In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the...In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial products.The stockholders focused on the prediction of return rate and risk rate of financial products.Therefore,an automatic return rate bitcoin prediction model becomes essential for BC financial products.The newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive method.This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial products.The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products.Besides,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects.The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.展开更多
Wireless Sensor Networks(WSN)play a vital role in several real-time applications ranging from military to civilian.Despite the benefits of WSN,energy efficiency becomes a major part of the challenging issue in WSN,whi...Wireless Sensor Networks(WSN)play a vital role in several real-time applications ranging from military to civilian.Despite the benefits of WSN,energy efficiency becomes a major part of the challenging issue in WSN,which necessitate proper load balancing amongst the clusters and serves a wider monitoring region.The clustering technique for WSN has several benefits:lower delay,higher energy efficiency,and collision avoidance.But clustering protocol has several challenges.In a large-scale network,cluster-based protocols mainly adapt multi-hop routing to save energy,leading to hot spot problems.A hot spot problem becomes a problem where a cluster node nearer to the base station(BS)tends to drain the energy much quicker than other nodes because of the need to implement more transmission.This article introduces a Jumping Spider Optimization Based Unequal Clustering Protocol for Mitigating Hotspot Problems(JSOUCP-MHP)in WSN.The JSO algorithm is stimulated by the characteristics of spiders naturally and mathematically modelled the hunting mechanism such as search,persecution,and jumping skills to attack prey.The presented JSOUCPMHP technique mainly resolves the hot spot issue for maximizing the network lifespan.The JSOUCP-MHP technique elects a proper set of cluster heads(CHs)using average residual energy(RE)to attain this.In addition,the JSOUCP-MHP technique determines the cluster sizes based on two measures,i.e.,RE and distance to BS(DBS),showing the novelty of the work.The proposed JSOUCP-MHP technique is examined under several experiments to ensure its supremacy.The comparison study shows the significance of the JSOUCPMHP technique over other models.展开更多
In literature review,experimental work and finite element analysis was carried out as per ASTM 1425 and ASTM 2344 to understand the distribution of ILSS(interlaminar shear strength)in S glass epoxy composite for thin ...In literature review,experimental work and finite element analysis was carried out as per ASTM 1425 and ASTM 2344 to understand the distribution of ILSS(interlaminar shear strength)in S glass epoxy composite for thin and thick laminates.Comparison of the ASTM methods is made and ASTM 1425 is recommended since sustainability can be achieved while understanding the properties of the composite.The objective of the present work is to estimate ILSS in CNT(carbon nanotube)reinforced S glass epoxy composites by finite element analysis.展开更多
Infection and intoxication are two common types of foodborne illnesses throughout the world.The aim of the present work was to isolate and characterize biosurfactant producing bacteria fromgroundnut oil cake dumping s...Infection and intoxication are two common types of foodborne illnesses throughout the world.The aim of the present work was to isolate and characterize biosurfactant producing bacteria fromgroundnut oil cake dumping sites and evaluate their biocontrol effects against foodborne pathogens.Bacteria were isolated by enrichment culture technique and preliminary screening method.Biosurfactant activity evaluation was carried out by oil displacement,drop collapse test,lipase activity,hemolytic activity,emulsification index and emulsification assay methods.16s rRNA sequence analysis was used for the isolate identification.Crude biosurfactant was extracted by acid precipitation method and characterized using FTIR(Fourier Transform Infrared Spectroscopy).Antibacterial activity was investigated using disc diffusion method.CMCand surface reduction were analyzed by DuNouy tensiometer.Top 10 strains were selected for biosurfactant activity assessment from the total 30 isolates.In addition,16s rRNA sequence identified that the potential isolate was Bacillus pseudomycoides OR 1.Then,FTIR result of the extracted biosurfactant established the extract as a lipopeptide based on the absorption peaks at 3500 to 3200 cm^-1 and 2963 to 2854.68 cm^-1,respectively.50μg/mL of lipopeptide showed the highest antibacterial activity.Critical Micelle Concentration(CMC)of the lipopeptide was 60 mg/L and it reduced the surface tension of water from 71.6 to 31.6 mN/m.Hence,this study widens the scope to employ the bacterial lipopeptide surfactant as a promising biocontrol agent against foodborne pathogens.展开更多
The power transfer capability of the smart transmission gridconnected networks needs to be reduced by inter-area oscillations.Due to the fact that inter-area modes of oscillations detain and make instability of power ...The power transfer capability of the smart transmission gridconnected networks needs to be reduced by inter-area oscillations.Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks.This fact is more noticeable in smart grid-connected systems.The smart grid infrastructure has more renewable energy resources installed for its operation.To overcome this problem,a deep learning widearea controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes.The proposed Deep Wide Area Controller(DWAC)uses the Deep Belief Network(DBN).The network weights are updated based on real-time data from Phasor measurement units.Resilience assessment based on failure probability,financial impact,and time-series data in grid failure management determine the norm H2.To demonstrate the effectiveness of the proposed framework,a time-domain simulation case study based on the IEEE-39 bus system was performed.For a one-channel attack on the test system,the resiliency index increased to 0.962,and inter-area dampingξwas reduced to 0.005.The obtained results validate the proposed deep learning algorithm’s efficiency on damping inter-area and local oscillation on the 2-channel attack as well.Results also offer robust management of power system resilience and timely control of the operating conditions.展开更多
Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits ...Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits from pictures.But,due to the similarity and complexity,fruit recognition becomes an issue for the stacked fruits on a weighing scale.Recently,Machine Learning(ML)methods have been used in fruit farming and agriculture and brought great convenience to human life.An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts.CNN’s(convolutional neural networks)have attained incredible outcomes in image classifiers in several domains.Considering the success of transfer learning and CNNs in other image classifier issues,this study introduces an Artificial Humming Bird Optimization with Siamese Convolutional Neural Network based Fruit Classification(AMO-SCNNFC)model.In the presented AMO-SCNNFC technique,image preprocessing is performed to enhance the contrast level of the image.In addition,spiral optimization(SPO)with the VGG-16 model is utilized to derive feature vectors.For fruit classification,AHO with end to end SCNN(ESCNN)model is applied to identify different classes of fruits.The performance validation of the AMO-SCNNFC technique is tested using a dataset comprising diverse classes of fruit images.Extensive comparison studies reported improving the AMOSCNNFC technique over other approaches with higher accuracy of 99.88%.展开更多
The recent developments in smart cities pose major security issues for the Internet of Things(IoT)devices.These security issues directly result from inappropriate security management protocols and their implementation...The recent developments in smart cities pose major security issues for the Internet of Things(IoT)devices.These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers.Cyber-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)attacks.In this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their damage.The recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying cyber-attacks.The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT environment.The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment.To accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square test.To detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this study.Finally,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition efficiency.The proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct aspects.The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.展开更多
Recently,a trust system was introduced to enhance security and cooperation between nodes in wireless sensor networks(WSN).In routing,the trust system includes or avoids nodes related to the estimated trust values in t...Recently,a trust system was introduced to enhance security and cooperation between nodes in wireless sensor networks(WSN).In routing,the trust system includes or avoids nodes related to the estimated trust values in the routing function.This article introduces Enhanced Metaheuristics with Trust Aware Secure Route Selection Protocol(EMTA-SRSP)for WSN.The presented EMTA-SRSP technique majorly involves the optimal selection of routes in WSN.To accomplish this,the EMTA-SRSP technique involves the design of an oppositional Aquila optimization algorithm to choose safe routes for data communication.For the clustering process,the nodes with maximum residual energy will be considered cluster heads(CHs).In addition,the OAOA technique gets executed to choose optimal routes based on objective functions with multiple parameters such as energy,distance,and trust degree.The experimental validation of the EMTA-SRSP technique is tested,and the results exhibited a better performance of the EMTA-SRSP technique over other approaches.展开更多
In this paper, a salp swarm algorithm(SSA) is proposed for solving the optimal power flow(OPF)problem of a power system with the incorporation of the thyristor-controlled series capacitor(TCSC). The proposed methodolo...In this paper, a salp swarm algorithm(SSA) is proposed for solving the optimal power flow(OPF)problem of a power system with the incorporation of the thyristor-controlled series capacitor(TCSC). The proposed methodology is implemented for determining the optimal setting of control variables for the OPF problem, which includes the real power of generators buses, voltages of generator buses, reactive power injected by shunt compensators, and tap changing transformer ratios. The performance of the proposed approach is validated and tested on the standard IEEE-30 bus system and single-objective functions, including transmission line losses. The severity factor has been minimized and the result obtained is compared with the existing algorithms. Simulation results achieved with the proposed SSA approach demonstrate that it results in an effective and better solution for the OPF problem.展开更多
Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield.Precision agriculture offers a novel solution utilizing a systematic technique for current...Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield.Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns.Weed control has become one of the significant problems in the agricultural sector.In traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same way.For more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed type.This may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable agriculture.This study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision Agriculture.The HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision agriculture.For image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise removal.In addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature vectors.To detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection performance.The experimental results of the HHOGCN-WD technique are investigated under the benchmark dataset.The results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%.展开更多
Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged,and due to the pest attacks,the quality is degraded.They are the major reason behind crop quality degr...Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged,and due to the pest attacks,the quality is degraded.They are the major reason behind crop quality degradation and diminished crop productivity.Hence,accurate pest detection is essential to guarantee safety and crop quality.Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features.Lately,some progress has been made in agriculture by employing machine learning(ML)to classify and detect pests.This study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops(MMTL-IPCAC)technique.The presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization(CLAHE)approach for image enhancement.The neural architectural search network(NASNet)model is applied for feature extraction,and a modified grey wolf optimization(MGWO)algorithm is employed for the hyperparameter tuning process,showing the novelty of the work.At last,the extreme gradient boosting(XGBoost)model is utilized to carry out the insect classification procedure.The simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.展开更多
The electrical transport behavior and magnetoresistance (MR) of a polycrystalline double layered manganite La1.2Sr1.4Ba0.4Mn2O7, synthesized by the sol-gel method, are investigated in the temperature range 4.2 K - 300...The electrical transport behavior and magnetoresistance (MR) of a polycrystalline double layered manganite La1.2Sr1.4Ba0.4Mn2O7, synthesized by the sol-gel method, are investigated in the temperature range 4.2 K - 300 K. The sample exhibits an insulator-to-metal transition at 87 K (TIM) and the spin-glass (SG)-like behavior is observed below 50 K (TSG). The transport behavior is analyzed in the entire temperature range considering three different regions: paramagnetic insulating region (T>TIM), ferromagnetic metallic region (TSG IM) and antiferromagnetic insulating region (TSG) by fitting the temperature dependent resistivity data to the equations governing the conduction process in the respective temperature regions. The results show that the conduction at T>TIM follows Mott variable range hopping (VRH) process, while the two-magnon scattering process is evidenced at TSG IM which is suppressed with the applied magnetic field of 4 T. The low temperature conductivity data are also fitted with Mott VRH equation. The sample exhibits a large MR (≈45%) over a temperature range???? 5 K – 50 K and it shows ≈32% MR at 5 K with a magnetic field of 0.5 T.展开更多
Recently,Cooperative Spectrum Sensing(CSS)for Cognitive Radio Networks(CRN)plays a significant role in efficient 5G wireless communication.Spectrum sensing is a significant technology in CRN to identify underutilized ...Recently,Cooperative Spectrum Sensing(CSS)for Cognitive Radio Networks(CRN)plays a significant role in efficient 5G wireless communication.Spectrum sensing is a significant technology in CRN to identify underutilized spectrums.The CSS technique is highly applicable due to its fast and efficient performance.5G wireless communication is widely employed for the continuous development of efficient and accurate Internet of Things(IoT)networks.5G wireless communication will potentially lead the way for next generation IoT communication.CSS has established significant consideration as a feasible resource to improve identification performance by developing spatial diversity in receiving signal strength in IoT.In this paper,an optimal CSS for CRN is performed using Offset Quadrature Amplitude Modulation Universal Filtered Multi-Carrier Non-Orthogonal Multiple Access(OQAM/UFMC/NOMA)methodologies.Availability of spectrum and bandwidth utilization is a key challenge in CRN for IoT 5G wireless communication.The optimal solution for CRN in IoT-based 5G communication should be able to provide optimal bandwidth and CSS,low latency,Signal Noise Ratio(SNR)improvement,maximum capacity,offset synchronization,and Peak Average Power Ratio(PAPR)reduction.The Energy Efficient All-Pass Filter(EEAPF)algorithm is used to eliminate PAPR.The deployment approach improves Quality of Service(QoS)in terms of system reliability,throughput,and energy efficiency.Our in-depth experimental results show that the proposed methodology provides an optimal solution when directly compares against current existing methodologies.展开更多
The polycrystalline colossal magnetoresistive double-layered manganite samples R1.2Sr1.8Mn2O7（R = La Pr, Nd, Sm） were prepared by the sol–gel method and their room temperature elastic behavior was investigated by u...The polycrystalline colossal magnetoresistive double-layered manganite samples R1.2Sr1.8Mn2O7（R = La Pr, Nd, Sm） were prepared by the sol–gel method and their room temperature elastic behavior was investigated by ultrasonic pulse transmission technique at 1 MHz. The values of elastic constants were calculated from longitudinal and shear velocities and they were corrected to zero porosity using Hasselman and Fulrath＇s formulae. The elastic constants of the samples were also estimated by Modi＇s heterogeneous metal-mixture rule which is based on the metal ions present in the samples. The measured,corrected, and estimated values of elastic moduli are found to increase with decreasing rare earth ion size. The variation of elastic moduli with the size of the rare earth ion is interpreted in terms of strength of interatomic bonding.展开更多
There has been an immense endeavor to mitigate global warming in spite of which it has only been worse. This paper presents the design and imple- mentation of a low power and eco-friendly refrigeration system using th...There has been an immense endeavor to mitigate global warming in spite of which it has only been worse. This paper presents the design and imple- mentation of a low power and eco-friendly refrigeration system using the thermoelectric effect. The conventional refrigerators make use of complex mechanisms which involves synchronous operation of various units, namely the compressor, condensers, expansion valves, evaporator, refi＇igerant and so on. But a thermoelectric refrigerator exploits the principle of the Peltier effect, thus avoiding the utilization of these complex components. This even helps curb the release of harmful chlorofluorocarbons （CFCs） into the atmosphere which contributes to the increase in global temperature. Moreover, the temperature can be controlled and set to required values with the help of a microcontroller. Hence, this can be used both for domestic and commercial purposes. The unit does not eject any harmful gases. Therefore, the heat expelled from the unit can be tapped for heating utilities, making the use of this device versatile in its application. Thus this proposal aims not only at reducing the air pollutants by not contributing to it but also at reducing the power consumption.展开更多
Reliable and accessible cloud applications are essential for the future of ubiquitous computing,smart appliances,and electronic health.Owing to the vastness and diversity of the cloud,a most cloud services,both physic...Reliable and accessible cloud applications are essential for the future of ubiquitous computing,smart appliances,and electronic health.Owing to the vastness and diversity of the cloud,a most cloud services,both physical and logical services have failed.Using currently accessible traces,we assessed and characterized the behaviors of successful and unsuccessful activities.We devised and implemented a method to forecast which jobs will fail.The proposed method optimizes cloud applications more efficiently in terms of resource usage.Using Google Cluster,Mustang,and Trinity traces,which are publicly available,an in-depth evaluation of the proposed model was conducted.The traces were also fed into several different machine learning models to select the most reliable model.Our efficiency analysis proves that the model performs well in terms of accuracy,F1-score,and recall.Several factors,such as failure of forecasting work,design of scheduling algorithms,modification of priority criteria,and restriction of task resubmission,may increase cloud service dependability and availability.展开更多
基金supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493)by the NRF grant funded by the Korea government(MSIT)(NRF-2022R1A2C1004401).
文摘In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial products.The stockholders focused on the prediction of return rate and risk rate of financial products.Therefore,an automatic return rate bitcoin prediction model becomes essential for BC financial products.The newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive method.This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial products.The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products.Besides,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects.The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.
基金This research was supported by the MSIT(Ministry of Science and ICT)Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)program(IITP-2022-2020-0-01832)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation)and the Korea Technology and Information Promotion Agency(TIPA)for SMEs grant funded by the Korea government(Ministry of SMEs and Startups)(No.S3271954)and the Soonchunhyang University Research Fund。
文摘Wireless Sensor Networks(WSN)play a vital role in several real-time applications ranging from military to civilian.Despite the benefits of WSN,energy efficiency becomes a major part of the challenging issue in WSN,which necessitate proper load balancing amongst the clusters and serves a wider monitoring region.The clustering technique for WSN has several benefits:lower delay,higher energy efficiency,and collision avoidance.But clustering protocol has several challenges.In a large-scale network,cluster-based protocols mainly adapt multi-hop routing to save energy,leading to hot spot problems.A hot spot problem becomes a problem where a cluster node nearer to the base station(BS)tends to drain the energy much quicker than other nodes because of the need to implement more transmission.This article introduces a Jumping Spider Optimization Based Unequal Clustering Protocol for Mitigating Hotspot Problems(JSOUCP-MHP)in WSN.The JSO algorithm is stimulated by the characteristics of spiders naturally and mathematically modelled the hunting mechanism such as search,persecution,and jumping skills to attack prey.The presented JSOUCPMHP technique mainly resolves the hot spot issue for maximizing the network lifespan.The JSOUCP-MHP technique elects a proper set of cluster heads(CHs)using average residual energy(RE)to attain this.In addition,the JSOUCP-MHP technique determines the cluster sizes based on two measures,i.e.,RE and distance to BS(DBS),showing the novelty of the work.The proposed JSOUCP-MHP technique is examined under several experiments to ensure its supremacy.The comparison study shows the significance of the JSOUCPMHP technique over other models.
文摘In literature review,experimental work and finite element analysis was carried out as per ASTM 1425 and ASTM 2344 to understand the distribution of ILSS(interlaminar shear strength)in S glass epoxy composite for thin and thick laminates.Comparison of the ASTM methods is made and ASTM 1425 is recommended since sustainability can be achieved while understanding the properties of the composite.The objective of the present work is to estimate ILSS in CNT(carbon nanotube)reinforced S glass epoxy composites by finite element analysis.
文摘Infection and intoxication are two common types of foodborne illnesses throughout the world.The aim of the present work was to isolate and characterize biosurfactant producing bacteria fromgroundnut oil cake dumping sites and evaluate their biocontrol effects against foodborne pathogens.Bacteria were isolated by enrichment culture technique and preliminary screening method.Biosurfactant activity evaluation was carried out by oil displacement,drop collapse test,lipase activity,hemolytic activity,emulsification index and emulsification assay methods.16s rRNA sequence analysis was used for the isolate identification.Crude biosurfactant was extracted by acid precipitation method and characterized using FTIR(Fourier Transform Infrared Spectroscopy).Antibacterial activity was investigated using disc diffusion method.CMCand surface reduction were analyzed by DuNouy tensiometer.Top 10 strains were selected for biosurfactant activity assessment from the total 30 isolates.In addition,16s rRNA sequence identified that the potential isolate was Bacillus pseudomycoides OR 1.Then,FTIR result of the extracted biosurfactant established the extract as a lipopeptide based on the absorption peaks at 3500 to 3200 cm^-1 and 2963 to 2854.68 cm^-1,respectively.50μg/mL of lipopeptide showed the highest antibacterial activity.Critical Micelle Concentration(CMC)of the lipopeptide was 60 mg/L and it reduced the surface tension of water from 71.6 to 31.6 mN/m.Hence,this study widens the scope to employ the bacterial lipopeptide surfactant as a promising biocontrol agent against foodborne pathogens.
文摘The power transfer capability of the smart transmission gridconnected networks needs to be reduced by inter-area oscillations.Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks.This fact is more noticeable in smart grid-connected systems.The smart grid infrastructure has more renewable energy resources installed for its operation.To overcome this problem,a deep learning widearea controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes.The proposed Deep Wide Area Controller(DWAC)uses the Deep Belief Network(DBN).The network weights are updated based on real-time data from Phasor measurement units.Resilience assessment based on failure probability,financial impact,and time-series data in grid failure management determine the norm H2.To demonstrate the effectiveness of the proposed framework,a time-domain simulation case study based on the IEEE-39 bus system was performed.For a one-channel attack on the test system,the resiliency index increased to 0.962,and inter-area dampingξwas reduced to 0.005.The obtained results validate the proposed deep learning algorithm’s efficiency on damping inter-area and local oscillation on the 2-channel attack as well.Results also offer robust management of power system resilience and timely control of the operating conditions.
基金supported by Basic Science Research Program through the National Research Foundation of Korea (NRF)funded by the Ministry of Education (2020R1A6A1A03038540)by Korea Institute of Planning and Evaluation for Technology in Food,Agriculture,Forestry and Fisheries (IPET)through Digital Breeding Transformation Technology Development Program,funded by Ministry of Agriculture,Food and Rural Affairs (MAFRA) (322063-03-1-SB010)by the Technology development Program (RS-2022-00156456)funded by the Ministry of SMEs and Startups (MSS,Korea).
文摘Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits from pictures.But,due to the similarity and complexity,fruit recognition becomes an issue for the stacked fruits on a weighing scale.Recently,Machine Learning(ML)methods have been used in fruit farming and agriculture and brought great convenience to human life.An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts.CNN’s(convolutional neural networks)have attained incredible outcomes in image classifiers in several domains.Considering the success of transfer learning and CNNs in other image classifier issues,this study introduces an Artificial Humming Bird Optimization with Siamese Convolutional Neural Network based Fruit Classification(AMO-SCNNFC)model.In the presented AMO-SCNNFC technique,image preprocessing is performed to enhance the contrast level of the image.In addition,spiral optimization(SPO)with the VGG-16 model is utilized to derive feature vectors.For fruit classification,AHO with end to end SCNN(ESCNN)model is applied to identify different classes of fruits.The performance validation of the AMO-SCNNFC technique is tested using a dataset comprising diverse classes of fruit images.Extensive comparison studies reported improving the AMOSCNNFC technique over other approaches with higher accuracy of 99.88%.
基金This research was supported by the Universiti Sains Malaysia(USM)and the ministry of Higher Education Malaysia through Fundamental Research GrantScheme(FRGS-Grant No:FRGS/1/2020/TK0/USM/02/1).
文摘The recent developments in smart cities pose major security issues for the Internet of Things(IoT)devices.These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers.Cyber-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)attacks.In this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their damage.The recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying cyber-attacks.The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT environment.The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment.To accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square test.To detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this study.Finally,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition efficiency.The proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct aspects.The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.
基金This research was supported by the Universiti Sains Malaysia(USM)and the Ministry of Higher Education Malaysia through Fundamental Research GrantScheme(FRGS-Grant No:FRGS/1/2020/TK0/USM/02/1).
文摘Recently,a trust system was introduced to enhance security and cooperation between nodes in wireless sensor networks(WSN).In routing,the trust system includes or avoids nodes related to the estimated trust values in the routing function.This article introduces Enhanced Metaheuristics with Trust Aware Secure Route Selection Protocol(EMTA-SRSP)for WSN.The presented EMTA-SRSP technique majorly involves the optimal selection of routes in WSN.To accomplish this,the EMTA-SRSP technique involves the design of an oppositional Aquila optimization algorithm to choose safe routes for data communication.For the clustering process,the nodes with maximum residual energy will be considered cluster heads(CHs).In addition,the OAOA technique gets executed to choose optimal routes based on objective functions with multiple parameters such as energy,distance,and trust degree.The experimental validation of the EMTA-SRSP technique is tested,and the results exhibited a better performance of the EMTA-SRSP technique over other approaches.
文摘In this paper, a salp swarm algorithm(SSA) is proposed for solving the optimal power flow(OPF)problem of a power system with the incorporation of the thyristor-controlled series capacitor(TCSC). The proposed methodology is implemented for determining the optimal setting of control variables for the OPF problem, which includes the real power of generators buses, voltages of generator buses, reactive power injected by shunt compensators, and tap changing transformer ratios. The performance of the proposed approach is validated and tested on the standard IEEE-30 bus system and single-objective functions, including transmission line losses. The severity factor has been minimized and the result obtained is compared with the existing algorithms. Simulation results achieved with the proposed SSA approach demonstrate that it results in an effective and better solution for the OPF problem.
基金This research was partly supported by the Technology Development Program of MSS[No.S3033853]by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2020R1I1A3069700).
文摘Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield.Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns.Weed control has become one of the significant problems in the agricultural sector.In traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same way.For more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed type.This may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable agriculture.This study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision Agriculture.The HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision agriculture.For image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise removal.In addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature vectors.To detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection performance.The experimental results of the HHOGCN-WD technique are investigated under the benchmark dataset.The results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%.
文摘Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged,and due to the pest attacks,the quality is degraded.They are the major reason behind crop quality degradation and diminished crop productivity.Hence,accurate pest detection is essential to guarantee safety and crop quality.Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features.Lately,some progress has been made in agriculture by employing machine learning(ML)to classify and detect pests.This study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops(MMTL-IPCAC)technique.The presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization(CLAHE)approach for image enhancement.The neural architectural search network(NASNet)model is applied for feature extraction,and a modified grey wolf optimization(MGWO)algorithm is employed for the hyperparameter tuning process,showing the novelty of the work.At last,the extreme gradient boosting(XGBoost)model is utilized to carry out the insect classification procedure.The simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.
文摘The electrical transport behavior and magnetoresistance (MR) of a polycrystalline double layered manganite La1.2Sr1.4Ba0.4Mn2O7, synthesized by the sol-gel method, are investigated in the temperature range 4.2 K - 300 K. The sample exhibits an insulator-to-metal transition at 87 K (TIM) and the spin-glass (SG)-like behavior is observed below 50 K (TSG). The transport behavior is analyzed in the entire temperature range considering three different regions: paramagnetic insulating region (T>TIM), ferromagnetic metallic region (TSG IM) and antiferromagnetic insulating region (TSG) by fitting the temperature dependent resistivity data to the equations governing the conduction process in the respective temperature regions. The results show that the conduction at T>TIM follows Mott variable range hopping (VRH) process, while the two-magnon scattering process is evidenced at TSG IM which is suppressed with the applied magnetic field of 4 T. The low temperature conductivity data are also fitted with Mott VRH equation. The sample exhibits a large MR (≈45%) over a temperature range???? 5 K – 50 K and it shows ≈32% MR at 5 K with a magnetic field of 0.5 T.
文摘Recently,Cooperative Spectrum Sensing(CSS)for Cognitive Radio Networks(CRN)plays a significant role in efficient 5G wireless communication.Spectrum sensing is a significant technology in CRN to identify underutilized spectrums.The CSS technique is highly applicable due to its fast and efficient performance.5G wireless communication is widely employed for the continuous development of efficient and accurate Internet of Things(IoT)networks.5G wireless communication will potentially lead the way for next generation IoT communication.CSS has established significant consideration as a feasible resource to improve identification performance by developing spatial diversity in receiving signal strength in IoT.In this paper,an optimal CSS for CRN is performed using Offset Quadrature Amplitude Modulation Universal Filtered Multi-Carrier Non-Orthogonal Multiple Access(OQAM/UFMC/NOMA)methodologies.Availability of spectrum and bandwidth utilization is a key challenge in CRN for IoT 5G wireless communication.The optimal solution for CRN in IoT-based 5G communication should be able to provide optimal bandwidth and CSS,low latency,Signal Noise Ratio(SNR)improvement,maximum capacity,offset synchronization,and Peak Average Power Ratio(PAPR)reduction.The Energy Efficient All-Pass Filter(EEAPF)algorithm is used to eliminate PAPR.The deployment approach improves Quality of Service(QoS)in terms of system reliability,throughput,and energy efficiency.Our in-depth experimental results show that the proposed methodology provides an optimal solution when directly compares against current existing methodologies.
文摘The polycrystalline colossal magnetoresistive double-layered manganite samples R1.2Sr1.8Mn2O7（R = La Pr, Nd, Sm） were prepared by the sol–gel method and their room temperature elastic behavior was investigated by ultrasonic pulse transmission technique at 1 MHz. The values of elastic constants were calculated from longitudinal and shear velocities and they were corrected to zero porosity using Hasselman and Fulrath＇s formulae. The elastic constants of the samples were also estimated by Modi＇s heterogeneous metal-mixture rule which is based on the metal ions present in the samples. The measured,corrected, and estimated values of elastic moduli are found to increase with decreasing rare earth ion size. The variation of elastic moduli with the size of the rare earth ion is interpreted in terms of strength of interatomic bonding.
文摘There has been an immense endeavor to mitigate global warming in spite of which it has only been worse. This paper presents the design and imple- mentation of a low power and eco-friendly refrigeration system using the thermoelectric effect. The conventional refrigerators make use of complex mechanisms which involves synchronous operation of various units, namely the compressor, condensers, expansion valves, evaporator, refi＇igerant and so on. But a thermoelectric refrigerator exploits the principle of the Peltier effect, thus avoiding the utilization of these complex components. This even helps curb the release of harmful chlorofluorocarbons （CFCs） into the atmosphere which contributes to the increase in global temperature. Moreover, the temperature can be controlled and set to required values with the help of a microcontroller. Hence, this can be used both for domestic and commercial purposes. The unit does not eject any harmful gases. Therefore, the heat expelled from the unit can be tapped for heating utilities, making the use of this device versatile in its application. Thus this proposal aims not only at reducing the air pollutants by not contributing to it but also at reducing the power consumption.
文摘Reliable and accessible cloud applications are essential for the future of ubiquitous computing,smart appliances,and electronic health.Owing to the vastness and diversity of the cloud,a most cloud services,both physical and logical services have failed.Using currently accessible traces,we assessed and characterized the behaviors of successful and unsuccessful activities.We devised and implemented a method to forecast which jobs will fail.The proposed method optimizes cloud applications more efficiently in terms of resource usage.Using Google Cluster,Mustang,and Trinity traces,which are publicly available,an in-depth evaluation of the proposed model was conducted.The traces were also fed into several different machine learning models to select the most reliable model.Our efficiency analysis proves that the model performs well in terms of accuracy,F1-score,and recall.Several factors,such as failure of forecasting work,design of scheduling algorithms,modification of priority criteria,and restriction of task resubmission,may increase cloud service dependability and availability.
