themost out of AI models.The goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input features.The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering.As a result,9 papers that tackle AI algorithmrecommendation through tangible and traceable rules and heuristics were collected.The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms. 展开更多
关键词 SLR systematic literature review artificial intelligence machine learning algorithm recommendation heuristics explainability
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