On the basis of the description of the rare-earth countercurrent extraction process, the on-line detecting method and equipments of rare-earth elements and the application in the process of the rare-earth countercurre...On the basis of the description of the rare-earth countercurrent extraction process, the on-line detecting method and equipments of rare-earth elements and the application in the process of the rare-earth countercurrent extraction are summarized. The procedure simulation of the computer, the automation control method and its current application are also mentioned in the process of rare-earth countercurrent extraction. The method of soft sensor is proposed. Optimal control method based on object-oriented rare-earth countercurrent extraction process and integrated automation system composed of process management system and process control system are presented, which are the developing direction of the automation of rare-earth countercurrent extraction process.展开更多
With the growth of Vehicular Ad-hoc Networks,many services delivery is gaining more attention from the intelligent transportation system.However,mobility characteristics of vehicular networks cause frequent disconnect...With the growth of Vehicular Ad-hoc Networks,many services delivery is gaining more attention from the intelligent transportation system.However,mobility characteristics of vehicular networks cause frequent disconnection of routes,especially during the delivery of data.In both developed and developing countries,a lot of time is consumed due to traffic congestion.This has significant negative consequences,including driver stress due to increased time demand,decreased productivity for various personalized and commercial vehicles,and increased emissions of hazardous gases especially air polluting gases are impacting public health in highly populated areas.Clustering is one of the most powerful strategies for achieving a consistent topological structure.Two algorithms are presented in this research work.First,a k-means clustering algorithm in which dynamic grouping by k-implies is performed that fits well with Vehicular network’s dynamic topology characteristics.The suggested clustering reduces overhead and traffic management.Second,for inter and intra-clustering routing,the dynamic routing protocol is proposed,which increases the overall Packet Delivery Ratio and decreases the End-to-End latency.Relative to the cluster-based approach,the proposed protocol achieves improved efficiency in terms of Throughput,Packet Delivery Ratio,and End-to-End delay parameters comparing the situations by taking different number of vehicular nodes in the network.展开更多
With the acceleration of the social information process,information awareness and information skills have become the basic qualities of every citizen.The establishment of the training mechanism for scientific and tech...With the acceleration of the social information process,information awareness and information skills have become the basic qualities of every citizen.The establishment of the training mechanism for scientific and technological innovation talents from the beginning of higher education is insufficient to meet the needs of the development of the times.It is imperative to improve the training of information technology innovation talents and explore a new training model.This paper describes the general situation of the development of education in the field of information technology from a domestic and international perspective.It then analyzes the existing problems,explores new exploration models and implementation suggestions,and puts forward prospects at the end of the paper.展开更多
Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the err...Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the error compensation model of fuzzy system,is proposed to solve the prob- lem that the component content in countercurrent rare-earth extraction process is hardly measured on-line.An industry experiment in the extraction Y process by HAB using this hybrid soft-sensor proves its effectiveness.展开更多
CMOS technology is one of the most frequently used technologies in the semiconductor industry as it can be successfully integrated with ICs.Every two years the number of MOS transistors doubles because the size of the...CMOS technology is one of the most frequently used technologies in the semiconductor industry as it can be successfully integrated with ICs.Every two years the number of MOS transistors doubles because the size of the MOSFET is reduced.Reducing the size of the MOSFET reduces the size of the channel length which causes short channel effects and it increases the leakage current.To reduce the short channel effects new designs and technologies are implemented.Double gate MOSFET design has shown improvement in performance as amplifiers over a single MOSFET.Silicon-based MOSFET design can be used in a harsh environment.It has been used in various applications such as in detecting biomolecules.The increase in number of gates increases the current drive capability of transistors.GAA MOSFET is an example of a quadruple gate around the four sides of channel that increases gate control over the channel region.It also increases effective channel width that improves drain current and reduces leakage current keeping short channel effects under limit.Junctionless MOSFET operates faster and uses less power with increase in ON-state current leading to a good value of ION/IOFF ratio.In this paper,several gate and channel engineered MOSFET structures are analyzed and compared for sub 45 nm technology node.A comparison among different MOSFET structures has been made for subthreshold performance parameters in terms of IOFF,subthreshold slope and DIBL values.The analog/RF performance is analyzed for transconductance,effective transistor capacitances,stability factor and critical frequency.The paper also covers different applications of advance MOSFET structures in analog/digital or IoT/biomedical applications.展开更多
Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation(5G)networks.However,it leads to performance degradation and huge...Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation(5G)networks.However,it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand.To meet these access conditions and improve Quality of Service,resource allocation(RA)should be carefully optimized.Traditionally,RA problems are nonconvex optimizations,which are performed using heuristic methods,such as genetic algorithm,particle swarm optimization,and simulated annealing.However,the application of these approaches remains computationally expensive and unattractive for dense cellular networks.Therefore,artificial intelligence algorithms are used to improve traditional RA mechanisms.Deep learning is a promising tool for addressing resource management problems in wireless communication.In this study,we investigate a double deep Q-network-based RA framework that maximizes energy efficiency(EE)and total network throughput in unmanned aerial vehicle(UAV)-assisted terrestrial networks.Specifically,the system is studied under the constraints of interference.However,the optimization problem is formulated as a mixed integer nonlinear program.Within this framework,we evaluated the effect of height and the number of UAVs on EE and throughput.Then,in accordance with the experimental results,we compare the proposed algorithm with several artificial intelligence methods.Simulation results indicate that the proposed approach can increase EE with a considerable throughput.展开更多
Fault detection of the photovoltaic(PV)grid is necessary to detect serious output power reduction to avoid PV modules’damage.To identify the fault of the PV arrays,there is a necessity to implement an automatic syste...Fault detection of the photovoltaic(PV)grid is necessary to detect serious output power reduction to avoid PV modules’damage.To identify the fault of the PV arrays,there is a necessity to implement an automatic system.In this IoT and LabVIEW-based automatic fault detection of 3×3 solar array,a PV system is proposed to control and monitor Internet connectivity remotely.Hardware component to automatically reconfigure the solar PV array from the series-parallel(SP)to the complete cross-linked array underneath partial shading conditions(PSC)is centered on the Atmega328 system to achieve maximum power.In the LabVIEW environment,an automated monitoring system is developed.The automatic monitoring system assesses the voltage drop losses present in the DC side of the PV generator and generates a decimal weighted value depending on the defective solar panels and transmits this value to the remote station through an RF modem,and provides an indicator of the faulty solar panel over the built-in Interface LabVIEW.The managing of this GUI indicator helps the monitoring system to generate a panel alert for damaged panels in the PV system.Node MCU in the receiver section enables transmission of the fault status of PV arrays via Internet connectivity.The IoT-based Blynk app is employed for visualizing the fault status of the 3×3 PV array.The dashboard of Blynk visualizes every array with the status.展开更多
Outcome based Education(OBE)emphasizes“student-centered,output-oriented”continuous improvement to meet the needs of students to acquire knowledge and develop abilities.The result orientation of the existing“Sensor ...Outcome based Education(OBE)emphasizes“student-centered,output-oriented”continuous improvement to meet the needs of students to acquire knowledge and develop abilities.The result orientation of the existing“Sensor principle and application”course teaching reform emphasizes the understanding and application of sensor principle,mastering the principle of language rules,object-oriented programming method and thinking mode.The teaching reform of this course is based on the concept of OBE,focusing on the combination of sensor principles and students’majors,and guiding students to apply object-oriented development tools reasonably and efficiently in related professional engineering.Comprehensive projects should be introduced into practical teaching to appropriately increase the workload and difficulty of practical teaching and cultivate students’awareness and ability of teamwork.Solve the“pain points”and“blocking points”existing in the curriculum,so as to combine the curriculum with talent training.The results of practical application show that the students’engineering application ability is improved and they can complete the comprehensive practical project well.展开更多
Accurate and reliable fault detection is essential for the safe operation of electric vehicles.Support vector data description(SVDD)has been widely used in the field of fault detection.However,constructing the hypersp...Accurate and reliable fault detection is essential for the safe operation of electric vehicles.Support vector data description(SVDD)has been widely used in the field of fault detection.However,constructing the hypersphere boundary only describes the distribution of unlabeled samples,while the distribution of faulty samples cannot be effectively described and easilymisses detecting faulty data due to the imbalance of sample distribution.Meanwhile,selecting parameters is critical to the detection performance,and empirical parameterization is generally timeconsuming and laborious and may not result in finding the optimal parameters.Therefore,this paper proposes a semi-supervised data-driven method based on which the SVDD algorithm is improved and achieves excellent fault detection performance.By incorporating faulty samples into the underlying SVDD model,training deals better with the problem of missing detection of faulty samples caused by the imbalance in the distribution of abnormal samples,and the hypersphere boundary ismodified to classify the samplesmore accurately.The Bayesian Optimization NSVDD(BO-NSVDD)model was constructed to quickly and accurately optimize hyperparameter combinations.In the experiments,electric vehicle operation data with four common fault types are used to evaluate the performance with other five models,and the results show that the BO-NSVDD model presents superior detection performance for each type of fault data,especially in the imperceptible early and minor faults,which has seen very obvious advantages.Finally,the strong robustness of the proposed method is verified by adding different intensities of noise in the dataset.展开更多
Reestablishment in power system brings in significant transformation in the power sector by extinguishing the possession of sound consolidated assistance.However,the collaboration of various manufacturing agencies,aut...Reestablishment in power system brings in significant transformation in the power sector by extinguishing the possession of sound consolidated assistance.However,the collaboration of various manufacturing agencies,autonomous power manufacturers,and buyers have created complex installation processes.The regular active load and inefficiency of best measures among varied associates is a huge hazard.Any sudden load deviation will give rise to immediate amendment in frequency and tie-line power errors.It is essential to deal with every zone’s frequency and tie-line power within permitted confines followed by fluctuations within the load.Therefore,it can be proficient by implementing Load Frequency Control under the Bilateral case,stabilizing the power and frequency distinction within the interrelated power grid.Balancing the net deviation in multiple areas is possible by minimizing the unbalance of Bilateral Contracts with the help of proportional integral and advanced controllers like Harris Hawks Optimizer.We proposed the advanced controller Harris Hawk optimizer-based model and validated it on a test bench.The experiment results show that the delay time is 0.0029 s and the settling time of 20.86 s only.This model can also be leveraged to examine the decision boundaries of the Bilateral case.展开更多
Detection and tracking of all major parts of pig body could be more productive to help to analyze pig behavior.To achieve this goal,a real-time algorithm based on You Only Look At CoefficienTs(YOLACT)was proposed.A pi...Detection and tracking of all major parts of pig body could be more productive to help to analyze pig behavior.To achieve this goal,a real-time algorithm based on You Only Look At CoefficienTs(YOLACT)was proposed.A pig body was divided into ten parts:one head,one trunk,four thighs and four shanks.And the key points of each part were calculated by the novel algorithm,which was based mainly on combination of the Zhang-Suen thinning algorithm and Gravity algorithm.The experiment results showed that these parts of pig body could be detected and tracked,and their contributions to overall pig activity could also be sought out.The detect accuracy of the algorithm in the data set could reach up to 90%,and the processing speed to 30.5 fps.Furthermore,the algorithm was robust and adaptive.展开更多
Lower automation level in industrial rare-earth extraction processes results in high production cost, inconsistent product quality and great consumption of resources in China. An integrated automation system for extr...Lower automation level in industrial rare-earth extraction processes results in high production cost, inconsistent product quality and great consumption of resources in China. An integrated automation system for extraction process of rare earth is proposed to realize optimal product indices, such as product purity,recycle rate and output. The optimal control strategy for output component, structure and function of the two-gradcd integrated automation system composed of the process management grade and the process control grade were discussed. This system is successfully applied to a HAB yttrium extraction production process and was found to provide optimal control, optimal operation, optimal management and remarkable benefits.展开更多
Human activity recognition is a recent area of research for researchers.Activity recognition has many applications in smart homes to observe and track toddlers or oldsters for their safety,monitor indoor and outdoor a...Human activity recognition is a recent area of research for researchers.Activity recognition has many applications in smart homes to observe and track toddlers or oldsters for their safety,monitor indoor and outdoor activities,develop Tele immersion systems,or detect abnormal activity recognition.Three dimensions(3D)skeleton data is robust and somehow view-invariant.Due to this,it is one of the popular choices for human action recognition.This paper proposed using a transversal tree from 3D skeleton data to represent videos in a sequence.Further proposed two neural networks:convolutional neural network recurrent neural network_1(CNN_RNN_1),used to find the optimal features and convolutional neural network recurrent neural network network_2(CNN_RNN_2),used to classify actions.The deep neural network-based model proposed CNN_RNN_1 and CNN_RNN_2 that uses a convolutional neural network(CNN),Long short-term memory(LSTM)and Bidirectional Long shortterm memory(BiLSTM)layered.The systemefficiently achieves the desired accuracy over state-of-the-art models,i.e.,88.89%.The performance of the proposed model compared with the existing state-of-the-art models.The NTURGB+D dataset uses for analyzing experimental results.It is one of the large benchmark datasets for human activity recognition.Moreover,the comparison results show that the proposed model outperformed the state-ofthe-art models.展开更多
Enhancing the upconversion luminescence of rare earth ions is crucial for their applications in the laser sources,fiber optic communications,color displays,biolabeling,and biomedical sensors.In this paper,we theoretic...Enhancing the upconversion luminescence of rare earth ions is crucial for their applications in the laser sources,fiber optic communications,color displays,biolabeling,and biomedical sensors.In this paper,we theoretically study the resonance-mediated(1+2)-three-photon absorption in Pr^(3+) ions by a rectangle phase modulation.The results show that the resonance-mediated(1+2)-three-photon absorption can be effectively enhanced by properly designing the depth and width of the rectangle phase modulation,which can be attributed to the constructive interference between on-resonant and near-resonant three-photon excitation pathways.Further,the enhancement efficiency of resonance-mediated(1+2)-threephoton absorption can be affected by the pulse width(or spectral bandwidth)of femtosecond laser field,final state transition frequency,and absorption bandwidths.This research can provide a clear physical picture for understanding and controlling the multi-photon absorption in rare-earth ions,and also can provide theoretical guidance for improving the up-conversion luminescence.展开更多
Many extensive UAV communication networks have used UAV cooperative control.Wireless networking services can be offered using unmanned aerial vehicles(UAVs)as aerial base stations.Not only is coverage maximization,but...Many extensive UAV communication networks have used UAV cooperative control.Wireless networking services can be offered using unmanned aerial vehicles(UAVs)as aerial base stations.Not only is coverage maximization,but also better connectivity,a fundamental design challenge that must be solved.The number of applications for unmanned aerial vehicles(UAVs)operating in unlicensed bands is fast expanding as the Internet of Things(IoT)develops.Those bands,however,have become overcrowded as the number of systems that use them grows.Cognitive Radio(CR)and spectrum allocation approaches have emerged as a potential approach for resolving spectrum scarcity in wireless networks,and hence as technological solutions for future generations,from this perspective.As a result,combining CR with UAVs has the potential to give significant benefits for large-scale UAV deployment.The paper examines existing research on the subject of UAV covering and proposes a multi-UAV cognitive-based error-free model for energy-efficient communication.Coverage maximization,power control,and enhanced connection quality are the three steps of the proposed model.To satisfy the desired signal-to-noise ratio,the covering zone efficacy is investigated as a function of the distance among UAVs stationed in a specific geographic region depending on multiple deployment configurations like as rural,suburban,and urban macro deployment scenarios of the ITU-R M.2135 standard(SNR).展开更多
Considering the exponential growth of wireless devices with datastarving applications fused with artificial intelligence,the significance of wireless network scalability using distributed behavior and fairness among u...Considering the exponential growth of wireless devices with datastarving applications fused with artificial intelligence,the significance of wireless network scalability using distributed behavior and fairness among users is a crucial feature in guaranteeing reliable service to numerous users in the network environment.TheKuramoto model is described as nonlinear selfsustained phase oscillators spinning at varying intrinsic frequencies connected through the sine of their phase differences and displays a phase transition at a specific coupling strength,in which a mutual behavior is accomplished.In this work,we apply the Kuramoto model to achieve a weighted fair resource allocation in a wireless network,where each user has different quality of service(QoS)requirements.Because the original Kuramoto model is the synchronization model,we propose a new weighting parameter for representing requirement of each node resource and modify the Kuramoto model to achieveweighted fair resource allocation for users with different QoS requirements.The proposed modified Kuramoto model allocates all users the resource based on their weight among contending nodes in a distributed manner.We analyze the convergence condition for the proposed model,and the results reveal that the proposed algorithm achieves aweighted fair resource allocation and with potentially high convergence speed compared to previous algorithm.展开更多
Modern control theory course is a compulsory course for automation majors.It is based on automatic control theory course and plays an important role in teaching process.In view of the problems in the teaching of moder...Modern control theory course is a compulsory course for automation majors.It is based on automatic control theory course and plays an important role in teaching process.In view of the problems in the teaching of modern control theory,such as abstract content and weak mathematical foundation,this paper puts forward some reform suggestions from the aspects of teaching method,mathematical foundation,and the difference and connection with the course of automatic control theory,in order to improve students’learning interest and teaching quality.Based on the analysis of the teaching status of automatic control theory in School of Electronic and Electrical Engineering of Zhengzhou University of Science and Technology,combined with the reform needs of ideological and political needs,teaching methods,assessment and evaluation,this paper puts forward a multi-dimensional curriculum reform with“moral education as the center”and“case type”+“online and offline hybrid”+“ideological and political”Thinking.Some results have been obtained through practice.展开更多
Image fusion is an important branch of image processing.Because of the remarkable advantages of neural network in image feature extraction and classification,the application of neural network technology in the field o...Image fusion is an important branch of image processing.Because of the remarkable advantages of neural network in image feature extraction and classification,the application of neural network technology in the field of image fusion is also a research hotspot in recent years.Firstly,the infrared and visible light image fusion algorithms based on shallow and deep neural networks are summarized,and the research progress of image fusion technology is introduced in detail,and the research results of fusion algorithms are presented.Finally,the challenges faced by image fusion are discussed,and the future development direction of this field is forecasted.展开更多
“Sensor Principle and Application”is a compulsory professional course for Internet of Things engineering,electronic information engineering,intelligent perception and other majors.In view of the pain points existing...“Sensor Principle and Application”is a compulsory professional course for Internet of Things engineering,electronic information engineering,intelligent perception and other majors.In view of the pain points existing in the teaching of this course,a series of innovative measures are put forward:integrating and updating the teaching content,adopting mixed teaching mode and innovative experimental methods,digging deeply into the curriculum ideology and politics,and building a multiple assessment and evaluation system,so as to comprehensively improve the learning effect of students and enhance their practical innovation ability and comprehensive quality.In order to promote the ideological and political teaching reform of the sensor Principle and application course,the research group combined the training requirements of engineering certification for students and the characteristics of application-oriented undergraduate students,with education moral education as the fundamental goal and task,to cultivate students’sense of innovation and feelings of home and country as the starting point,clear teaching objectives,use a variety of teaching methods,fully tap the integration point of moral education in the course,and optimize the assessment method.The ideological and political construction of professional courses will stimulate young students’confidence in the system,theory,road and culture to a greater extent,and train students to become application-oriented technical talents who love the Party,love the country and dare to innovate.展开更多
Deep learning is more and more widely used in natural language processing.Compared with the traditional n-gram statistical language model,Recurrent neural network(RNN)modeling technology has shown great advantages in ...Deep learning is more and more widely used in natural language processing.Compared with the traditional n-gram statistical language model,Recurrent neural network(RNN)modeling technology has shown great advantages in language modeling,and has been gradually applied in speech recognition,machine translation and other fields.However,at present,the training of RNN language models is mostly offline.For different speech recognition tasks,there are language differences between training corpus and recognition tasks,which affects the recognition rate of speech recognition systems.While using RNN modeling technology to train the Chinese language model,an online RNN model self-adaption algorithm is proposed,which takes the preliminary recognition results of speech signals as corpus to continue training the model,so that the adaptive RNN model can get the maximum match with the recognition task.The experimental results show that the adaptive model effectively reduces the language difference between the language model and the recognition task,and the recognition rate of the system is further improved after the Chinese word confusion network is re-scored,which has been verified in the actual Chinese speech recognition system.展开更多
文摘On the basis of the description of the rare-earth countercurrent extraction process, the on-line detecting method and equipments of rare-earth elements and the application in the process of the rare-earth countercurrent extraction are summarized. The procedure simulation of the computer, the automation control method and its current application are also mentioned in the process of rare-earth countercurrent extraction. The method of soft sensor is proposed. Optimal control method based on object-oriented rare-earth countercurrent extraction process and integrated automation system composed of process management system and process control system are presented, which are the developing direction of the automation of rare-earth countercurrent extraction process.
文摘With the growth of Vehicular Ad-hoc Networks,many services delivery is gaining more attention from the intelligent transportation system.However,mobility characteristics of vehicular networks cause frequent disconnection of routes,especially during the delivery of data.In both developed and developing countries,a lot of time is consumed due to traffic congestion.This has significant negative consequences,including driver stress due to increased time demand,decreased productivity for various personalized and commercial vehicles,and increased emissions of hazardous gases especially air polluting gases are impacting public health in highly populated areas.Clustering is one of the most powerful strategies for achieving a consistent topological structure.Two algorithms are presented in this research work.First,a k-means clustering algorithm in which dynamic grouping by k-implies is performed that fits well with Vehicular network’s dynamic topology characteristics.The suggested clustering reduces overhead and traffic management.Second,for inter and intra-clustering routing,the dynamic routing protocol is proposed,which increases the overall Packet Delivery Ratio and decreases the End-to-End latency.Relative to the cluster-based approach,the proposed protocol achieves improved efficiency in terms of Throughput,Packet Delivery Ratio,and End-to-End delay parameters comparing the situations by taking different number of vehicular nodes in the network.
文摘With the acceleration of the social information process,information awareness and information skills have become the basic qualities of every citizen.The establishment of the training mechanism for scientific and technological innovation talents from the beginning of higher education is insufficient to meet the needs of the development of the times.It is imperative to improve the training of information technology innovation talents and explore a new training model.This paper describes the general situation of the development of education in the field of information technology from a domestic and international perspective.It then analyzes the existing problems,explores new exploration models and implementation suggestions,and puts forward prospects at the end of the paper.
基金Supported by National Natural Science Foundation of P.R.China(50474020,60534010,60504006)
文摘Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the error compensation model of fuzzy system,is proposed to solve the prob- lem that the component content in countercurrent rare-earth extraction process is hardly measured on-line.An industry experiment in the extraction Y process by HAB using this hybrid soft-sensor proves its effectiveness.
文摘CMOS technology is one of the most frequently used technologies in the semiconductor industry as it can be successfully integrated with ICs.Every two years the number of MOS transistors doubles because the size of the MOSFET is reduced.Reducing the size of the MOSFET reduces the size of the channel length which causes short channel effects and it increases the leakage current.To reduce the short channel effects new designs and technologies are implemented.Double gate MOSFET design has shown improvement in performance as amplifiers over a single MOSFET.Silicon-based MOSFET design can be used in a harsh environment.It has been used in various applications such as in detecting biomolecules.The increase in number of gates increases the current drive capability of transistors.GAA MOSFET is an example of a quadruple gate around the four sides of channel that increases gate control over the channel region.It also increases effective channel width that improves drain current and reduces leakage current keeping short channel effects under limit.Junctionless MOSFET operates faster and uses less power with increase in ON-state current leading to a good value of ION/IOFF ratio.In this paper,several gate and channel engineered MOSFET structures are analyzed and compared for sub 45 nm technology node.A comparison among different MOSFET structures has been made for subthreshold performance parameters in terms of IOFF,subthreshold slope and DIBL values.The analog/RF performance is analyzed for transconductance,effective transistor capacitances,stability factor and critical frequency.The paper also covers different applications of advance MOSFET structures in analog/digital or IoT/biomedical applications.
基金This work was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R323)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia,and Taif University Researchers Supporting Project Number TURSP-2020/34),Taif,Saudi Arabia。
文摘Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation(5G)networks.However,it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand.To meet these access conditions and improve Quality of Service,resource allocation(RA)should be carefully optimized.Traditionally,RA problems are nonconvex optimizations,which are performed using heuristic methods,such as genetic algorithm,particle swarm optimization,and simulated annealing.However,the application of these approaches remains computationally expensive and unattractive for dense cellular networks.Therefore,artificial intelligence algorithms are used to improve traditional RA mechanisms.Deep learning is a promising tool for addressing resource management problems in wireless communication.In this study,we investigate a double deep Q-network-based RA framework that maximizes energy efficiency(EE)and total network throughput in unmanned aerial vehicle(UAV)-assisted terrestrial networks.Specifically,the system is studied under the constraints of interference.However,the optimization problem is formulated as a mixed integer nonlinear program.Within this framework,we evaluated the effect of height and the number of UAVs on EE and throughput.Then,in accordance with the experimental results,we compare the proposed algorithm with several artificial intelligence methods.Simulation results indicate that the proposed approach can increase EE with a considerable throughput.
基金This work was funded and supported by the Taif University Researchers Supporting Project Number(TURSP-2020/147),Taif University,Taif,Saudi Arabia.
文摘Fault detection of the photovoltaic(PV)grid is necessary to detect serious output power reduction to avoid PV modules’damage.To identify the fault of the PV arrays,there is a necessity to implement an automatic system.In this IoT and LabVIEW-based automatic fault detection of 3×3 solar array,a PV system is proposed to control and monitor Internet connectivity remotely.Hardware component to automatically reconfigure the solar PV array from the series-parallel(SP)to the complete cross-linked array underneath partial shading conditions(PSC)is centered on the Atmega328 system to achieve maximum power.In the LabVIEW environment,an automated monitoring system is developed.The automatic monitoring system assesses the voltage drop losses present in the DC side of the PV generator and generates a decimal weighted value depending on the defective solar panels and transmits this value to the remote station through an RF modem,and provides an indicator of the faulty solar panel over the built-in Interface LabVIEW.The managing of this GUI indicator helps the monitoring system to generate a panel alert for damaged panels in the PV system.Node MCU in the receiver section enables transmission of the fault status of PV arrays via Internet connectivity.The IoT-based Blynk app is employed for visualizing the fault status of the 3×3 PV array.The dashboard of Blynk visualizes every array with the status.
基金School-level Education Reform Project in 2024:Thoughts and Exploration on Ideological and Political Teaching Reform of“Sensor Principle and Application”Course under OBE Concept(Youth Project 66).
文摘Outcome based Education(OBE)emphasizes“student-centered,output-oriented”continuous improvement to meet the needs of students to acquire knowledge and develop abilities.The result orientation of the existing“Sensor principle and application”course teaching reform emphasizes the understanding and application of sensor principle,mastering the principle of language rules,object-oriented programming method and thinking mode.The teaching reform of this course is based on the concept of OBE,focusing on the combination of sensor principles and students’majors,and guiding students to apply object-oriented development tools reasonably and efficiently in related professional engineering.Comprehensive projects should be introduced into practical teaching to appropriately increase the workload and difficulty of practical teaching and cultivate students’awareness and ability of teamwork.Solve the“pain points”and“blocking points”existing in the curriculum,so as to combine the curriculum with talent training.The results of practical application show that the students’engineering application ability is improved and they can complete the comprehensive practical project well.
基金supported partially by NationalNatural Science Foundation of China(NSFC)(No.U21A20146)Collaborative Innovation Project of Anhui Universities(No.GXXT-2020-070)+8 种基金Cooperation Project of Anhui Future Technology Research Institute and Enterprise(No.2023qyhz32)Development of a New Dynamic Life Prediction Technology for Energy Storage Batteries(No.KH10003598)Opening Project of Key Laboratory of Electric Drive and Control of Anhui Province(No.DQKJ202304)Anhui Provincial Department of Education New Era Education Quality Project(No.2023dshwyx019)Special Fund for Collaborative Innovation between Anhui Polytechnic University and Jiujiang District(No.2022cyxtb10)Key Research and Development Program of Wuhu City(No.2022yf42)Open Research Fund of Anhui Key Laboratory of Detection Technology and Energy Saving Devices(No.JCKJ2021B06)Anhui Provincial Graduate Student Innovation and Entrepreneurship Practice Project(No.2022cxcysj123)Key Scientific Research Project for Anhui Universities(No.2022AH050981).
文摘Accurate and reliable fault detection is essential for the safe operation of electric vehicles.Support vector data description(SVDD)has been widely used in the field of fault detection.However,constructing the hypersphere boundary only describes the distribution of unlabeled samples,while the distribution of faulty samples cannot be effectively described and easilymisses detecting faulty data due to the imbalance of sample distribution.Meanwhile,selecting parameters is critical to the detection performance,and empirical parameterization is generally timeconsuming and laborious and may not result in finding the optimal parameters.Therefore,this paper proposes a semi-supervised data-driven method based on which the SVDD algorithm is improved and achieves excellent fault detection performance.By incorporating faulty samples into the underlying SVDD model,training deals better with the problem of missing detection of faulty samples caused by the imbalance in the distribution of abnormal samples,and the hypersphere boundary ismodified to classify the samplesmore accurately.The Bayesian Optimization NSVDD(BO-NSVDD)model was constructed to quickly and accurately optimize hyperparameter combinations.In the experiments,electric vehicle operation data with four common fault types are used to evaluate the performance with other five models,and the results show that the BO-NSVDD model presents superior detection performance for each type of fault data,especially in the imperceptible early and minor faults,which has seen very obvious advantages.Finally,the strong robustness of the proposed method is verified by adding different intensities of noise in the dataset.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia has funded this project,under grant no.(FP-221-43).
文摘Reestablishment in power system brings in significant transformation in the power sector by extinguishing the possession of sound consolidated assistance.However,the collaboration of various manufacturing agencies,autonomous power manufacturers,and buyers have created complex installation processes.The regular active load and inefficiency of best measures among varied associates is a huge hazard.Any sudden load deviation will give rise to immediate amendment in frequency and tie-line power errors.It is essential to deal with every zone’s frequency and tie-line power within permitted confines followed by fluctuations within the load.Therefore,it can be proficient by implementing Load Frequency Control under the Bilateral case,stabilizing the power and frequency distinction within the interrelated power grid.Balancing the net deviation in multiple areas is possible by minimizing the unbalance of Bilateral Contracts with the help of proportional integral and advanced controllers like Harris Hawks Optimizer.We proposed the advanced controller Harris Hawk optimizer-based model and validated it on a test bench.The experiment results show that the delay time is 0.0029 s and the settling time of 20.86 s only.This model can also be leveraged to examine the decision boundaries of the Bilateral case.
基金This study was supported by Beijing Jiaotong University(C18A800090).All the supports from above organizations are gratefully acknowledged.
文摘Detection and tracking of all major parts of pig body could be more productive to help to analyze pig behavior.To achieve this goal,a real-time algorithm based on You Only Look At CoefficienTs(YOLACT)was proposed.A pig body was divided into ten parts:one head,one trunk,four thighs and four shanks.And the key points of each part were calculated by the novel algorithm,which was based mainly on combination of the Zhang-Suen thinning algorithm and Gravity algorithm.The experiment results showed that these parts of pig body could be detected and tracked,and their contributions to overall pig activity could also be sought out.The detect accuracy of the algorithm in the data set could reach up to 90%,and the processing speed to 30.5 fps.Furthermore,the algorithm was robust and adaptive.
文摘Lower automation level in industrial rare-earth extraction processes results in high production cost, inconsistent product quality and great consumption of resources in China. An integrated automation system for extraction process of rare earth is proposed to realize optimal product indices, such as product purity,recycle rate and output. The optimal control strategy for output component, structure and function of the two-gradcd integrated automation system composed of the process management grade and the process control grade were discussed. This system is successfully applied to a HAB yttrium extraction production process and was found to provide optimal control, optimal operation, optimal management and remarkable benefits.
文摘Human activity recognition is a recent area of research for researchers.Activity recognition has many applications in smart homes to observe and track toddlers or oldsters for their safety,monitor indoor and outdoor activities,develop Tele immersion systems,or detect abnormal activity recognition.Three dimensions(3D)skeleton data is robust and somehow view-invariant.Due to this,it is one of the popular choices for human action recognition.This paper proposed using a transversal tree from 3D skeleton data to represent videos in a sequence.Further proposed two neural networks:convolutional neural network recurrent neural network_1(CNN_RNN_1),used to find the optimal features and convolutional neural network recurrent neural network network_2(CNN_RNN_2),used to classify actions.The deep neural network-based model proposed CNN_RNN_1 and CNN_RNN_2 that uses a convolutional neural network(CNN),Long short-term memory(LSTM)and Bidirectional Long shortterm memory(BiLSTM)layered.The systemefficiently achieves the desired accuracy over state-of-the-art models,i.e.,88.89%.The performance of the proposed model compared with the existing state-of-the-art models.The NTURGB+D dataset uses for analyzing experimental results.It is one of the large benchmark datasets for human activity recognition.Moreover,the comparison results show that the proposed model outperformed the state-ofthe-art models.
基金supported by the National Natural Science Foundation of China(Grant Nos.12004238 and 11764036)the Natural Science Foundation of Henan Province,China(Grant No.222102230068)the Open Subject of the Key Laboratory of Weak Light Nonlinear Photonics of Nankai University(Grant No.OS 21-3)。
文摘Enhancing the upconversion luminescence of rare earth ions is crucial for their applications in the laser sources,fiber optic communications,color displays,biolabeling,and biomedical sensors.In this paper,we theoretically study the resonance-mediated(1+2)-three-photon absorption in Pr^(3+) ions by a rectangle phase modulation.The results show that the resonance-mediated(1+2)-three-photon absorption can be effectively enhanced by properly designing the depth and width of the rectangle phase modulation,which can be attributed to the constructive interference between on-resonant and near-resonant three-photon excitation pathways.Further,the enhancement efficiency of resonance-mediated(1+2)-threephoton absorption can be affected by the pulse width(or spectral bandwidth)of femtosecond laser field,final state transition frequency,and absorption bandwidths.This research can provide a clear physical picture for understanding and controlling the multi-photon absorption in rare-earth ions,and also can provide theoretical guidance for improving the up-conversion luminescence.
基金Ahmed Alhussen would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2022-193.
文摘Many extensive UAV communication networks have used UAV cooperative control.Wireless networking services can be offered using unmanned aerial vehicles(UAVs)as aerial base stations.Not only is coverage maximization,but also better connectivity,a fundamental design challenge that must be solved.The number of applications for unmanned aerial vehicles(UAVs)operating in unlicensed bands is fast expanding as the Internet of Things(IoT)develops.Those bands,however,have become overcrowded as the number of systems that use them grows.Cognitive Radio(CR)and spectrum allocation approaches have emerged as a potential approach for resolving spectrum scarcity in wireless networks,and hence as technological solutions for future generations,from this perspective.As a result,combining CR with UAVs has the potential to give significant benefits for large-scale UAV deployment.The paper examines existing research on the subject of UAV covering and proposes a multi-UAV cognitive-based error-free model for energy-efficient communication.Coverage maximization,power control,and enhanced connection quality are the three steps of the proposed model.To satisfy the desired signal-to-noise ratio,the covering zone efficacy is investigated as a function of the distance among UAVs stationed in a specific geographic region depending on multiple deployment configurations like as rural,suburban,and urban macro deployment scenarios of the ITU-R M.2135 standard(SNR).
基金supported by the MSIT (Ministry of Science and ICT),Korea,under the ITRC support program (IITP-2021-2018-0-01799)supervised by the IITP (Institute for Information&communications Technology Planning&Evaluation)+1 种基金the Korea Institute of Energy Technology Evaluation and Planning (KETEP)and the Ministry of Trade,Industry&Energy (MOTIE)of the Republic of Korea (No.20214000000280)by the National Research Foundation of Korea (NRF)grant funded by the Korea government (MEST) (No.NRF-2020R1A2C1010929).
文摘Considering the exponential growth of wireless devices with datastarving applications fused with artificial intelligence,the significance of wireless network scalability using distributed behavior and fairness among users is a crucial feature in guaranteeing reliable service to numerous users in the network environment.TheKuramoto model is described as nonlinear selfsustained phase oscillators spinning at varying intrinsic frequencies connected through the sine of their phase differences and displays a phase transition at a specific coupling strength,in which a mutual behavior is accomplished.In this work,we apply the Kuramoto model to achieve a weighted fair resource allocation in a wireless network,where each user has different quality of service(QoS)requirements.Because the original Kuramoto model is the synchronization model,we propose a new weighting parameter for representing requirement of each node resource and modify the Kuramoto model to achieveweighted fair resource allocation for users with different QoS requirements.The proposed modified Kuramoto model allocates all users the resource based on their weight among contending nodes in a distributed manner.We analyze the convergence condition for the proposed model,and the results reveal that the proposed algorithm achieves aweighted fair resource allocation and with potentially high convergence speed compared to previous algorithm.
文摘Modern control theory course is a compulsory course for automation majors.It is based on automatic control theory course and plays an important role in teaching process.In view of the problems in the teaching of modern control theory,such as abstract content and weak mathematical foundation,this paper puts forward some reform suggestions from the aspects of teaching method,mathematical foundation,and the difference and connection with the course of automatic control theory,in order to improve students’learning interest and teaching quality.Based on the analysis of the teaching status of automatic control theory in School of Electronic and Electrical Engineering of Zhengzhou University of Science and Technology,combined with the reform needs of ideological and political needs,teaching methods,assessment and evaluation,this paper puts forward a multi-dimensional curriculum reform with“moral education as the center”and“case type”+“online and offline hybrid”+“ideological and political”Thinking.Some results have been obtained through practice.
基金Science and technology research project,name“Research on key technologies of image fusion based on deep learning”(Project number:242102210187).
文摘Image fusion is an important branch of image processing.Because of the remarkable advantages of neural network in image feature extraction and classification,the application of neural network technology in the field of image fusion is also a research hotspot in recent years.Firstly,the infrared and visible light image fusion algorithms based on shallow and deep neural networks are summarized,and the research progress of image fusion technology is introduced in detail,and the research results of fusion algorithms are presented.Finally,the challenges faced by image fusion are discussed,and the future development direction of this field is forecasted.
基金supported by the School-level Education Reform Project in 2024:Thoughts and Exploration on Ideological and Political Teaching Reform of”Sensor Principle and Application”Course under OBE Concept(Project number:2024JGQN03).
文摘“Sensor Principle and Application”is a compulsory professional course for Internet of Things engineering,electronic information engineering,intelligent perception and other majors.In view of the pain points existing in the teaching of this course,a series of innovative measures are put forward:integrating and updating the teaching content,adopting mixed teaching mode and innovative experimental methods,digging deeply into the curriculum ideology and politics,and building a multiple assessment and evaluation system,so as to comprehensively improve the learning effect of students and enhance their practical innovation ability and comprehensive quality.In order to promote the ideological and political teaching reform of the sensor Principle and application course,the research group combined the training requirements of engineering certification for students and the characteristics of application-oriented undergraduate students,with education moral education as the fundamental goal and task,to cultivate students’sense of innovation and feelings of home and country as the starting point,clear teaching objectives,use a variety of teaching methods,fully tap the integration point of moral education in the course,and optimize the assessment method.The ideological and political construction of professional courses will stimulate young students’confidence in the system,theory,road and culture to a greater extent,and train students to become application-oriented technical talents who love the Party,love the country and dare to innovate.
基金supported by the“Research on language adaptive model based on Deep learning in futureoriented teaching scenarios”,project number:25B413010.
文摘Deep learning is more and more widely used in natural language processing.Compared with the traditional n-gram statistical language model,Recurrent neural network(RNN)modeling technology has shown great advantages in language modeling,and has been gradually applied in speech recognition,machine translation and other fields.However,at present,the training of RNN language models is mostly offline.For different speech recognition tasks,there are language differences between training corpus and recognition tasks,which affects the recognition rate of speech recognition systems.While using RNN modeling technology to train the Chinese language model,an online RNN model self-adaption algorithm is proposed,which takes the preliminary recognition results of speech signals as corpus to continue training the model,so that the adaptive RNN model can get the maximum match with the recognition task.The experimental results show that the adaptive model effectively reduces the language difference between the language model and the recognition task,and the recognition rate of the system is further improved after the Chinese word confusion network is re-scored,which has been verified in the actual Chinese speech recognition system.
