This paper presents an innovative and effective control strategy tailored for a deregulated,diversified energy system involving multiple interconnected area.Each area integrates a unique mix of power generation techno...This paper presents an innovative and effective control strategy tailored for a deregulated,diversified energy system involving multiple interconnected area.Each area integrates a unique mix of power generation technologies:Area 1 combines thermal,hydro,and distributed generation;Area 2 utilizes a blend of thermal units,distributed solar technologies(DST),and hydro power;andThird control area hosts geothermal power station alongside thermal power generation unit and hydropower units.The suggested control system employs a multi-layered approach,featuring a blended methodology utilizing the Tilted Integral Derivative controller(TID)and the Fractional-Order Integral method to enhance performance and stability.The parameters of this hybrid TID-FOI controller are finely tuned using an advanced optimization method known as the Walrus Optimization Algorithm(WaOA).Performance analysis reveals that the combined TID-FOI controller significantly outperforms the TID and PID controllers when comparing their dynamic response across various system configurations.The study also incorporates investigation of redox flow batteries within the broader scope of energy storage applications to assess their impact on system performance.In addition,the research explores the controller’s effectiveness under different power exchange scenarios in a deregulated market,accounting for restrictions on generation ramp rates and governor hysteresis effects in dynamic control.To ensure the reliability and resilience of the presented methodology,the system transitions and develops across a broad range of varying parameters and stochastic load fluctuation.To wrap up,the study offers a pioneering control approach-a hybrid TID-FOI controller optimized via the Walrus Optimization Algorithm(WaOA)-designed for enhanced stability and performance in a complex,three-region hybrid energy system functioning within a deregulated framework.展开更多
The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that...The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.The overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.展开更多
The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial antennas.The radiation cost and quality factor of the antenna are influenced by the size of the antenna.Metamateri...The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial antennas.The radiation cost and quality factor of the antenna are influenced by the size of the antenna.Metamaterial antennas allow for the circumvention of the bandwidth restriction for small antennas.Antenna parameters have recently been predicted using machine learning algorithms in existing literature.Machine learning can take the place of the manual process of experimenting to find the ideal simulated antenna parameters.The accuracy of the prediction will be primarily dependent on the model that is used.In this paper,a novel method for forecasting the bandwidth of the metamaterial antenna is proposed,based on using the Pearson Kernel as a standard kernel.Along with these new approaches,this paper suggests a unique hypersphere-based normalization to normalize the values of the dataset attributes and a dimensionality reduction method based on the Pearson kernel to reduce the dimension.A novel algorithm for optimizing the parameters of Convolutional Neural Network(CNN)based on improved Bat Algorithm-based Optimization with Pearson Mutation(BAO-PM)is also presented in this work.The prediction results of the proposed work are better when compared to the existing models in the literature.展开更多
We have realized efficient photopatterning and high-quality ZrO_(2)films through combustion synthesis and manufactured resistive random access memory(RRAM)devices with excellent switching stability at low temperatures...We have realized efficient photopatterning and high-quality ZrO_(2)films through combustion synthesis and manufactured resistive random access memory(RRAM)devices with excellent switching stability at low temperatures(250℃)using these approaches.Combustion synthesis reduces the energy required for oxide conversion,thus accelerating the decomposition of organic ligands in the UV-exposed area,and promoting the formation of metal-oxygen networks,contributing to patterning.Thermal analysis confirmed a reduction in the conversion temperature of combustion precursors,and the prepared combustion ZrO_(2)films exhibited a high proportion of metal-oxygen bonding that constitutes the oxide lattice,along with an amorphous phase.Furthermore,the synergistic effect of combustion synthesis and UV/O_(3)-assisted photochemical activation resulted in patterned ZrO_(2)films forming even more complete metal-oxygen networks.RRAM devices fabricated with patterned ZrO_(2)films using combustion synthesis exhibited excellent switching characteristics,including a narrow resistance distribution,endurance of 103 cycles,and retention for 105 s at 85℃,despite low-temperature annealing.Combustion synthesis not only enables the formation of high-quality metal oxide films with low external energy but also facilitates improved photopatterning.展开更多
Efficient optical network management poses significant importance in backhaul and access network communicationfor preventing service disruptions and ensuring Quality of Service(QoS)satisfaction.The emerging faultsin o...Efficient optical network management poses significant importance in backhaul and access network communicationfor preventing service disruptions and ensuring Quality of Service(QoS)satisfaction.The emerging faultsin optical networks introduce challenges that can jeopardize the network with a variety of faults.The existingliterature witnessed various partial or inadequate solutions.On the other hand,Machine Learning(ML)hasrevolutionized as a promising technique for fault detection and prevention.Unlike traditional fault managementsystems,this research has three-fold contributions.First,this research leverages the ML and Deep Learning(DL)multi-classification system and evaluates their accuracy in detecting six distinct fault types,including fiber cut,fibereavesdropping,splicing,bad connector,bending,and PC connector.Secondly,this paper assesses the classificationdelay of each classification algorithm.Finally,this work proposes a fiber optics fault prevention algorithm thatdetermines to mitigate the faults accordingly.This work utilized a publicly available fiber optics dataset namedOTDR_Data and applied different ML classifiers,such as Gaussian Naive Bayes(GNB),Logistic Regression(LR),Support Vector Machine(SVM),K-Nearest Neighbor(KNN),Random Forest(RF),and Decision Tree(DT).Moreover,Ensemble Learning(EL)techniques are applied to evaluate the accuracy of various classifiers.In addition,this work evaluated the performance of DL-based Convolutional Neural Network and Long-Short Term Memory(CNN-LSTM)hybrid classifier.The findings reveal that the CNN-LSTM hybrid technique achieved the highestaccuracy of 99%with a delay of 360 s.On the other hand,EL techniques improved the accuracy in detecting fiberoptic faults.Thus,this research comprehensively assesses accuracy and delay metrics for various classifiers andproposes the most efficient attack detection system in fiber optics.展开更多
The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accide...The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.展开更多
Pyrolysis is a rapidly expanding chemical-based recyclable method that complements physical recycling. It avoids improper disposal of post-consumer polymers and mitigates the ecological problems linked to the producti...Pyrolysis is a rapidly expanding chemical-based recyclable method that complements physical recycling. It avoids improper disposal of post-consumer polymers and mitigates the ecological problems linked to the production of new plastic. Nevertheless, while there is a consensus that pyrolysis might be a crucial technology in the years to come, more discussions are needed to address the challenges related to scaling up, the long-term sustainability of the process, and additional variables essential to the advancement of the green economy. Herein, it emphasizes knowledge gaps and methodological issues in current Life Cycle Assessment (LCA), underlining the need for standardized techniques and updated data to support robust decision-making for adopting pyrolysis technologies in waste management strategies. For this purpose, this study reviews the LCAs of pyrolytic processes, encompassing the complete life cycle, from feedstock collection to end-product distribution, including elements such as energy consumption, greenhouse gas emissions, and waste creation. Hence, we evaluate diverse pyrolysis processes, including slow, rapid, and catalytic pyrolysis, emphasizing their distinct efficiency and environmental footprints. Furthermore, we evaluate the impact of feedstock composition, process parameters, and scale of operation on the overall sustainability of pyrolysis-based plastic waste treatment by integrating results from current literature and identifying essential research needs. Therefore, this paper argues that existing LCA studies need more coherence and accuracy. It follows a thorough evaluation of previous research and suggests new insights into methodologies and restrictions.展开更多
Camera Pose Estimating from point and line correspondences is critical in various applications,including robotics,augmented reality,3D reconstruction,and autonomous navigation.Existing methods,such as the Perspective-...Camera Pose Estimating from point and line correspondences is critical in various applications,including robotics,augmented reality,3D reconstruction,and autonomous navigation.Existing methods,such as the Perspective-n-Point(PnP)and Perspective-n-Line(PnL)approaches,offer limited accuracy and robustness in environments with occlusions,noise,or sparse feature data.This paper presents a unified solution,Efficient and Accurate Pose Estimation from Point and Line Correspondences(EAPnPL),combining point-based and linebased constraints to improve pose estimation accuracy and computational efficiency,particularly in low-altitude UAV navigation and obstacle avoidance.The proposed method utilizes quaternion parameterization of the rotation matrix to overcome singularity issues and address challenges in traditional rotation matrix-based formulations.A hybrid optimization framework is developed to integrate both point and line constraints,providing a more robust and stable solution in complex scenarios.The method is evaluated using synthetic and realworld datasets,demonstrating significant improvements in performance over existing techniques.The results indicate that the EAPnPL method enhances accuracy and reduces computational complexity,making it suitable for real-time applications in autonomous UAV systems.This approach offers a promising solution to the limitations of existing camera pose estimation methods,with potential applications in low-altitude navigation,autonomous robotics,and 3D scene reconstruction.展开更多
In many non-motor vehicle traffic accidents in China,the main cause of injury or death for drivers is not wearing a helmet.Therefore,the detection and punishment of such riders hold great significance in protecting pe...In many non-motor vehicle traffic accidents in China,the main cause of injury or death for drivers is not wearing a helmet.Therefore,the detection and punishment of such riders hold great significance in protecting people's lives and property safety.This paper delves into a deep learning-based method for detecting helmet-wearing on electric vehicles.The approach involves studying and designing an improved YOLOv5 model to identify the violation behavior of not wearing a helmet,including inserting the SE module in the network of the visual attention mechanism into the enhanced backbone network;bidirectional feature fusion is significantly enhanced by substituting the concat module with the Bidirectional Feature Pyramid Network(BiFPN)module,and adding receptive field attention Convolution(RFAConv)to the detection head.The improved YOLOv5 model demonstrates a higher mean Average Precision(mAP)while achieving a relatively smaller model size.This method provides technical support for the real-time and accurate detection of non-vehicle helmet targets;its efficacy has been confirmed through analysis of experimental results.These findings suggest that this method can assist traffic management departments in supervising non-motor vehicles,carrying significant practical value and importance.展开更多
Transmission line faults pose a significant threat to power system resilience,underscoring the need for accurate and rapid fault identification to facilitate proper resource monitoring,economic loss prevention,and bla...Transmission line faults pose a significant threat to power system resilience,underscoring the need for accurate and rapid fault identification to facilitate proper resource monitoring,economic loss prevention,and blackout avoidance.Extreme learning machine(ELM)offers a compelling solution for rapid classification,achieving network training in a single epoch.Leveraging the Internet of Things(IoT)and the virtual instrumentation capabilities of LabVIEW,ELM can enable the swift and precise identification of transmission line faults.This paper presents a regularized radial basis function(RBF)ELM-based fault detection and classification system for transmission lines,utilizing a LabVIEW based virtual phasor measurement unit(PMU)and IoT sensors.The transmission line fault is identified using the phaselet algorithm applied to the phase current acquired from the virtual PMU.Classification is then performed using the ELM algorithm.The proposed methodology is validated in real-time on a practical transmission line,achieving an accuracy of 99.46%.This has the potential to significantly influence future fault detection strategies incorporating virtual PMUs and machine learning.展开更多
Ethics and governance are vital to the healthy and sustainable development of artificial intelligence(AI).With the long-term goal of keeping AI beneficial to human society,governments,research organizations,and compan...Ethics and governance are vital to the healthy and sustainable development of artificial intelligence(AI).With the long-term goal of keeping AI beneficial to human society,governments,research organizations,and companies in China have published ethical guidelines and principles for AI,and have launched projects to develop AI governance technologies.This paper presents a survey of these efforts and highlights the preliminary outcomes in China.It also describes the major research challenges in AI governance research and discusses future research directions.展开更多
A new scheme of small compact optical frequency standard based on thermal calcium beam with application of 423 nm shelving detection and sharp-angle velocity selection detection is proposed. Combining these presented ...A new scheme of small compact optical frequency standard based on thermal calcium beam with application of 423 nm shelving detection and sharp-angle velocity selection detection is proposed. Combining these presented techniques, we conclude that a small compact optical frequency standard based on thermal calcium beam will outperform the commercial caesium-beam microwave dock, like the 5071 Cs clock （from Hp to Agilent, now Symmetricom company）, both in accuracy and stability.展开更多
Most current researches working on improving stiffness focus on the application of control theories.But controller in closed-loop hydraulic control system takes effect only after the controlled position is deviated,so...Most current researches working on improving stiffness focus on the application of control theories.But controller in closed-loop hydraulic control system takes effect only after the controlled position is deviated,so the control action is lagged.Thus dynamic performance against force disturbance and dynamic load stiffness can’t be improved evidently by advanced control algorithms.In this paper,the elementary principle of maintaining piston position unchanged under sudden external force load change by charging additional oil is analyzed.On this basis,the conception of raising dynamic stiffness of electro hydraulic position servo system by flow feedforward compensation is put forward.And a scheme using double servo valves to realize flow feedforward compensation is presented,in which another fast response servo valve is added to the regular electro hydraulic servo system and specially utilized to compensate the compressed oil volume caused by load impact in time.The two valves are arranged in parallel to control the cylinder jointly.Furthermore,the model of flow compensation is derived,by which the product of the amplitude and width of the valve’s pulse command signal can be calculated.And determination rules of the amplitude and width of pulse signal are concluded by analysis and simulations.Using the proposed scheme,simulations and experiments at different positions with different force changes are conducted.The simulation and experimental results show that the system dynamic performance against load force impact is largely improved with decreased maximal dynamic position deviation and shortened settling time.That is,system dynamic load stiffness is evidently raised.This paper proposes a new method which can effectively improve the dynamic stiffness of electro-hydraulic servo systems.展开更多
Research on chip-scale atomic clocks （CSACs） based on coherent population trapping （CPT） is reviewed. The back- ground and the inspiration for the research are described, including the important schemes proposed t...Research on chip-scale atomic clocks （CSACs） based on coherent population trapping （CPT） is reviewed. The back- ground and the inspiration for the research are described, including the important schemes proposed to improve the CPT signal quality, the selection of atoms and buffer gases, and the development of micro-cell fabrication. With regard to the re- liability, stability, and service life of the CSACs, the research regarding the sensitivity of the CPT resonance to temperature and laser power changes is also reviewed, as well as the CPT resonance＇s collision and light of frequency shifts. The first generation CSACs have already been developed but its characters are still far from our expectations. Our conclusion is that miniaturization and power reduction are the most important aspects calling for further research.展开更多
High resistance thin film chip resistors(0603 type) were studied,and the specifications are as follows:1 k? with tolerance about ±0.1% after laser trimming and temperature coefficient of resistance(TCR) less than...High resistance thin film chip resistors(0603 type) were studied,and the specifications are as follows:1 k? with tolerance about ±0.1% after laser trimming and temperature coefficient of resistance(TCR) less than ±15×10-6/℃.Cr-Si-Ta-Al films were prepared with Ar flow rate and sputtering power fixed at 20 standard-state cubic centimeter per minute(sccm) and 100 W,respectively.The experiment shows that the electrical properties of Cr-SiTa-Al deposition films can meet the specification requirements of 0603 ty...展开更多
Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram(ECG)signals.In a life-threatening situation,an automated system is necessary for early detecti...Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram(ECG)signals.In a life-threatening situation,an automated system is necessary for early detection of beat abnormalities in order to reduce the mortality rate.In this paper,we propose an automatic classification system of ECG beats based on the multi-domain features derived from the ECG signals.The experimental study was evaluated on ECG signals obtained from the MIT-BIH Arrhythmia Database.The feature set comprises eight empirical mode decomposition(EMD)based features,three features from variational mode decomposition(VMD)and four features from RR intervals.In total,15 features are ranked according to a ranker search approach and then used as input to the support vector machine(SVM)and C4.5 decision tree classifiers for classifying six types of arrhythmia beats.The proposed method achieved best result in C4.5 decision tree classifier with an accuracy of 98.89%compared to cubic-SVM classifier which achieved an accuracy of 95.35%only.Besides accuracy measures,all other parameters such as sensitivity(Se),specificity(Sp)and precision rates of 95.68%,99.28%and 95.8%was achieved better in C4.5 classifier.Also the computational time of 0.65 s with an error rate of 0.11 was achieved which is very less compared to SVM.The multi-domain based features with decision tree classifier obtained the best results in classifying cardiac arrhythmias hence the system could be used efficiently in clinical practices.展开更多
Objectives： The study was done to evaluate the efficacy of optical coherence tomography （OCT）, to detect and analyze the microdamage occurring around the microimplant immediately following its placement, and to com...Objectives： The study was done to evaluate the efficacy of optical coherence tomography （OCT）, to detect and analyze the microdamage occurring around the microimplant immediately following its placement, and to compare the findings with micro-computed tomography （IJCT） images of the samples to validate the result of the present study. Methods： Microimplants were inserted into bovine bone samples. Images of the samples were obtained using OCT and μCT. Visual comparisons of the images were made to evaluate whether anatomical details and microdamage induced by microimplant insertion were accurately revealed by OCT. Results： The surface of the cortical bone with its anatomical variations is visualized on the OCT images. Microdamage occurring on the surface of the cortical bone around the microimplant can be appreciated in OCT images. The resulting OCT images were compared with the μCT images. A high correlation regarding the visualization of individual microcracks was observed. The depth penetration of OCT is limited when compared to μCT. Conclusions： OCT in the present study was able to generate high-resolution images of the microdamage occurring around the microimplant. Image quality at the surface of the cortical bone is above par when compared with μCT imaging, because of the inherent high contrast and high-resolution quality of OCT systems. Improvements in the imaging depth and development of intraoral sensors are vital for developing a real-time imaging system and integrating the system into orthodontic practice.展开更多
Abstract： The layered decoding algorithm has been widely used in the implementation of Low Density Parity Check （LDPC） decoders, due to its high convergence speed. However, the pipeline operation of the layered dec...Abstract： The layered decoding algorithm has been widely used in the implementation of Low Density Parity Check （LDPC） decoders, due to its high convergence speed. However, the pipeline operation of the layered decoder may introduce memory access conflicts, which heavily deteriorates the decoder throughput. To essentially deal with the issue of memory access conflicts,展开更多
Cd_(1-x)Zn_(x)S thin films were deposited by chemical bath deposition(CBD)on the glass substrate to study the influence of cadmium sulfate concentration on the structural characteristics of the thin film.The SEM resul...Cd_(1-x)Zn_(x)S thin films were deposited by chemical bath deposition(CBD)on the glass substrate to study the influence of cadmium sulfate concentration on the structural characteristics of the thin film.The SEM results show that the thin film surfaces under the cadmium sulfate concentration of 0.005 M exhibit better compactness and uniformity.The distribution diagrams of thin film elements illustrate the film growth rate changes on the trend of the increase,decrease,and increase with the increase of cadmium sulfate concentration.XRD studies exhibit the crystal structure of the film is the hexagonal phase,and there are obvious diffraction peaks and better crystallinity when the concentration is 0.005 M.Spectrophotometer test results demonstrate that the relationship between zinc content x and optical band gap value E_(g) can be expressed by the equation E_(g)(x)=0.59x^(2)+0.69x+2.43.Increasing the zinc content can increase the optical band gap,and the absorbance of the thin film can be improved by decreasing the cadmium sulfate concentration,however,all of them have good transmittance.At a concentration of 0.005 M,the thin film has good absorbance in the 300-800 nm range,80%transmittance,and band gap value of 3.24 eV,which is suitable for use as a buffer layer for solar cells.展开更多
The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle （LV） from multi-slice cine short axis cardiac magnetic reso...The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle （LV） from multi-slice cine short axis cardiac magnetic resonance （MR） images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting （LGDF） energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract （LVOT） and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions （MICCAI） 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.9781 and 0.9567, for LV mass these values are 0.9249 and 0.8554. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method.展开更多
文摘This paper presents an innovative and effective control strategy tailored for a deregulated,diversified energy system involving multiple interconnected area.Each area integrates a unique mix of power generation technologies:Area 1 combines thermal,hydro,and distributed generation;Area 2 utilizes a blend of thermal units,distributed solar technologies(DST),and hydro power;andThird control area hosts geothermal power station alongside thermal power generation unit and hydropower units.The suggested control system employs a multi-layered approach,featuring a blended methodology utilizing the Tilted Integral Derivative controller(TID)and the Fractional-Order Integral method to enhance performance and stability.The parameters of this hybrid TID-FOI controller are finely tuned using an advanced optimization method known as the Walrus Optimization Algorithm(WaOA).Performance analysis reveals that the combined TID-FOI controller significantly outperforms the TID and PID controllers when comparing their dynamic response across various system configurations.The study also incorporates investigation of redox flow batteries within the broader scope of energy storage applications to assess their impact on system performance.In addition,the research explores the controller’s effectiveness under different power exchange scenarios in a deregulated market,accounting for restrictions on generation ramp rates and governor hysteresis effects in dynamic control.To ensure the reliability and resilience of the presented methodology,the system transitions and develops across a broad range of varying parameters and stochastic load fluctuation.To wrap up,the study offers a pioneering control approach-a hybrid TID-FOI controller optimized via the Walrus Optimization Algorithm(WaOA)-designed for enhanced stability and performance in a complex,three-region hybrid energy system functioning within a deregulated framework.
文摘The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.The overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.
文摘The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial antennas.The radiation cost and quality factor of the antenna are influenced by the size of the antenna.Metamaterial antennas allow for the circumvention of the bandwidth restriction for small antennas.Antenna parameters have recently been predicted using machine learning algorithms in existing literature.Machine learning can take the place of the manual process of experimenting to find the ideal simulated antenna parameters.The accuracy of the prediction will be primarily dependent on the model that is used.In this paper,a novel method for forecasting the bandwidth of the metamaterial antenna is proposed,based on using the Pearson Kernel as a standard kernel.Along with these new approaches,this paper suggests a unique hypersphere-based normalization to normalize the values of the dataset attributes and a dimensionality reduction method based on the Pearson kernel to reduce the dimension.A novel algorithm for optimizing the parameters of Convolutional Neural Network(CNN)based on improved Bat Algorithm-based Optimization with Pearson Mutation(BAO-PM)is also presented in this work.The prediction results of the proposed work are better when compared to the existing models in the literature.
基金supported by the National Research Founda-tion of Korea(NRF)grants funded by the Ministry of Science and ICT(MSIT)(Nos.RS-2023-00251283,RS-2023-00257003,and 2022M3D1A2083618)supported by the DGIST R&D Program of the MSIT(No.23-CoE-BT-03).
文摘We have realized efficient photopatterning and high-quality ZrO_(2)films through combustion synthesis and manufactured resistive random access memory(RRAM)devices with excellent switching stability at low temperatures(250℃)using these approaches.Combustion synthesis reduces the energy required for oxide conversion,thus accelerating the decomposition of organic ligands in the UV-exposed area,and promoting the formation of metal-oxygen networks,contributing to patterning.Thermal analysis confirmed a reduction in the conversion temperature of combustion precursors,and the prepared combustion ZrO_(2)films exhibited a high proportion of metal-oxygen bonding that constitutes the oxide lattice,along with an amorphous phase.Furthermore,the synergistic effect of combustion synthesis and UV/O_(3)-assisted photochemical activation resulted in patterned ZrO_(2)films forming even more complete metal-oxygen networks.RRAM devices fabricated with patterned ZrO_(2)films using combustion synthesis exhibited excellent switching characteristics,including a narrow resistance distribution,endurance of 103 cycles,and retention for 105 s at 85℃,despite low-temperature annealing.Combustion synthesis not only enables the formation of high-quality metal oxide films with low external energy but also facilitates improved photopatterning.
基金in part by the National Natural Science Foundation of China under Grants 62271079,61875239,62127802in part by the Fundamental Research Funds for the Central Universities under Grant 2023PY01+1 种基金in part by the National Key Research and Development Program of China under Grant 2018YFB2200903in part by the Beijing Nova Program with Grant Number Z211100002121138.
文摘Efficient optical network management poses significant importance in backhaul and access network communicationfor preventing service disruptions and ensuring Quality of Service(QoS)satisfaction.The emerging faultsin optical networks introduce challenges that can jeopardize the network with a variety of faults.The existingliterature witnessed various partial or inadequate solutions.On the other hand,Machine Learning(ML)hasrevolutionized as a promising technique for fault detection and prevention.Unlike traditional fault managementsystems,this research has three-fold contributions.First,this research leverages the ML and Deep Learning(DL)multi-classification system and evaluates their accuracy in detecting six distinct fault types,including fiber cut,fibereavesdropping,splicing,bad connector,bending,and PC connector.Secondly,this paper assesses the classificationdelay of each classification algorithm.Finally,this work proposes a fiber optics fault prevention algorithm thatdetermines to mitigate the faults accordingly.This work utilized a publicly available fiber optics dataset namedOTDR_Data and applied different ML classifiers,such as Gaussian Naive Bayes(GNB),Logistic Regression(LR),Support Vector Machine(SVM),K-Nearest Neighbor(KNN),Random Forest(RF),and Decision Tree(DT).Moreover,Ensemble Learning(EL)techniques are applied to evaluate the accuracy of various classifiers.In addition,this work evaluated the performance of DL-based Convolutional Neural Network and Long-Short Term Memory(CNN-LSTM)hybrid classifier.The findings reveal that the CNN-LSTM hybrid technique achieved the highestaccuracy of 99%with a delay of 360 s.On the other hand,EL techniques improved the accuracy in detecting fiberoptic faults.Thus,this research comprehensively assesses accuracy and delay metrics for various classifiers andproposes the most efficient attack detection system in fiber optics.
基金This paper is financed by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.004-0001-C01.
文摘The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.
文摘Pyrolysis is a rapidly expanding chemical-based recyclable method that complements physical recycling. It avoids improper disposal of post-consumer polymers and mitigates the ecological problems linked to the production of new plastic. Nevertheless, while there is a consensus that pyrolysis might be a crucial technology in the years to come, more discussions are needed to address the challenges related to scaling up, the long-term sustainability of the process, and additional variables essential to the advancement of the green economy. Herein, it emphasizes knowledge gaps and methodological issues in current Life Cycle Assessment (LCA), underlining the need for standardized techniques and updated data to support robust decision-making for adopting pyrolysis technologies in waste management strategies. For this purpose, this study reviews the LCAs of pyrolytic processes, encompassing the complete life cycle, from feedstock collection to end-product distribution, including elements such as energy consumption, greenhouse gas emissions, and waste creation. Hence, we evaluate diverse pyrolysis processes, including slow, rapid, and catalytic pyrolysis, emphasizing their distinct efficiency and environmental footprints. Furthermore, we evaluate the impact of feedstock composition, process parameters, and scale of operation on the overall sustainability of pyrolysis-based plastic waste treatment by integrating results from current literature and identifying essential research needs. Therefore, this paper argues that existing LCA studies need more coherence and accuracy. It follows a thorough evaluation of previous research and suggests new insights into methodologies and restrictions.
基金funded by the Jiangsu Province Postgraduate Scientific Research and Practice Innovation Program(SJCX240449)projectthe Nanjing University of Information Science and Technology Talent Startup Fund(2022r078).
文摘Camera Pose Estimating from point and line correspondences is critical in various applications,including robotics,augmented reality,3D reconstruction,and autonomous navigation.Existing methods,such as the Perspective-n-Point(PnP)and Perspective-n-Line(PnL)approaches,offer limited accuracy and robustness in environments with occlusions,noise,or sparse feature data.This paper presents a unified solution,Efficient and Accurate Pose Estimation from Point and Line Correspondences(EAPnPL),combining point-based and linebased constraints to improve pose estimation accuracy and computational efficiency,particularly in low-altitude UAV navigation and obstacle avoidance.The proposed method utilizes quaternion parameterization of the rotation matrix to overcome singularity issues and address challenges in traditional rotation matrix-based formulations.A hybrid optimization framework is developed to integrate both point and line constraints,providing a more robust and stable solution in complex scenarios.The method is evaluated using synthetic and realworld datasets,demonstrating significant improvements in performance over existing techniques.The results indicate that the EAPnPL method enhances accuracy and reduces computational complexity,making it suitable for real-time applications in autonomous UAV systems.This approach offers a promising solution to the limitations of existing camera pose estimation methods,with potential applications in low-altitude navigation,autonomous robotics,and 3D scene reconstruction.
文摘In many non-motor vehicle traffic accidents in China,the main cause of injury or death for drivers is not wearing a helmet.Therefore,the detection and punishment of such riders hold great significance in protecting people's lives and property safety.This paper delves into a deep learning-based method for detecting helmet-wearing on electric vehicles.The approach involves studying and designing an improved YOLOv5 model to identify the violation behavior of not wearing a helmet,including inserting the SE module in the network of the visual attention mechanism into the enhanced backbone network;bidirectional feature fusion is significantly enhanced by substituting the concat module with the Bidirectional Feature Pyramid Network(BiFPN)module,and adding receptive field attention Convolution(RFAConv)to the detection head.The improved YOLOv5 model demonstrates a higher mean Average Precision(mAP)while achieving a relatively smaller model size.This method provides technical support for the real-time and accurate detection of non-vehicle helmet targets;its efficacy has been confirmed through analysis of experimental results.These findings suggest that this method can assist traffic management departments in supervising non-motor vehicles,carrying significant practical value and importance.
基金supported by the Experimental-Demonstration project PN-IV-P7-7.1-PED-2024-0567(Improving the Fuel Cell Hybrid Electric Vehicle Drivetrain by Implementing a Novel Optimal Real-Time Power Management Strategy),contract no.58PED(N.B.)the APC was funded by S.C.ECAI CONFERENCE S.R.Lsupported by Science and Engineering Research Board,India with grant number ECR/2017/000812.
文摘Transmission line faults pose a significant threat to power system resilience,underscoring the need for accurate and rapid fault identification to facilitate proper resource monitoring,economic loss prevention,and blackout avoidance.Extreme learning machine(ELM)offers a compelling solution for rapid classification,achieving network training in a single epoch.Leveraging the Internet of Things(IoT)and the virtual instrumentation capabilities of LabVIEW,ELM can enable the swift and precise identification of transmission line faults.This paper presents a regularized radial basis function(RBF)ELM-based fault detection and classification system for transmission lines,utilizing a LabVIEW based virtual phasor measurement unit(PMU)and IoT sensors.The transmission line fault is identified using the phaselet algorithm applied to the phase current acquired from the virtual PMU.Classification is then performed using the ELM algorithm.The proposed methodology is validated in real-time on a practical transmission line,achieving an accuracy of 99.46%.This has the potential to significantly influence future fault detection strategies incorporating virtual PMUs and machine learning.
文摘Ethics and governance are vital to the healthy and sustainable development of artificial intelligence(AI).With the long-term goal of keeping AI beneficial to human society,governments,research organizations,and companies in China have published ethical guidelines and principles for AI,and have launched projects to develop AI governance technologies.This paper presents a survey of these efforts and highlights the preliminary outcomes in China.It also describes the major research challenges in AI governance research and discusses future research directions.
基金Supported by the National Key Basic Research and Development Programme of China under Grant No 2005CB724500, and the National Natural Science Foundation of China under Grant Nos 60178016 and 10104002.
文摘A new scheme of small compact optical frequency standard based on thermal calcium beam with application of 423 nm shelving detection and sharp-angle velocity selection detection is proposed. Combining these presented techniques, we conclude that a small compact optical frequency standard based on thermal calcium beam will outperform the commercial caesium-beam microwave dock, like the 5071 Cs clock （from Hp to Agilent, now Symmetricom company）, both in accuracy and stability.
基金supported by National Natural Science Foundation of China(Grant No.51075291)Shanxi Scholarship Council of China(Grant No.2012-076)
文摘Most current researches working on improving stiffness focus on the application of control theories.But controller in closed-loop hydraulic control system takes effect only after the controlled position is deviated,so the control action is lagged.Thus dynamic performance against force disturbance and dynamic load stiffness can’t be improved evidently by advanced control algorithms.In this paper,the elementary principle of maintaining piston position unchanged under sudden external force load change by charging additional oil is analyzed.On this basis,the conception of raising dynamic stiffness of electro hydraulic position servo system by flow feedforward compensation is put forward.And a scheme using double servo valves to realize flow feedforward compensation is presented,in which another fast response servo valve is added to the regular electro hydraulic servo system and specially utilized to compensate the compressed oil volume caused by load impact in time.The two valves are arranged in parallel to control the cylinder jointly.Furthermore,the model of flow compensation is derived,by which the product of the amplitude and width of the valve’s pulse command signal can be calculated.And determination rules of the amplitude and width of pulse signal are concluded by analysis and simulations.Using the proposed scheme,simulations and experiments at different positions with different force changes are conducted.The simulation and experimental results show that the system dynamic performance against load force impact is largely improved with decreased maximal dynamic position deviation and shortened settling time.That is,system dynamic load stiffness is evidently raised.This paper proposes a new method which can effectively improve the dynamic stiffness of electro-hydraulic servo systems.
基金Project support by the National Natural Science Foundation of China(Grant No.11074012)
文摘Research on chip-scale atomic clocks （CSACs） based on coherent population trapping （CPT） is reviewed. The back- ground and the inspiration for the research are described, including the important schemes proposed to improve the CPT signal quality, the selection of atoms and buffer gases, and the development of micro-cell fabrication. With regard to the re- liability, stability, and service life of the CSACs, the research regarding the sensitivity of the CPT resonance to temperature and laser power changes is also reviewed, as well as the CPT resonance＇s collision and light of frequency shifts. The first generation CSACs have already been developed but its characters are still far from our expectations. Our conclusion is that miniaturization and power reduction are the most important aspects calling for further research.
基金Supported by Science and Technology Committee of Tianjin (No.06YFGPGX08400)Ministry of Science and Technology of China (No.2009GJF20022)Innovation Fund of Tianjin University
文摘High resistance thin film chip resistors(0603 type) were studied,and the specifications are as follows:1 k? with tolerance about ±0.1% after laser trimming and temperature coefficient of resistance(TCR) less than ±15×10-6/℃.Cr-Si-Ta-Al films were prepared with Ar flow rate and sputtering power fixed at 20 standard-state cubic centimeter per minute(sccm) and 100 W,respectively.The experiment shows that the electrical properties of Cr-SiTa-Al deposition films can meet the specification requirements of 0603 ty...
文摘Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram(ECG)signals.In a life-threatening situation,an automated system is necessary for early detection of beat abnormalities in order to reduce the mortality rate.In this paper,we propose an automatic classification system of ECG beats based on the multi-domain features derived from the ECG signals.The experimental study was evaluated on ECG signals obtained from the MIT-BIH Arrhythmia Database.The feature set comprises eight empirical mode decomposition(EMD)based features,three features from variational mode decomposition(VMD)and four features from RR intervals.In total,15 features are ranked according to a ranker search approach and then used as input to the support vector machine(SVM)and C4.5 decision tree classifiers for classifying six types of arrhythmia beats.The proposed method achieved best result in C4.5 decision tree classifier with an accuracy of 98.89%compared to cubic-SVM classifier which achieved an accuracy of 95.35%only.Besides accuracy measures,all other parameters such as sensitivity(Se),specificity(Sp)and precision rates of 95.68%,99.28%and 95.8%was achieved better in C4.5 classifier.Also the computational time of 0.65 s with an error rate of 0.11 was achieved which is very less compared to SVM.The multi-domain based features with decision tree classifier obtained the best results in classifying cardiac arrhythmias hence the system could be used efficiently in clinical practices.
基金Project supported by the BK21 Plus Project Funded by the Ministry of Education,Korea(No.21A20131600011)the Industrial Infrastructure Program of Laser Industry Support Funded by the Ministry of Trade,Industry & Energy,Korea(No.N0000598)
文摘Objectives： The study was done to evaluate the efficacy of optical coherence tomography （OCT）, to detect and analyze the microdamage occurring around the microimplant immediately following its placement, and to compare the findings with micro-computed tomography （IJCT） images of the samples to validate the result of the present study. Methods： Microimplants were inserted into bovine bone samples. Images of the samples were obtained using OCT and μCT. Visual comparisons of the images were made to evaluate whether anatomical details and microdamage induced by microimplant insertion were accurately revealed by OCT. Results： The surface of the cortical bone with its anatomical variations is visualized on the OCT images. Microdamage occurring on the surface of the cortical bone around the microimplant can be appreciated in OCT images. The resulting OCT images were compared with the μCT images. A high correlation regarding the visualization of individual microcracks was observed. The depth penetration of OCT is limited when compared to μCT. Conclusions： OCT in the present study was able to generate high-resolution images of the microdamage occurring around the microimplant. Image quality at the surface of the cortical bone is above par when compared with μCT imaging, because of the inherent high contrast and high-resolution quality of OCT systems. Improvements in the imaging depth and development of intraoral sensors are vital for developing a real-time imaging system and integrating the system into orthodontic practice.
基金the National Natural Science Foundation of China，the National Key Basic Research Program of China，The authors would like to thank all project partners for their valuable contributions and feedbacks
文摘Abstract： The layered decoding algorithm has been widely used in the implementation of Low Density Parity Check （LDPC） decoders, due to its high convergence speed. However, the pipeline operation of the layered decoder may introduce memory access conflicts, which heavily deteriorates the decoder throughput. To essentially deal with the issue of memory access conflicts,
基金This work was supported by the Tianjin Municipal Education Commission,Horizontal subject(grant number 70304901).
文摘Cd_(1-x)Zn_(x)S thin films were deposited by chemical bath deposition(CBD)on the glass substrate to study the influence of cadmium sulfate concentration on the structural characteristics of the thin film.The SEM results show that the thin film surfaces under the cadmium sulfate concentration of 0.005 M exhibit better compactness and uniformity.The distribution diagrams of thin film elements illustrate the film growth rate changes on the trend of the increase,decrease,and increase with the increase of cadmium sulfate concentration.XRD studies exhibit the crystal structure of the film is the hexagonal phase,and there are obvious diffraction peaks and better crystallinity when the concentration is 0.005 M.Spectrophotometer test results demonstrate that the relationship between zinc content x and optical band gap value E_(g) can be expressed by the equation E_(g)(x)=0.59x^(2)+0.69x+2.43.Increasing the zinc content can increase the optical band gap,and the absorbance of the thin film can be improved by decreasing the cadmium sulfate concentration,however,all of them have good transmittance.At a concentration of 0.005 M,the thin film has good absorbance in the 300-800 nm range,80%transmittance,and band gap value of 3.24 eV,which is suitable for use as a buffer layer for solar cells.
基金supported by Department of Science and Technology, Ministry of Science and Technology, India (No. DST/TSG/ICT/2010/08)
文摘The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle （LV） from multi-slice cine short axis cardiac magnetic resonance （MR） images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting （LGDF） energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract （LVOT） and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions （MICCAI） 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.9781 and 0.9567, for LV mass these values are 0.9249 and 0.8554. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method.
