Electrical energy can be harvested from the rotational kinetic energy of moving bodies,consisting of both mechanical and kinetic energy as a potential power source through electromagnetic induction,similar to wind ene...Electrical energy can be harvested from the rotational kinetic energy of moving bodies,consisting of both mechanical and kinetic energy as a potential power source through electromagnetic induction,similar to wind energy applications.In industries,rotational bodies are commonly present in operations,yet this kinetic energy remains untapped.This research explores the energy generation characteristics of two rotational body types,disk-shaped and cylinder-shaped under specific experimental setups.The hardware setup included a direct current(DC)motor driver,power supply,DC generator,mechanical support,and load resistance,while the software setup involved automation testing tools and data logging.Electromagnetic induction was used to harvest energy,and experiments were conducted at room temperature(25℃)with controlled variables like speed and friction.Results showed the disk-shaped body exhibited higher energy efficiency than the cylinder-shaped body,largely due to lower mechanical losses.The disk required only two bearings,while the cylinder required four,resulting in lower bearing losses for the disk.Additionally,the disk experienced only air friction,whereas the cylinder encountered friction from a soft,uneven rubber material,increasing surface contact losses.Under a 40 W resistive load,the disk demonstrated a 17.1%energy loss due to mechanical friction,achieving up to 15.55 J of recycled energy.Conversely,the cylinder body experienced a 48.05%energy loss,delivering only 51.95%of energy to the load.These insights suggest significant potential for designing efficient energy recycling systems in industrial settings,particularly in manufacturing and processing industries where rotational machinery is prevalent.Despite its lower energy density,this system could be beneficially integrated with energy storage solutions,enhancing sustainability in industrial practices.展开更多
Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves ...Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves resource allocation techniques is host load prediction.This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response times.To solve this issue and accurately predict cloud capacity,cloud data centers use prediction algorithms.This permits dynamic cloud scalability while maintaining superior service quality.For host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this work.First,the suggested hybrid model is input is subjected to the vector auto regression technique.The data in many variables that,prior to analysis,has been filtered to eliminate linear interdependencies.After that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing unit.The next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series components.The key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant unit.Cloud systems require accurate prediction due to the increasing degrees of unpredictability in data centers.Because of this,two actual load traces were used in this study’s assessment of the performance.An example of the load trace is in the typical dispersed system.In comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.展开更多
Industrial processes often involve rotating machinery that generates substantial kinetic energy,much of which remains untapped.Harvesting rotational kinetic energy offers a promising solution to reduce energy waste an...Industrial processes often involve rotating machinery that generates substantial kinetic energy,much of which remains untapped.Harvesting rotational kinetic energy offers a promising solution to reduce energy waste and improve energy efficiency in industrial applications.This research investigates the potential of electromagnetic induction for harvesting rotational kinetic energy from industrial machinery.A comparative study was conducted between disk and cylinder-shaped rotational bodies to evaluate their energy efficiency under various load conditions.Experimental results demonstrated that the disk body exhibited higher energy efficiency,primarily due to lower mechanical losses compared to the cylinder body.A power management circuit was developed to regulate and store the harvested energy,integrating voltage,current,and speed sensors along with a charge controller for battery storage.The experimental setup successfully converted rotational kinetic energy into usable electrical power,with the disk achieving up to 16.33 J of recycled energy,outperforming the cylinder.The disk body demonstrated higher energy recovery efficiency compared to the cylinder,particularly under the 40 W resistive load condition.These findings demonstrate the feasibility of implementing energy recycling systems in industrial settings to enhance sustainability,reduce energy consumption,and minimize waste.Future research should focus on optimizing power management systems and improving energy harvesting efficiency to enable wider adoption of energy recycling technologies in various industrial applications.展开更多
Traditional rule-based IntrusionDetection Systems(IDS)are commonly employed owing to their simple design and ability to detect known threats.Nevertheless,as dynamic network traffic and a new degree of threats exist in...Traditional rule-based IntrusionDetection Systems(IDS)are commonly employed owing to their simple design and ability to detect known threats.Nevertheless,as dynamic network traffic and a new degree of threats exist in IoT environments,these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy.In this study,we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model(ERBM)to classify the packets better and identify intrusions.The ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic,where three membership functions are created to classify all the network traffic features as low,medium,or high to remain situationally aware of the environment.Such fuzzy logic sets produce adaptive detection rules by reducing data uncertainty.Also,for further classification,machine learning classifiers such as Decision Tree(DT),Random Forest(RF),and Neural Networks(NN)learn complex ways of attacks and make the detection process more precise.A thorough performance evaluation using different metrics,including accuracy,precision,recall,F1 Score,detection rate,and false-positive rate,verifies the supremacy of ERBM over classical IDS.Under extensive experiments,the ERBM enables a remarkable detection rate of 99%with considerably fewer false positives than the conventional models.Integrating the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions,the ERBM systemprovides a unique,scalable,data-driven approach to IoT intrusion detection.This research presents a major enhancement initiative in the context of rule-based IDS,introducing improvements in accuracy to evolving IoT threats.展开更多
Vehicle recognition plays a vital role in intelligent transportation systems,law enforcement,access control,and security operations—domains that are becoming increasingly dynamic and complex.Despite advancements,most...Vehicle recognition plays a vital role in intelligent transportation systems,law enforcement,access control,and security operations—domains that are becoming increasingly dynamic and complex.Despite advancements,most existing solutions remain siloed,addressing individual tasks such as vehicle make and model recognition(VMMR),automatic number plate recognition(ANPR),and color classification separately.This fragmented approach limits real-world efficiency,leading to slower processing,reduced accuracy,and increased operational costs,particularly in traffic monitoring and surveillance scenarios.To address these limitations,we present a unified framework that consolidates all three recognition tasks into a single,lightweight system.The framework utilizes MobileNetV2 for efficient VMMR,YOLO(You Only Look Once)for accurate license plate detection,and histogram-based clustering in the HSV color space for precise color identification.Rather than optimizing each module in isolation,our approach emphasizes tight integration,enabling improved performance and reliability.The system also features adaptive image calibration and robust algorithmic enhancements to ensure consistent results under varying environmental conditions.Experimental evaluations demonstrate that the proposedmodel achieves a combined accuracy of 93.3%,outperforming traditional methods and offering practical scalability for deployment in real-world transportation infrastructures.展开更多
Artificial intelligence(AI),particularly deep learning algorithms utilizing convolutional neural networks,plays an increasingly pivotal role in enhancing medical image examination.It demonstrates the potential for imp...Artificial intelligence(AI),particularly deep learning algorithms utilizing convolutional neural networks,plays an increasingly pivotal role in enhancing medical image examination.It demonstrates the potential for improving diagnostic accuracy within dental care.Orthopantomograms(OPGs)are essential in dentistry;however,their manual interpretation is often inconsistent and tedious.To the best of our knowledge,this is the first comprehensive application of YOLOv5m for the simultaneous detection and classification of six distinct dental pathologies using panoramic OPG images.The model was trained and refined on a custom dataset that began with 232 panoramic radiographs and was later expanded to 604 samples.These included annotated subclasses representing Caries,Infection,Impacted Teeth,Fractured Teeth,Broken Crowns,and Healthy conditions.The training was performed using GPU resources alongside tuned hyperparameters of batch size,learning rate schedule,and early stopping tailored for generalization to prevent overfitting.Evaluation on a held-out test set showed strong performance in the detection and localization of various dental pathologies and robust overall accuracy.At an IoU of 0.5,the system obtained a mean precision of 94.22%and recall of 90.42%,with mAP being 93.71%.This research confirms the use of YOLOv5m as a robust,highly efficient AI technology for the analysis of dental pathologies using OPGs,providing a clinically useful solution to enhance workflow efficiency and aid in sustaining consistency in complex multi-dimensional case evaluations.展开更多
The Internet of Things(IoT)ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027,operating in distributed networks with resour...The Internet of Things(IoT)ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027,operating in distributed networks with resource limitations and diverse system architectures.The current conventional intrusion detection systems(IDS)face scalability problems and trust-related issues,but blockchain-based solutions face limitations because of their low transaction throughput(Bitcoin:7 TPS(Transactions Per Second),Ethereum:15-30 TPS)and high latency.The research introduces MBID(Multi-Tier Blockchain Intrusion Detection)as a groundbreaking Multi-Tier Blockchain Intrusion Detection System with AI-Enhanced Detection,which solves the problems in huge IoT networks.The MBID system uses a four-tier architecture that includes device,edge,fog,and cloud layers with blockchain implementations and Physics-Informed Neural Networks(PINNs)for edge-based anomaly detection and a dual consensus mechanism that uses Honesty-based Distributed Proof-of-Authority(HDPoA)and Delegated Proof of Stake(DPoS).The system achieves scalability and efficiency through the combination of dynamic sharding and Interplanetary File System(IPFS)integration.Experimental evaluations demonstrate exceptional performance,achieving a detection accuracy of 99.84%,an ultra-low false positive rate of 0.01% with a False Negative Rate of 0.15%,and a near-instantaneous edge detection latency of 0.40 ms.The system demonstrated an aggregate throughput of 214.57 TPS in a 3-shard configuration,providing a clear,evidence-based path for horizontally scaling to support overmillions of devices with exceeding throughput.The proposed architecture represents a significant advancement in blockchain-based security for IoT networks,effectively balancing the trade-offs between scalability,security,and decentralization.展开更多
Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We p...Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We propose HAMOT,a hierarchical adaptive multi-object tracker that solves these challenges with a novel,unified framework.Unlike previous methods that rely on isolated components,HAMOT incorporates a Swin Transformer-based Adaptive Enhancement(STAE)module—comprising Scene-Adaptive Transformer Enhancement and Confidence-Adaptive Feature Refinement—to improve detection under low-visibility conditions.The hierarchical DynamicGraphNeuralNetworkwith TemporalAttention(DGNN-TA)models both short-and long-termassociations,and the Adaptive Unscented Kalman Filter with Gated Recurrent Unit(AUKF-GRU)ensures accurate motion prediction.The novel Graph-Based Density-Aware Clustering(GDAC)improves occlusion recovery by adapting to scene density,preserving identity integrity.This integrated approach enables adaptive responses to complex visual scenarios,Achieving exceptional performance across all evaluation metrics,including aHigher Order TrackingAccuracy(HOTA)of 67.05%,a Multiple Object Tracking Accuracy(MOTA)of 82.4%,an ID F1 Score(IDF1)of 83.1%,and a total of 1052 Identity Switches(IDSW)on theMOT17;66.61%HOTA,78.3%MOTA,82.1%IDF1,and a total of 748 IDSWonMOT20;and 66.4%HOTA,92.32%MOTA,and 68.96%IDF1 on DanceTrack.With fixed thresholds,the full HAMOT model(all six components)achieves real-time functionality at 24 FPS on MOT17 using RTX3090,ensuring robustness and scalability for real-world MOT applications.展开更多
Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embed...Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embedded methods,have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data.Deep learning-based FS methods,particularly Convolutional Neural Networks(CNNs)and autoencoders,have demonstrated superior performance but lack interpretability.Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution,offering improved accuracy and explainability.Furthermore,integratingmulti-modal imaging data(e.g.,MagneticResonance Imaging(MRI),ComputedTomography(CT),Positron Emission Tomography(PET),and Ultrasound(US))poses additional challenges in FS,necessitating advanced feature fusion strategies.Multi-modal feature fusion combines information fromdifferent imagingmodalities to improve diagnostic accuracy.Recently,quantum computing has gained attention as a revolutionary approach for FS,providing the potential to handle high-dimensional medical data more efficiently.This systematic literature review comprehensively examines classical,Deep Learning(DL),hybrid,and quantum-based FS techniques inmedical imaging.Key outcomes include a structured taxonomy of FS methods,a critical evaluation of their performance across modalities,and identification of core challenges such as computational burden,interpretability,and ethical considerations.Future research directions—such as explainable AI(XAI),federated learning,and quantum-enhanced FS—are also emphasized to bridge the current gaps.This review provides actionable insights for developing scalable,interpretable,and clinically applicable FS methods in the evolving landscape of medical imaging.展开更多
本文提出了光码多分址(CDMA)和光密集波分复用(DWDM)的混合系统,全面研究了四波混频(FWM)的影响。在这个系统中,主要存在两个四波混频问题:包括多址干扰(MAI)和码间干扰(ISI)的帧间四波混频和信道内四波混频。结果表明,综合考虑信道间...本文提出了光码多分址(CDMA)和光密集波分复用(DWDM)的混合系统,全面研究了四波混频(FWM)的影响。在这个系统中,主要存在两个四波混频问题:包括多址干扰(MAI)和码间干扰(ISI)的帧间四波混频和信道内四波混频。结果表明,综合考虑信道间和信道内四波混频的影响,最佳发射功率可选为18 d Bm。当发射功率大于18 d Bm时,混合系统的误码率(BER)将增加。基于此,本文提出了一种电光相位调制器(EOPM)模块,将其放置在波分复用器之后,通过抑制信道内四波混频的影响,同时调制所有波长信号的相位,从而增加混合系统的非线性容限,这极大地改善了基于OOK传输的光学CDMA-DWDM混合系统的性能。此外,由于多对角线(MD)结构具有零互相关特性,通过使用多对角线识别序列码可以减少多址干扰的影响。结果还表明,CDMA技术与色散相结合有助于降低信道间四波混频的影响。此外,识别序列码间隔在减轻码间干扰中起着至关重要的作用,如结果所示,当识别序列码间隔压缩至比特持续时间的25%时,可以避免码间干扰,此时所提出的混合系统的性能最佳。展开更多
The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a se...The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.展开更多
Due to rapid growth in wireless communication technology,higher bandwidth requirement for advance telecommunication systems,capable of operating on two or higher bands with higher channel capacities and minimum distor...Due to rapid growth in wireless communication technology,higher bandwidth requirement for advance telecommunication systems,capable of operating on two or higher bands with higher channel capacities and minimum distortion losses is desired.In this paper,a compact Ultra-Wideband(UWB)V-shaped monopole antenna is presented.UWB response is achieved by modifying the ground plane with Chichen Itzia inspired rectangular staircase shape.The proposed V-shaped is designed by incorporating a rectangle,and an inverted isosceles triangle using FR4 substrate.The size of the antenna is 25 mm×26 mm×1.6 mm.The proposed V-shaped monopole antenna produces bandwidth response of 3 GHz Industrial,Scientific,and Medical(ISM),Worldwide Interoperability for Microwave Access(WiMAX),(IEEE 802.11/HIPERLAN band,5G sub 6 GHz)which with an additional square cut amplified the bandwidth response up to 8 GHz ranging from 3.1 GHz to 10.6 GHz attaining UWB defined by Federal Communications Commission(FCC)with a maximum gain of 3.83 dB.The antenna is designed in Ansys HFSS.Results for key performance parameters of the antenna are presented.The measured results are in good agreement with the simulated results.Due to flat gain,uniform group delay,omni directional radiation pattern characteristics and well-matched impedance,the proposed antenna is suitable for WiMAX,ISM and heterogeneous wireless systems.展开更多
Design of a robust production facility layout with minimum handling cost （MHC） presents an appropriate approach to tackle facility layout problems in a dynamic volatile environment, in which product demands randomly...Design of a robust production facility layout with minimum handling cost （MHC） presents an appropriate approach to tackle facility layout problems in a dynamic volatile environment, in which product demands randomly change in each planning period. The objective of the design is to find the robust facility layout with minimum total material handling cost over the entire multiperiod planning horizon. This paper proposes a new mathematical model for designing robust machine layout in the stochastic dynamic environment of manufacturing systems using quadratic assignment problem （QAP） formulation. In this investigation, product demands are assumed to be normally distributed random variables with known expected value, variance, and covariance that randomly change from period to period. The proposed model was verified and validated using randomly generated numerical data and benchmark examples. The effect of dependent product demands and varying interest rate on the total cost function of the proposed model has also been investigated. Sensitivity analysis on the proposed model has been performed. Dynamic programming and simulated annealing optimization algorithms were used in solving the modeled example problems.展开更多
AIM:To sufficiently improve magnetic resonance cholangiopancreatography(MRCP) quality to enable reliable computer-aided diagnosis(CAD).METHODS:A set of image enhancement strategies that included filters(i.e.Gaussian,m...AIM:To sufficiently improve magnetic resonance cholangiopancreatography(MRCP) quality to enable reliable computer-aided diagnosis(CAD).METHODS:A set of image enhancement strategies that included filters(i.e.Gaussian,median,Wiener and Perona-Malik),wavelets(i.e.contourlet,ridgelet and a non-orthogonal noise compensation implementation),graph-cut approaches using lazy-snapping and Phase Unwrapping MAxflow,and binary thresholding using a fixed threshold and dynamic thresholding via histogram analysis were implemented to overcome the adverse characteristics of MRCP images such as acquisition noise,artifacts,partial volume effect and large inter-and intra-patient image intensity variations,all of which pose problems in application development.Subjective evaluation of several popular pre-processing techniques was undertaken to improve the quality of the 2D MRCP images and enhance the detection of the significant biliary structures within them,with the purpose of biliary disease detection.RESULTS:The results varied as expected since each algorithm capitalized on different characteristics of the images.For denoising,the Perona-Malik and contourlet approaches were found to be the most suitable.In terms of extraction of the significant biliary structures and removal of background,the thresholding approaches performed well.The interactive scheme performed the best,especially by using the strengths of the graphcut algorithm enhanced by user-friendly lazy-snapping for foreground and background marker selection.CONCLUSION:Tests show promising results for some techniques,but not others,as viable image enhancement modules for automatic CAD systems for biliary and liver diseases.展开更多
In this paper,a low cost,highly efficient and low profile monopole antenna for ultra-wideband(UWB)applications is presented.A new inverted triangular-shape structure possessing meander lines is designed to achieve a w...In this paper,a low cost,highly efficient and low profile monopole antenna for ultra-wideband(UWB)applications is presented.A new inverted triangular-shape structure possessing meander lines is designed to achieve a wideband response and high efficiency.To design the proposed structure,three steps are utilized to achieve an UWB response.The bandwidth of the proposed antenna is improved with changing meander lines parameters,miniaturization of the ground width and optimization of the feeding line.The measured and simulated frequency band ranges from 3.2 to 12 GHz,while the radiation patterns are measured at 4,5.3,6 and 8 GHz frequency bands.The overall volume of the proposed antenna is 26×25×1.6 mm^(3);whereas the FR4 material is used as a substrate with a relative permittivity and loss tangent of 4.3 and 0.025,correspondingly.The peak gain of 4 dB is achieved with a radiation efficiency of 80 to 98%for the entire wideband.Design modelling of proposed antenna is performed in ANSYS HFSS 13 software.A decent consistency between the simulated and measured results is accomplished which shows that the proposed antenna is a potential candidate for the UWB applications.展开更多
The naïve Bayes classifier is one of the commonly used data mining methods for classification.Despite its simplicity,naïve Bayes is effective and computationally efficient.Although the strong attribute indep...The naïve Bayes classifier is one of the commonly used data mining methods for classification.Despite its simplicity,naïve Bayes is effective and computationally efficient.Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning,this assumption may not hold in real-world applications.Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption.While these methods improve the classification performance,they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational time.One approach to reduce the naïvetéof the classifier is to incorporate attribute weights in the conditional probability.In this paper,we proposed a method to incorporate attribute weights to naïve Bayes.To evaluate the performance of our method,we used the public benchmark datasets.We compared our method with the standard naïve Bayes and baseline attribute weighting methods.Experimental results show that our method to incorporate attribute weights improves the classification performance compared to both standard naïve Bayes and baseline attribute weighting methods in terms of classification accuracy and F1,especially when the independence assumption is strongly violated,which was validated using the Chi-square test of independence.展开更多
In this paper,a unit cell of a single-negative metamaterial structure loaded with a meander line and defected ground structure(DGS)is investigated as the principle radiating element of an antenna.The unit cell antenna...In this paper,a unit cell of a single-negative metamaterial structure loaded with a meander line and defected ground structure(DGS)is investigated as the principle radiating element of an antenna.The unit cell antenna causes even or odd mode resonances similar to the unit cell structure depending on the orientation of the microstrip feed used to excite the unit cell.However,the orientation which gives low-frequency resonance is considered here.The unit cell antenna is then loaded with a meander line which is parallel to the split bearing side and connects the other two sides orthogonal to the split bearing side.This modified structure excites another mode of resonance at high frequency when a meander line defect is loaded on the metallic ground plane.Specific parameters of the meander line structure,the DGS shape,and the unit cell are optimized to place these two resonances at different frequencies with proper frequency intervals to enhance the bandwidth.Finally,the feed is placed in an offset position for better impedance matching without affecting the bandwidth The compact dimension of the antenna is 0.25λL×0.23λL×0.02λL,whereλL is the free space wavelength with respect to the center frequency of the impedance bandwidth.The proposed antenna is fabricated and measured.Experimental results reveal that the modified design gives monopole like radiation patterns which achieves a fractional operating bandwidth of 26.6%,from 3.26 to 4.26 GHz for|S11|<−10 dB and a pick gain of 1.26 dBi is realized.In addition,the simulated and measured crosspolarization levels are both less than−15 dB in the horizontal plane.展开更多
In order to develop an automated segmentation system for Computed Tomography （CT） brain images, a new approach which consists of several unsupervised segmcotation techniques was introduced. The system segments the C...In order to develop an automated segmentation system for Computed Tomography （CT） brain images, a new approach which consists of several unsupervised segmcotation techniques was introduced. The system segments the CT brain images into three partitions, i. e., abnormalities, cerebrospinal fluid （CSF）, and brain matter. Our approach consists of two phase-segmentation methods. In the first phase segmentation, k-means and fuzzy cmeans （FCM） methods were implemented to segment and transform the images into the binary images. Based on the connected component in binary images, a decision tree was employed for the annotation of normal or abnormal regions. In the second phase segmentation, the modified FCM with population-diameter independent （PDI） segmentation was applied to segment the images into CSF and brain matter. The experimental results have shown that our proposed system is feasible and yield satisfactory results.展开更多
CSTR(Continuous stirred tank reactor)is employed in process control and chemical industries to improve response characteristics and system efficiency.It has a highly nonlinear characteristic that includes complexities...CSTR(Continuous stirred tank reactor)is employed in process control and chemical industries to improve response characteristics and system efficiency.It has a highly nonlinear characteristic that includes complexities in its control and design.Dynamic performance is compassionate to change in system parameterswhich need more effort for planning a significant controller for CSTR.The reactor temperature changes in either direction from the defined reference value.It is important to note that the intensity of chemical actions inside the CSTR is dependent on the various levels of temperature,and deviation from reference values may cause degradation of biomass quality.Design and implementation of an appropriate adaptive controller for such a nonlinear system are essential.In this paper,a conventional Proportional Integral Derivative(PID)controller is designed.The conventional techniques to deal with constraints suffer severe limitations like it has fixed controller parameters.Hence,A novel method is applied for computing the PID controller parameters using a swarm algorithm that overcomes the conventional controller’s limitation.In the proposed technique,PID parameters are tuned by Particle Swarm Optimization(PSO).It is not easy to choose the suitable objective function to design a PID controller using PSO to get an optimal response.In this article,a multi-objective function is proposed for PSO based controller design of CSTR.展开更多
基金The APC was funded by Research Management Center, Multimedia University, Malaysia.
文摘Electrical energy can be harvested from the rotational kinetic energy of moving bodies,consisting of both mechanical and kinetic energy as a potential power source through electromagnetic induction,similar to wind energy applications.In industries,rotational bodies are commonly present in operations,yet this kinetic energy remains untapped.This research explores the energy generation characteristics of two rotational body types,disk-shaped and cylinder-shaped under specific experimental setups.The hardware setup included a direct current(DC)motor driver,power supply,DC generator,mechanical support,and load resistance,while the software setup involved automation testing tools and data logging.Electromagnetic induction was used to harvest energy,and experiments were conducted at room temperature(25℃)with controlled variables like speed and friction.Results showed the disk-shaped body exhibited higher energy efficiency than the cylinder-shaped body,largely due to lower mechanical losses.The disk required only two bearings,while the cylinder required four,resulting in lower bearing losses for the disk.Additionally,the disk experienced only air friction,whereas the cylinder encountered friction from a soft,uneven rubber material,increasing surface contact losses.Under a 40 W resistive load,the disk demonstrated a 17.1%energy loss due to mechanical friction,achieving up to 15.55 J of recycled energy.Conversely,the cylinder body experienced a 48.05%energy loss,delivering only 51.95%of energy to the load.These insights suggest significant potential for designing efficient energy recycling systems in industrial settings,particularly in manufacturing and processing industries where rotational machinery is prevalent.Despite its lower energy density,this system could be beneficially integrated with energy storage solutions,enhancing sustainability in industrial practices.
基金funded by Multimedia University(Ref:MMU/RMC/PostDoc/NEW/2024/9804).
文摘Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves resource allocation techniques is host load prediction.This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response times.To solve this issue and accurately predict cloud capacity,cloud data centers use prediction algorithms.This permits dynamic cloud scalability while maintaining superior service quality.For host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this work.First,the suggested hybrid model is input is subjected to the vector auto regression technique.The data in many variables that,prior to analysis,has been filtered to eliminate linear interdependencies.After that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing unit.The next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series components.The key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant unit.Cloud systems require accurate prediction due to the increasing degrees of unpredictability in data centers.Because of this,two actual load traces were used in this study’s assessment of the performance.An example of the load trace is in the typical dispersed system.In comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.
文摘Industrial processes often involve rotating machinery that generates substantial kinetic energy,much of which remains untapped.Harvesting rotational kinetic energy offers a promising solution to reduce energy waste and improve energy efficiency in industrial applications.This research investigates the potential of electromagnetic induction for harvesting rotational kinetic energy from industrial machinery.A comparative study was conducted between disk and cylinder-shaped rotational bodies to evaluate their energy efficiency under various load conditions.Experimental results demonstrated that the disk body exhibited higher energy efficiency,primarily due to lower mechanical losses compared to the cylinder body.A power management circuit was developed to regulate and store the harvested energy,integrating voltage,current,and speed sensors along with a charge controller for battery storage.The experimental setup successfully converted rotational kinetic energy into usable electrical power,with the disk achieving up to 16.33 J of recycled energy,outperforming the cylinder.The disk body demonstrated higher energy recovery efficiency compared to the cylinder,particularly under the 40 W resistive load condition.These findings demonstrate the feasibility of implementing energy recycling systems in industrial settings to enhance sustainability,reduce energy consumption,and minimize waste.Future research should focus on optimizing power management systems and improving energy harvesting efficiency to enable wider adoption of energy recycling technologies in various industrial applications.
基金A research grant from the Multimedia University,Malaysia supports this work。
文摘Traditional rule-based IntrusionDetection Systems(IDS)are commonly employed owing to their simple design and ability to detect known threats.Nevertheless,as dynamic network traffic and a new degree of threats exist in IoT environments,these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy.In this study,we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model(ERBM)to classify the packets better and identify intrusions.The ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic,where three membership functions are created to classify all the network traffic features as low,medium,or high to remain situationally aware of the environment.Such fuzzy logic sets produce adaptive detection rules by reducing data uncertainty.Also,for further classification,machine learning classifiers such as Decision Tree(DT),Random Forest(RF),and Neural Networks(NN)learn complex ways of attacks and make the detection process more precise.A thorough performance evaluation using different metrics,including accuracy,precision,recall,F1 Score,detection rate,and false-positive rate,verifies the supremacy of ERBM over classical IDS.Under extensive experiments,the ERBM enables a remarkable detection rate of 99%with considerably fewer false positives than the conventional models.Integrating the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions,the ERBM systemprovides a unique,scalable,data-driven approach to IoT intrusion detection.This research presents a major enhancement initiative in the context of rule-based IDS,introducing improvements in accuracy to evolving IoT threats.
基金supported in part by Multimedia University Research Fellow under Grant MMUI/250008in part by Telekom Research and Development Sdn Bhd under Grant RDTC/241149.
文摘Vehicle recognition plays a vital role in intelligent transportation systems,law enforcement,access control,and security operations—domains that are becoming increasingly dynamic and complex.Despite advancements,most existing solutions remain siloed,addressing individual tasks such as vehicle make and model recognition(VMMR),automatic number plate recognition(ANPR),and color classification separately.This fragmented approach limits real-world efficiency,leading to slower processing,reduced accuracy,and increased operational costs,particularly in traffic monitoring and surveillance scenarios.To address these limitations,we present a unified framework that consolidates all three recognition tasks into a single,lightweight system.The framework utilizes MobileNetV2 for efficient VMMR,YOLO(You Only Look Once)for accurate license plate detection,and histogram-based clustering in the HSV color space for precise color identification.Rather than optimizing each module in isolation,our approach emphasizes tight integration,enabling improved performance and reliability.The system also features adaptive image calibration and robust algorithmic enhancements to ensure consistent results under varying environmental conditions.Experimental evaluations demonstrate that the proposedmodel achieves a combined accuracy of 93.3%,outperforming traditional methods and offering practical scalability for deployment in real-world transportation infrastructures.
基金funding from the Princess Nourah bint Abdulrahman University Researchers Supporting Project(PNURSP2025R195)the University of Bisha through its Fast-Track Research Support Program.
文摘Artificial intelligence(AI),particularly deep learning algorithms utilizing convolutional neural networks,plays an increasingly pivotal role in enhancing medical image examination.It demonstrates the potential for improving diagnostic accuracy within dental care.Orthopantomograms(OPGs)are essential in dentistry;however,their manual interpretation is often inconsistent and tedious.To the best of our knowledge,this is the first comprehensive application of YOLOv5m for the simultaneous detection and classification of six distinct dental pathologies using panoramic OPG images.The model was trained and refined on a custom dataset that began with 232 panoramic radiographs and was later expanded to 604 samples.These included annotated subclasses representing Caries,Infection,Impacted Teeth,Fractured Teeth,Broken Crowns,and Healthy conditions.The training was performed using GPU resources alongside tuned hyperparameters of batch size,learning rate schedule,and early stopping tailored for generalization to prevent overfitting.Evaluation on a held-out test set showed strong performance in the detection and localization of various dental pathologies and robust overall accuracy.At an IoU of 0.5,the system obtained a mean precision of 94.22%and recall of 90.42%,with mAP being 93.71%.This research confirms the use of YOLOv5m as a robust,highly efficient AI technology for the analysis of dental pathologies using OPGs,providing a clinically useful solution to enhance workflow efficiency and aid in sustaining consistency in complex multi-dimensional case evaluations.
基金supported in part by Multimedia University under the Research Fellow Grant MMUI/250008in part by Telekom Research&Development Sdn Bhd underGrantRDTC/241149Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R140),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The Internet of Things(IoT)ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027,operating in distributed networks with resource limitations and diverse system architectures.The current conventional intrusion detection systems(IDS)face scalability problems and trust-related issues,but blockchain-based solutions face limitations because of their low transaction throughput(Bitcoin:7 TPS(Transactions Per Second),Ethereum:15-30 TPS)and high latency.The research introduces MBID(Multi-Tier Blockchain Intrusion Detection)as a groundbreaking Multi-Tier Blockchain Intrusion Detection System with AI-Enhanced Detection,which solves the problems in huge IoT networks.The MBID system uses a four-tier architecture that includes device,edge,fog,and cloud layers with blockchain implementations and Physics-Informed Neural Networks(PINNs)for edge-based anomaly detection and a dual consensus mechanism that uses Honesty-based Distributed Proof-of-Authority(HDPoA)and Delegated Proof of Stake(DPoS).The system achieves scalability and efficiency through the combination of dynamic sharding and Interplanetary File System(IPFS)integration.Experimental evaluations demonstrate exceptional performance,achieving a detection accuracy of 99.84%,an ultra-low false positive rate of 0.01% with a False Negative Rate of 0.15%,and a near-instantaneous edge detection latency of 0.40 ms.The system demonstrated an aggregate throughput of 214.57 TPS in a 3-shard configuration,providing a clear,evidence-based path for horizontally scaling to support overmillions of devices with exceeding throughput.The proposed architecture represents a significant advancement in blockchain-based security for IoT networks,effectively balancing the trade-offs between scalability,security,and decentralization.
基金supported in part by Multimedia University under the Research Fellow Grant MMUI/250008in part by Telekom Research&Development Sdn Bhd under Grants RDTC/241149 and RDTC/231095+1 种基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R140)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We propose HAMOT,a hierarchical adaptive multi-object tracker that solves these challenges with a novel,unified framework.Unlike previous methods that rely on isolated components,HAMOT incorporates a Swin Transformer-based Adaptive Enhancement(STAE)module—comprising Scene-Adaptive Transformer Enhancement and Confidence-Adaptive Feature Refinement—to improve detection under low-visibility conditions.The hierarchical DynamicGraphNeuralNetworkwith TemporalAttention(DGNN-TA)models both short-and long-termassociations,and the Adaptive Unscented Kalman Filter with Gated Recurrent Unit(AUKF-GRU)ensures accurate motion prediction.The novel Graph-Based Density-Aware Clustering(GDAC)improves occlusion recovery by adapting to scene density,preserving identity integrity.This integrated approach enables adaptive responses to complex visual scenarios,Achieving exceptional performance across all evaluation metrics,including aHigher Order TrackingAccuracy(HOTA)of 67.05%,a Multiple Object Tracking Accuracy(MOTA)of 82.4%,an ID F1 Score(IDF1)of 83.1%,and a total of 1052 Identity Switches(IDSW)on theMOT17;66.61%HOTA,78.3%MOTA,82.1%IDF1,and a total of 748 IDSWonMOT20;and 66.4%HOTA,92.32%MOTA,and 68.96%IDF1 on DanceTrack.With fixed thresholds,the full HAMOT model(all six components)achieves real-time functionality at 24 FPS on MOT17 using RTX3090,ensuring robustness and scalability for real-world MOT applications.
文摘Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embedded methods,have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data.Deep learning-based FS methods,particularly Convolutional Neural Networks(CNNs)and autoencoders,have demonstrated superior performance but lack interpretability.Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution,offering improved accuracy and explainability.Furthermore,integratingmulti-modal imaging data(e.g.,MagneticResonance Imaging(MRI),ComputedTomography(CT),Positron Emission Tomography(PET),and Ultrasound(US))poses additional challenges in FS,necessitating advanced feature fusion strategies.Multi-modal feature fusion combines information fromdifferent imagingmodalities to improve diagnostic accuracy.Recently,quantum computing has gained attention as a revolutionary approach for FS,providing the potential to handle high-dimensional medical data more efficiently.This systematic literature review comprehensively examines classical,Deep Learning(DL),hybrid,and quantum-based FS techniques inmedical imaging.Key outcomes include a structured taxonomy of FS methods,a critical evaluation of their performance across modalities,and identification of core challenges such as computational burden,interpretability,and ethical considerations.Future research directions—such as explainable AI(XAI),federated learning,and quantum-enhanced FS—are also emphasized to bridge the current gaps.This review provides actionable insights for developing scalable,interpretable,and clinically applicable FS methods in the evolving landscape of medical imaging.
基金Supported by Multimedia University(Malaysia),project SAP ID(MMUI/160092)
文摘本文提出了光码多分址(CDMA)和光密集波分复用(DWDM)的混合系统,全面研究了四波混频(FWM)的影响。在这个系统中,主要存在两个四波混频问题:包括多址干扰(MAI)和码间干扰(ISI)的帧间四波混频和信道内四波混频。结果表明,综合考虑信道间和信道内四波混频的影响,最佳发射功率可选为18 d Bm。当发射功率大于18 d Bm时,混合系统的误码率(BER)将增加。基于此,本文提出了一种电光相位调制器(EOPM)模块,将其放置在波分复用器之后,通过抑制信道内四波混频的影响,同时调制所有波长信号的相位,从而增加混合系统的非线性容限,这极大地改善了基于OOK传输的光学CDMA-DWDM混合系统的性能。此外,由于多对角线(MD)结构具有零互相关特性,通过使用多对角线识别序列码可以减少多址干扰的影响。结果还表明,CDMA技术与色散相结合有助于降低信道间四波混频的影响。此外,识别序列码间隔在减轻码间干扰中起着至关重要的作用,如结果所示,当识别序列码间隔压缩至比特持续时间的25%时,可以避免码间干扰,此时所提出的混合系统的性能最佳。
文摘The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.
基金This work was supported by the Research Program through the National Research Foundation of Korea,NRF-2019R1A2C1005920,S.K.
文摘Due to rapid growth in wireless communication technology,higher bandwidth requirement for advance telecommunication systems,capable of operating on two or higher bands with higher channel capacities and minimum distortion losses is desired.In this paper,a compact Ultra-Wideband(UWB)V-shaped monopole antenna is presented.UWB response is achieved by modifying the ground plane with Chichen Itzia inspired rectangular staircase shape.The proposed V-shaped is designed by incorporating a rectangle,and an inverted isosceles triangle using FR4 substrate.The size of the antenna is 25 mm×26 mm×1.6 mm.The proposed V-shaped monopole antenna produces bandwidth response of 3 GHz Industrial,Scientific,and Medical(ISM),Worldwide Interoperability for Microwave Access(WiMAX),(IEEE 802.11/HIPERLAN band,5G sub 6 GHz)which with an additional square cut amplified the bandwidth response up to 8 GHz ranging from 3.1 GHz to 10.6 GHz attaining UWB defined by Federal Communications Commission(FCC)with a maximum gain of 3.83 dB.The antenna is designed in Ansys HFSS.Results for key performance parameters of the antenna are presented.The measured results are in good agreement with the simulated results.Due to flat gain,uniform group delay,omni directional radiation pattern characteristics and well-matched impedance,the proposed antenna is suitable for WiMAX,ISM and heterogeneous wireless systems.
基金Supported by the Ministry of Higher Education of Malaysia through the Foundation Research(Grant Scheme no.FRGS/1/2012/TK01/MMU/02/2)
文摘Design of a robust production facility layout with minimum handling cost （MHC） presents an appropriate approach to tackle facility layout problems in a dynamic volatile environment, in which product demands randomly change in each planning period. The objective of the design is to find the robust facility layout with minimum total material handling cost over the entire multiperiod planning horizon. This paper proposes a new mathematical model for designing robust machine layout in the stochastic dynamic environment of manufacturing systems using quadratic assignment problem （QAP） formulation. In this investigation, product demands are assumed to be normally distributed random variables with known expected value, variance, and covariance that randomly change from period to period. The proposed model was verified and validated using randomly generated numerical data and benchmark examples. The effect of dependent product demands and varying interest rate on the total cost function of the proposed model has also been investigated. Sensitivity analysis on the proposed model has been performed. Dynamic programming and simulated annealing optimization algorithms were used in solving the modeled example problems.
基金Supported by The Brain Gain Malaysia international fellowship and post-doctoral program grant under the Ministry of Science,Technology and Innovation,Malaysia
文摘AIM:To sufficiently improve magnetic resonance cholangiopancreatography(MRCP) quality to enable reliable computer-aided diagnosis(CAD).METHODS:A set of image enhancement strategies that included filters(i.e.Gaussian,median,Wiener and Perona-Malik),wavelets(i.e.contourlet,ridgelet and a non-orthogonal noise compensation implementation),graph-cut approaches using lazy-snapping and Phase Unwrapping MAxflow,and binary thresholding using a fixed threshold and dynamic thresholding via histogram analysis were implemented to overcome the adverse characteristics of MRCP images such as acquisition noise,artifacts,partial volume effect and large inter-and intra-patient image intensity variations,all of which pose problems in application development.Subjective evaluation of several popular pre-processing techniques was undertaken to improve the quality of the 2D MRCP images and enhance the detection of the significant biliary structures within them,with the purpose of biliary disease detection.RESULTS:The results varied as expected since each algorithm capitalized on different characteristics of the images.For denoising,the Perona-Malik and contourlet approaches were found to be the most suitable.In terms of extraction of the significant biliary structures and removal of background,the thresholding approaches performed well.The interactive scheme performed the best,especially by using the strengths of the graphcut algorithm enhanced by user-friendly lazy-snapping for foreground and background marker selection.CONCLUSION:Tests show promising results for some techniques,but not others,as viable image enhancement modules for automatic CAD systems for biliary and liver diseases.
基金the Research Program through the National Research Foundation of Korea,NRF-2019R1A2C1005920,S.K.
文摘In this paper,a low cost,highly efficient and low profile monopole antenna for ultra-wideband(UWB)applications is presented.A new inverted triangular-shape structure possessing meander lines is designed to achieve a wideband response and high efficiency.To design the proposed structure,three steps are utilized to achieve an UWB response.The bandwidth of the proposed antenna is improved with changing meander lines parameters,miniaturization of the ground width and optimization of the feeding line.The measured and simulated frequency band ranges from 3.2 to 12 GHz,while the radiation patterns are measured at 4,5.3,6 and 8 GHz frequency bands.The overall volume of the proposed antenna is 26×25×1.6 mm^(3);whereas the FR4 material is used as a substrate with a relative permittivity and loss tangent of 4.3 and 0.025,correspondingly.The peak gain of 4 dB is achieved with a radiation efficiency of 80 to 98%for the entire wideband.Design modelling of proposed antenna is performed in ANSYS HFSS 13 software.A decent consistency between the simulated and measured results is accomplished which shows that the proposed antenna is a potential candidate for the UWB applications.
文摘The naïve Bayes classifier is one of the commonly used data mining methods for classification.Despite its simplicity,naïve Bayes is effective and computationally efficient.Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning,this assumption may not hold in real-world applications.Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption.While these methods improve the classification performance,they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational time.One approach to reduce the naïvetéof the classifier is to incorporate attribute weights in the conditional probability.In this paper,we proposed a method to incorporate attribute weights to naïve Bayes.To evaluate the performance of our method,we used the public benchmark datasets.We compared our method with the standard naïve Bayes and baseline attribute weighting methods.Experimental results show that our method to incorporate attribute weights improves the classification performance compared to both standard naïve Bayes and baseline attribute weighting methods in terms of classification accuracy and F1,especially when the independence assumption is strongly violated,which was validated using the Chi-square test of independence.
文摘In this paper,a unit cell of a single-negative metamaterial structure loaded with a meander line and defected ground structure(DGS)is investigated as the principle radiating element of an antenna.The unit cell antenna causes even or odd mode resonances similar to the unit cell structure depending on the orientation of the microstrip feed used to excite the unit cell.However,the orientation which gives low-frequency resonance is considered here.The unit cell antenna is then loaded with a meander line which is parallel to the split bearing side and connects the other two sides orthogonal to the split bearing side.This modified structure excites another mode of resonance at high frequency when a meander line defect is loaded on the metallic ground plane.Specific parameters of the meander line structure,the DGS shape,and the unit cell are optimized to place these two resonances at different frequencies with proper frequency intervals to enhance the bandwidth.Finally,the feed is placed in an offset position for better impedance matching without affecting the bandwidth The compact dimension of the antenna is 0.25λL×0.23λL×0.02λL,whereλL is the free space wavelength with respect to the center frequency of the impedance bandwidth.The proposed antenna is fabricated and measured.Experimental results reveal that the modified design gives monopole like radiation patterns which achieves a fractional operating bandwidth of 26.6%,from 3.26 to 4.26 GHz for|S11|<−10 dB and a pick gain of 1.26 dBi is realized.In addition,the simulated and measured crosspolarization levels are both less than−15 dB in the horizontal plane.
文摘In order to develop an automated segmentation system for Computed Tomography （CT） brain images, a new approach which consists of several unsupervised segmcotation techniques was introduced. The system segments the CT brain images into three partitions, i. e., abnormalities, cerebrospinal fluid （CSF）, and brain matter. Our approach consists of two phase-segmentation methods. In the first phase segmentation, k-means and fuzzy cmeans （FCM） methods were implemented to segment and transform the images into the binary images. Based on the connected component in binary images, a decision tree was employed for the annotation of normal or abnormal regions. In the second phase segmentation, the modified FCM with population-diameter independent （PDI） segmentation was applied to segment the images into CSF and brain matter. The experimental results have shown that our proposed system is feasible and yield satisfactory results.
基金University Malaysia Sabah fully funds this research under the grant number F08/PGRG/1908/2019,Ag.Asri Ag.Ibrahim received the grant,sponsors’websites:https://www.u ms.edu.my.Conflicts of Interest。
文摘CSTR(Continuous stirred tank reactor)is employed in process control and chemical industries to improve response characteristics and system efficiency.It has a highly nonlinear characteristic that includes complexities in its control and design.Dynamic performance is compassionate to change in system parameterswhich need more effort for planning a significant controller for CSTR.The reactor temperature changes in either direction from the defined reference value.It is important to note that the intensity of chemical actions inside the CSTR is dependent on the various levels of temperature,and deviation from reference values may cause degradation of biomass quality.Design and implementation of an appropriate adaptive controller for such a nonlinear system are essential.In this paper,a conventional Proportional Integral Derivative(PID)controller is designed.The conventional techniques to deal with constraints suffer severe limitations like it has fixed controller parameters.Hence,A novel method is applied for computing the PID controller parameters using a swarm algorithm that overcomes the conventional controller’s limitation.In the proposed technique,PID parameters are tuned by Particle Swarm Optimization(PSO).It is not easy to choose the suitable objective function to design a PID controller using PSO to get an optimal response.In this article,a multi-objective function is proposed for PSO based controller design of CSTR.
