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A review of intelligent optimization for group scheduling problems in cellular manufacturing

Frontiers of engineering Management 2023年第3期10卷 406-426页

作者： Yuting WANG Yuyan HAN Dunwei GONG Huan LI School of Computer Science Liaocheng UniversityLiaocheng 252059China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China

Given that group technology can reduce the changeover time of equipment,broaden the productivity,and enhance the flexibility of manufacturing,especially cellular manufacturing,group scheduling problems(GSPs)have elicited considerable attention in the academic and industry practical *** are two issues to be solved in GSPs:One is how to allocate groups into the production cells in view of major setup times between groups and the other is how to schedule jobs in each *** a number of studies on GSPs have been published,few integrated reviews have been conducted so far on considered problems with different constraints and their optimization *** this end,this study hopes to shorten the gap by reviewing the development of research and analyzing these *** literature is classified according to the number of objective functions,number of machines,and optimization *** classical mathematical models of single-machine,permutation,and distributed flowshop GSPs based on adjacent and position-based modeling methods,respectively,are also *** but not least,outlooks are given for outspread problems and problem algorithms for future research in the fields of group scheduling.

关键词： cellular manufacturing group scheduling flowshop literature review

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Adaptive dynamic surface control for quadrotor-slung load transportation system with uncertainties

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Science China(Technological Sciences) 2023年第10期66卷 2917-2929页

作者： DING Feng HUANG Jian SUN Chong AI Yong XU WenXia YANG ChenGuang School of Computer Science South-Central Minzu UniversityWuhan 430074China Key Laboratory of Ministryof EducationforImage Processing and Intelligent ControlHubei Key Laboratoryof Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Engineering Wuhan Institute of TechnologyWuhan 430205China Key Laboratory of Ministry of Education for Autonomous Systems and Networked Control Guangdong Engineering Technology Research Center of Control of Intelligent SystemsCollege of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Quadrotors play a significant role in our lives and are transforming our *** cable-suspended loads is an unavoidable quadrotor application trend and a hot research topic in the control ***,the load swing and unpredictability pose significant challenges to the quadrotor's *** this paper,an anti-swing controller with an inner-outer control strategy for the quadrotor-slung load transportation system is *** facilitate the controller design,the outer position dynamics are restructured in the form of ***,a virtual controller is created to force the underactuated states to the dynamic surface to ensure the position subsystem's *** improve robustness,an adaptive law is used to eliminate the effects of uncertain cable ***,a dynamic surface controller for the inner attitude subsystem is presented to drive the actual force to the virtual *** is demonstrated that the control strategy can stabilize the quadrotor despite mass and cable length *** results are provided to demonstrate the efficacy and durability of the proposed method.

关键词： quadrotor-slung load transportation system dynamic surface control adaptive law cascade system model uncertainties

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Unsupervised pedestrian intention estimation through deep neural embeddings and spatio-temporal graph convolutional networks

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Pattern Analysis and Applications 2025年第2期28卷 1-14页

作者： Scaccia, Simone Pro, Francesco Amerini, Irene Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

A deep understanding of pedestrian intention and crossing behaviors is crucial in applications like pedestrian attribute recognition and autonomous driving. While vehicles need to predict the movements of pedestrians accurately for safety, the recognition and re-identification systems rely on behavioral cues that help them enhance identity tracking and attribute analysis. Traditional trajectory-based methods for pedestrian intention estimation evaluate the future positions of pedestrians based on their past movements but may fail to capture their true intentions. A more effective approach will anticipate actions by analyzing underlying intent, improving the precision of pedestrian recognition and the motion prediction. Current research on estimating pedestrian intentions primarily depends on supervised learning methods. In contrast, this work introduces an unsupervised learning approach to learn intention representations. This method is based on the idea that similar intentions lead to comparable behaviors among pedestrians, and, therefore, they can be clustered. To achieve this, this paper introduces UnPIE, an unsupervised method for predicting pedestrian intentions. It utilizes Spatio-Temporal Graph Convolutional Networks to encode intentions from videos and map them into a D-dimensional latent space. The training phase incorporates Instance Recognition to increase separation between embeddings from different classes and Local Aggregation to form soft clusters of related embeddings. A supervised non-parametric classifier is used to evaluate the performance of the method. The results demonstrate that UnPIE has comparable performance with respect to supervised approaches and even surpasses them, achieving a higher Precision by about 7% on the Pedestrian Intention Estimation dataset.

关键词： Pedestrian intention estimation Unsupervised learning Graph convolutional networks Video classification

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EverydayFantasy - A Mobile Computing Application in Mental Healthcare 18

EverydayFantasy - A Mobile Computing Application in Mental H...

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18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Triohin, Victor Leba, Monica Ionica, Andreea Cristina University of Petrosani Department of System Control and Computer Engineering Petrosani Romania University of Petrosani Department of Management and Industrial Engineering Petrosani Romania

ISBN: (纸本)9789893347928

The paper proposes a game, EverydayFantasy, based on the principles of serious games, game mechanics and gamification and intended for young people with cognitive behavior issues. The game is a mobile computing application in mental healthcare, in the architecture of which is integrated the connection to a database through a server and the use of dedicated APIs such as the one for locating on the map. The game creates a fantasy environment superimposed on the real environment in which the player is motivated to move, socialize and solve different challenges, all to improve his estimated state. © 2023 ITMA.

关键词： Serious games

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A Recommendation Model with Attention-Aware Graph Matching 5

A Recommendation Model with Attention-Aware Graph Matching

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5th IEEE International Conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Ma, Xinxin Cui, Zhendong Yantai University School of Computer and Control Engineering Yantai China

ISBN: (纸本)9781665499132

Under the background of information overload, recommendation system can provide decision-making tools for information producers to promote information and information consumers to obtain personalized preference information. Use user-item attribute information for interaction can effectively improve the performance of the recommendation model. Existing work models and aggregates the attribute interaction of users and items in the same way, without effectively distinguishing them. However, this can't express the feature interaction between attributes well. In this work, we propose Attention-aware Graph Matching Recommendation model (AGMRec), which convert the recommendation process into a graph matching problem. In particular, we design internal-node message passing and external-node matching to model different orders feature interactions between attributes. Then the proposed model uses an attention approach to fuse different aspects of the nodes. Experimental results on two real-world datasets show its superiority over the state-of-the-arts. © 2022 IEEE.

关键词： Recommender systems

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Memristive effects on an improved discrete Rulkov neuron model

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Science China(Technological Sciences) 2023年第11期66卷 3153-3163页

作者： BAO Han LI KeXin MA Jun HUA ZhongYun XU Quan BAO BoCheng School of Microelectronics and Control Engineering Changzhou UniversityChangzhou 213159China Department of Physics Lanzhou University of TechnologyLanzhou 730050China School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhen 518055China

A change in neuronal-action potential can generate a magnetically induced current during the release and propagation of intracellular *** better characterize the electromagnetic-induction effect,this paper presents an improved discrete Rulkov(ID-Rulkov)neuron model by coupling a discrete model of a memristor with sine memductance into a discrete Rulkov neuron *** ID-Rulkov neuron model possesses infinite invariant points,and its memristor-induced stability effect is evaluated by detecting the routes of period-doubling and Neimark-Sacker *** investigated the memristor-induced dynamic effects on the neuron model using bifurcation plots and firing ***,we theoretically expounded the memristor initial-boosting mechanism of infinite coexisting *** results show that the ID-Rulkov neuron model can realize diverse neuron firing patterns and produce hyperchaotic attractors that are nondestructively boosted by the initial value of the memristor,indicating that the introduced memristor greatly benefits the original neuron *** hyperchaotic attractors initially boosted by the memristor were verified by hardware experiments based on a hardware *** addition,pseudorandom number generators are designed using the ID-Rulkov neuron model,and their high randomness is demonstrated based onstrict test results.

关键词： neuron model invariant point memristive effect complex dynamics pseudorandom number generators

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An Evolutionary Model for Earthquake Prediction Considering Time-Series Evolution and Feature Extraction and Its Application

An Evolutionary Model for Earthquake Prediction Considering ...

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2022 International Conference on computer Modelling and Big Data Analytics, CMBDA 2022

作者： Du, Depeng Cui, Zhendong School of Computer and Control Engineering Yantai University Yantai China

Seismic activity presents characteristics such as relatively concentrated distribution, high destructiveness, huge secondary hazards, complex change patterns, and unpredictable future activity conditions. It is of great theoretical significance and application and promotion value to research magnitude prediction for earthquake-prone areas. This study attempts to develop an earthquake prediction model based on seismic data from 1970 to 2021 and to predict earthquakes of magnitude (4.5-6). Firstly, the data are statistically analyzed using data statistics to analyze trends, identify patterns and characteristics of change, and make decisions conducive to development. Secondly, feature extraction and selection are carried out by convolutional neural networks to select the optimal features. The CNN_BiLSTM model was constructed and compared with other prediction models (long and short-term memory neural network, back propagation neural network, and support vector regression) to obtain prediction results for sensitivity, specificity, true prediction values, false prediction values, and accuracy values were compared and analyzed. The earthquake magnitude predictions for Japan and surrounding areas using the above techniques show essential and encouraging results, thus taking an essential step towards an eventual robust prediction mechanism. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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Improving Date Fruit Classification Using Cycle GAN-Generated Dataset

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computer Modeling in engineering & Sciences 2022年第4期131卷 331-348页

作者： Dina M.Ibrahim Nada M.Elshennawy Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computers and Control Engineering Department Faculty of EngineeringTanta UniversityTanta37133Egypt

Dates are an important part of human *** are high in essential nutrients and provide a number of health *** fruits are also known to protect against a number of diseases,including cancer and heart *** fruits have several sizes,colors,tastes,and *** are a lot of challenges facing the date *** of the most significant challenges is the classification and sorting of *** there is no public dataset for date fruits,which is a major limitation in order to improve the performance of convolutional neural networks(CNN)models and avoid the overfitting *** this paper,an augmented date fruits dataset was developed using Deep Convolutional Generative Adversarial Networks(DCGAN)and CycleGAN approach to augment our collected date fruit *** augmentation is required to address the issue of a restricted number of images in our datasets,as well as to establish a balanced *** are three types of dates in our proposed dataset:Sukkari,Ajwa,and *** dataset augmentation,we train our created dataset using ResNet152V2 and CNN models to assess the classification process for our three categories in the *** train these two models,we start with the original ***,the models were trained using the DCGAN-generated dataset,followed by the CycleGAN-generated *** resulting results demonstrated that when using the ResNet152V2model,the CycleGAN-generated dataset had the highest classification performance with 96.8%accuracy,followed by the CNN model with 94.3%accuracy.

关键词： Dates fruits data augmentation DCGAN CycleGAN,deep learning convolution neural networks

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Ring-Overlap: A Storage Scaling Mechanism For Consortium Blockchain

Ring-Overlap: A Storage Scaling Mechanism For Consortium Blo...

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2022 CCF International Conference on Service Science, CCF ICSS 2022

作者： Liu, Wenxuan Zhang, Donghong Zhao, Jindong Yantai University School of Computer and Control Engineering Yantai China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

With the rapid development of blockchain applications, the data that need to be stored increases dramatically, and the blockchain is about to face the problem of storage limitations. To deal with this problem, this paper proposes a storage scaling mechanism for Hyperledger Fabric, which relieves the storage pressure by dividing the peer nodes into clusters, and each node only stores partial data instead of the whole ledger. First, all accounting nodes are divided into clusters that include several nodes respectively, and part of the whole block data is stored in a single cluster;then, the block data are stored overlappingly on some nodes in the cluster, and each block is guaranteed to have some copies in a cluster. By arranging the copies on selected nodes according to our proposed mechanism, all the blocks are overlapped in a cluster. Theoretical analysis and simulation show that the storage volume occupied by nodes is decreased significantly in blockchain applications with frequent transactions, and in the case that the number of node failures in a single cluster does not exceed the threshold, the mechanism can still guarantee data integrity. Moreover, for applications with frequent transactions, storage space consumption can be significantly reduced without increasing excessive query time overhead. © 2022 IEEE.

关键词： Blockchain

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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation 26

Out-of-distribution Object Detection through Bayesian Uncert...

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26th International Conference on Information Fusion, FUSION 2023

作者： Zhang, Tianhao Wang, Shenglin Bouaynaya, Nidhal Calinescu, Radu Mihaylova, Lyudmila The University of Sheffield Automatic Control and Systems Engineering United Kingdom Rowan University Department of Electrical and Computer Engineering United States University of York Department of Computer Science United Kingdom

ISBN: (纸本)9798890344854

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset. © 2023 International Society of Information Fusion.

关键词： Object detection

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