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Dragonfly Interaction Algorithm for Optimization of Queuing Delay in Industrial Wireless Networks

Journal of Bionic Engineering 2024年第1期21卷 447-485页

作者： Sanjay Bhardwaj Da-Hye Kim Dong-Seong Kim department of IT Convergence Engineering ICT Convergence Research CenterKumoh National Institute of TechnologyGumiSouth Korea

In industrial wireless networks,data transmitted from source to destination are highly *** often leads to the queuing of the data,and poor management of the queued data results in excessive delays,increased energy consumption,and packet ***,a nature-inspired-based Dragonfly Interaction Optimization Algorithm(DMOA)is proposed for optimization of the queue delay in industrial wireless *** term“interaction”herein used is the characterization of the“flying movement”of the dragonfly towards damselflies(female dragonflies)for *** a result,interaction is represented as the flow of transmitted data packets,or traffic,from the source to the base *** includes each and every feature of dragonfly movement as well as awareness of the rival dragonflies,predators,and damselflies for the desired optimization of the queue *** features are juxtaposed as noise and interference,which are further used in the calculation of industrial wireless metrics:latency,error rate(reliability),throughput,energy efficiency,and fairness for the optimization of the queue *** analysis,convergence analysis,the Wilcoxon test,the Friedman test,and the classical as well as the 2014 IEEE Congress of Evolutionary Computation(CEC)on the benchmark functions are also used for the evaluation of DMOA in terms of its robustness and *** results demonstrate the robustness of the proposed algorithm for both classical and benchmarking functions of the IEEE CEC ***,the accuracy and efficacy of DMOA were demonstrated by means of the convergence rate,Wilcoxon testing,and ***,fairness using Jain’s index in queue delay optimization in terms of throughput and latency,along with computational complexity,is also evaluated and compared with other *** results show that DMOA exceeds other bio-inspired optimization algorithms in terms of fairness in queue delay management and average packet *** proposed a

关键词： Dragonfly Damselfly Interaction Queuing delay Optimization Industrial wireless networks

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Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1729-1755页

作者： Awais Khan Chomyong Kim Jung-Yeon Kim Ahsan Aziz Yunyoung Nam department of ict Convergence Soonchunhyang UniversityAsan31538Korea ict Convergence Research Center Soonchunhyang UniversityAsan31538Korea Emotional and Intelligent Child Care Convergence Research Center Soonchunhyang UniversityAsan31538Korea

Sleep posture surveillance is crucial for patient comfort,yet current systems face difficulties in providing compre-hensive studies due to the obstruction caused by *** posture assessment remains challenging because of the complex nature of the human body and variations in sleep ***,this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification,thereby enhancing the analysis of body position and *** method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras,which depict six commonly adopted postures:supine,left log,right log,prone head,prone left,and prone *** study involves 10 participants under two conditions:with and without ***,the database is normalized into a video *** subsequent step entails training a fine-tuned,pretrained Visual Geometry Group(VGG16)and ResNet50 *** the third phase,the extracted features are utilized for *** fourth step of the proposed approach employs a serial fusion technique based on the normal distribution to merge the vectors derived from both the RGB and thermal ***,the fused vectors are passed to machine learning classifiers for final *** dataset,which includes human sleep postures used in this study’s experiments,achieved a 96.7%accuracy rate using the Quadratic Support Vector Machine(QSVM)without the ***,the Linear SVM,when utilized with a blanket,attained an accuracy of 96%.When normal distribution serial fusion was applied to the blanket features,it resulted in a remarkable average accuracy of 99%.

关键词： Human sleep posture VGG16 deep learning ResNet50 fusion machine learning

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Vision based intelligent traffic light management system using Faster R‐CNN

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 932-947页

作者： Syed Konain Abbas Muhammad Usman Ghani Khan Jia Zhu Raheem Sarwar Naif RAljohani Ibrahim AHameed Muhammad Umair Hassan department of Computer Science and Engineering University of Engineering and TechnologyLahorePakistan Zhejiang Key Laboratory of Intelligent education Technology and Application Zhejiang Normal UniversityJinhuaChina OTHEM Manchester Metropolitan UniversityManchesterUK Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia department of ict and Natural Sciences Norwegian University of Science and Technology(NTNU)ÅlesundNorway

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

关键词： access control artificial intelligence computer vision intelligent control

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Stability analysis of a time-delayed Van der Pol–Helmholtz–Duffing oscillator in fractal space with a non-perturbative approach

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Communications in Theoretical Physics 2024年第4期76卷 22-31页

作者： Yusry O El-Dib department of Mathematics Faculty of EducationAin Shams UniversityRoxyCairoEgypt

The time-delayed fractal Van der Pol–Helmholtz–Duffing(VPHD)oscillator is the subject of this paper,which explores its mechanisms and highlights its stability *** time-delayed technologies are currently garnering significant attention,the focus of this research remains crucially relevant.A non-perturbative approach is employed to refine and set the stage for the system under *** innovative methodologies introduced yield an equivalent linear differential equation,mirroring the inherent nonlinearities of the ***,the incorporation of quadratic nonlinearity into the frequency formula represents a cutting-edge *** analytical solution's validity is corroborated using a numerical *** conditions are ascertained through the residual Galerkin ***,it is observed that the delay parameter,in the context of the fractal system,reverses its stabilizing influence,impacting both the amplitude of delayed velocity and the *** analytical solution's precision is underscored by its close alignment with numerical ***,the study reveals that fractal characteristics emulate damping *** its applicability across diverse nonlinear dynamical systems,this non-perturbative approach emerges as a promising avenue for future research.

关键词： Van der Pol-Helmholtz-Duffing oscillator fractal space time-delay non-perturbative approach stability analysis El-Dib's frequency formula

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Noninvasive beam diagnosis based on the TM_(010)mode

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Nuclear Science and Techniques 2024年第7期35卷 115-125页

作者： Chuang-Ye Song Wen-Hui Huang department of Engineering Physics Tsinghua University BeijingBeijing100084China of education Beijing100084China

A resonant cavity based on the TM_(010)mode is an effective tool for noninvasive beam *** technique has the advantages of a high signal-to-noise ratio,compact structure,and is related to multiple parameters compared with other beam *** this study,high-precision measurements of the bunch charge,arrival timne,bunch length,and energy parameters based on the TM_(010)mode are discussed.A cavity beam arrival time monitor(BAM)utilizing a phase cavity has been widely used in many *** bunch-length measurermnents,the influence of the beam energy,beam offset,and longitudinal spectrum on the TM_(010)mode are ca rfully considered to reduce errors,and the theoretical resolution of two cavities with different frequencies is *** to the dependence of the beam velocity of the beam loss factor,this method can also be used for the detection low beam energy using two cavities with the same frequency but different cavity lengths.A set of three cavities with different lengths and frequencies of1902 and 1424 GHz is presented for measuring the four aforementioned parameters.

关键词： TM_(010)mode Noninvasive diagnosis Beam length Low energy

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Identifying Malaria Hotspots Regions in Ghana Using Bayesian Spatial and Spatiotemporal Models

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Infectious Diseases & Immunity 2024年第2期4卷 69-78页

作者： Abdul-Karim Iddrisu Dominic Otoo Gordon Hinneh Yakubu Dekongmene Kanyiri Kanimam Yaaba Samuel Cecilia Kubio Francis Balungnaa Dhari Veriegh department of Mathematics and Statistics University of Energy and Natural ResourcesSunyani 233Ghana department of Statistical Service Ghana Statistical ServiceBono RegionSunyani 233Ghana department of Mathematics and ict Gambaga College of EducationSunyani 233Ghana Human Resources Division University of Energy and Natural ResourcesSunyani 233Ghana department of Biological Science University of Energy and Natural ResourcesSunyani 233Ghana.

Background:Malaria remains a significant public health concern in Ghana,with varying risk levels across different geographical *** affects millions of people each year and imposes a substantial burden on the health care system and *** risk estimation and mapping are crucial for effectively allocating resources and implementing targeted interventions to identify regions with disease *** study aimed to identify regions exhibiting elevated malaria risk so that public health interventions can be implemented,and to identify malaria risk predictors that can be controlled as part of public health interventions for malaria ***:The data on laboratory-confirmed malaria cases from 2015 to 2021 were obtained from the Ghana Health Service and Ghana Statistical *** studied the spatial and spatiotemporal patterns of the relative risk of malaria using Bayesian spatial and spatiotemporal *** malaria risk for each region was mapped to visually identify regions with malaria *** and heterogeneity of disease risks were established using correlated and uncorrelated structures via the conditional autoregressive and Gaussian models,*** estimates from the marginal posterior distribution were estimated within the Integrated Nested Laplace Approximation using the R ***:The spatial model indicated an increased risk of malaria in the North East,Bono East,Ahafo,Central,Upper West,Brong Ahafo,Ashanti,and Eastern *** spatiotemporal model results highlighted an elevated malaria risk in the North East,Upper West,Upper East,Savannah,Bono East,Central,Bono,and Ahafo *** spatial and spatiotemporal models identified the North East,Upper West,Bono East,Central,and Ahafo Regions as hotspots for malaria *** variations in risk were evident across regions(H=104.9,P<0.001).Although climatic and economic factors influenced malaria infection,statistical significance was not

关键词： Malaria Disease hotspot Bayesian modeling Conditional auto-regressive Integrated Nested Laplace Approximation Spatial and spatiotemporal models

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Improved YOLOv5 foreign object detection for transmission lines

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Optoelectronics Letters 2024年第8期20卷 490-496页

作者： ZHOU Liming LI Shixin ZHU Zhiren CHEN Fankai LIU Chen DONG Xiuhuan Electrical Engineering department Tianjin University of Technology and Education

The traditional transmission line detection has the problems of low efficiency. To improve the performance, this paper proposes an improved you only look once version 5(YOLOv5) transmission line foreign object detection algorithm. First, efficient channel attention(ECA) module is introduced in the backbone network for focusing the target features and improving the feature extraction capability of the network. Secondly, bilinear interpolation upsampling is introduced in the neck network to improve the model detection accuracy. Finally, by integrating the efficient intersection over union(EIo U) loss function and Soft non-maximum suppression(Soft NMS) algorithm, the convergence speed of the model is accelerated while the detection effect of the model is enhanced. Relative to the original algorithm, the improved algorithm reduces the number of parameters by 16.4%, increases the mean average precision(m AP)@0.5 by 3.9%, m AP@0.5: 0.95 by 6.3%, and increases the detection speed to 55.3 frames per second(FPS). The improved algorithm is able to improve the performance of the foreign object detection in transmission lines effectively.

关键词： A

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Dynamic Signature Verification Using Pattern Recognition

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Journal of Software Engineering and Applications 2024年第5期17卷 214-227页

作者： Emmanuel Nwabueze Ekwonwune Duroha Austin Ekekwe Chinyere Iheakachi Ubochi Henry Chinedu Oleribe department of Computer Science Imo State University Owerri Nigeria department of Computer Science Gregory University Abia Nigeria ict department Alvan Ikoku University of Education Owerri Nigeria

Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.

关键词： Verification Security Biometrics Signature Authentication Model Pattern Recognition Dynamic

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Optimised hybrid classification approach for rice leaf disease prediction with proposed texture features

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Journal of Control and Decision 2024年第1期11卷 84-97页

作者： Sakhamuri Sridevi K.Kiran Kumar department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramIndia

This paper aims to frame a new rice disease prediction model that included three major ***,median filtering(MF)is deployed during pre-processing and then‘proposed Fuzzy Means Clustering(FCM)based segmentation’is *** that,‘Discrete Wavelet Transform(DWT),Scale-Invariant Feature Transform(SIFT)and low-level features(colour and shape),Proposed local Binary Pattern(LBP)based features’are extracted that are classified via‘MultiLayer Perceptron(MLP)and Long Short Term Memory(LSTM)’and predicted outcomes are *** exact prediction,this work intends to optimise the weights of LSTM using Inertia Weighted Salp Swarm Optimisation(IW-SSO)***,the development of IW-SSO method is established on varied metrics.

关键词： Rice disease improved fuzzy hybrid classifiers optimised LSTM IW-SSO algorithm

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Synthesis of uniform two-dimensional MoS_(2) films via thermal evaporation

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Nano Research 2024年第4期17卷 3217-3223页

作者： Xue-Wei Lu Zhewei Li Chen-Kai Yang Weijia Mou Liying Jiao Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of education Department of ChemistryTsinghua UniversityBeijing 100084China

Two-dimensional(2D)molybdenum disulfide(MoS_(2))holds great potential for various applications such as electronic devices,catalysis,lubrication,anti-corrosion and so *** evaporation is a versatile thin film deposition technique,however,the conventional thermal evaporation techniques face challenges in producing uniform thin films of MoS_(2) due to its high melting temperature of 1375℃.As a result,only thick and rough MoS_(2) films can be obtained using these *** address this issue,we have designed a vacuum thermal evaporation system specifically for large-scale preparation of MoS_(2) thin *** using K2MoS4 as the precursor,we achieved reliable deposition of uniform polycrystalline MoS_(2) thin films with a size of 50 mm×50 mm and controllable thickness ranging from 0.8 to 2.4 *** approach also allows for patterned deposition of MoS_(2) using shadow masks and sequential deposition of MoS_(2) and tungsten disulfide(WS_(2)),similar to conventional thermal evaporation ***,we have demonstrated the potential applications of the obtained MoS_(2) thin films in field effect transistors(FETs),memristors and electrocatalysts for hydrogen evolution reaction(HER).

关键词： thermal evaporation molybdenum disulfide two-dimensional memristors hydrogen evolution reaction

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