Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty per...Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of the total world population.The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains.This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques.Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy.There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind,relative humidity,Instant Wind Speed in paddy cultivation.The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series.A Robust Optimized Artificial Neural Network(ROANN)Algorithm with Genetic Algorithm(GA)and Multi Objective Particle Swarm Optimization Algorithm(MOPSO)proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation.A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database.The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database.Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm.The reason for improving the growth of paddy was identified using the output of the Neural Network.Performance metrics such as Accuracy,Error Rate etc were used to measure the performance of the proposed algorithm.Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield.展开更多
Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,maki...Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.展开更多
With the growth of the online market,demand for logistics and courier cargo is increasing rapidly.Accordingly,in the case of urban areas,road congestion and environmental problems due to cargo vehicles are mainly occu...With the growth of the online market,demand for logistics and courier cargo is increasing rapidly.Accordingly,in the case of urban areas,road congestion and environmental problems due to cargo vehicles are mainly occurring.The joint courier logistics system,a plan to solve this problem,aims to establish an efficient logistics transportation system by utilizing one joint logistics delivery terminal by several logistics and delivery companies.However,several courier companies use different types of courier invoices.Such a system has a problem of information data transmission interruption.Therefore,the data processing process was systematically analyzed,a practically feasible methodology was devised,and delivery invoice information processing standards were established for this.In addition,the importance of this paper can be emphasized in terms of data processing in the logistics sector,which is expected to grow rapidly in the future.The results of this study can be used as basic data for the implementation of the logistics joint delivery terminal system in the future.And it can be used as a basis for securing the operational reliability of the joint courier logistics system.展开更多
An optimizing method of observation scheduling based on time-division multiplexing is proposed in this paper,and its efficiency is verified by outdoor experiments. The initial observation scheduling is first obtained ...An optimizing method of observation scheduling based on time-division multiplexing is proposed in this paper,and its efficiency is verified by outdoor experiments. The initial observation scheduling is first obtained by using a semi-random search algorithm,and secondly the connection time pair（ CTP） between adjacent objects is optimized by using a genetic algorithm. After obtaining these two parameters,the final observation scheduling can be obtained. According to pre-designed tracks between each adjacent objects in observation order,the seamless observation of neighboring targets is derived by automatically steering the antenna beam,so the observation efficiency is improved.展开更多
With the increasing number of vehicles,there has been an unprecedented pressure on the operation and maintenance of intelligent transportation systems and transportation infrastructure.In order to achieve faster and m...With the increasing number of vehicles,there has been an unprecedented pressure on the operation and maintenance of intelligent transportation systems and transportation infrastructure.In order to achieve faster and more accurate identification of traffic vehicles,computer vision and deep learning technology play a vital role and have made significant advancements.This study summarizes the current research status,latest findings,and future development trends of traditional detection algorithms and deep learning-based detection algorithms.Among the detection algorithms based on deep learning,this study focuses on the representative convolutional neural network models.Specifically,it examines the two-stage and one-stage detection algorithms,which have been extensively utilized in the field of intelligent transportation systems.Compared to traditional detection algorithms,deep learning-based detection algorithms can achieve higher accuracy and efficiency.The single-stage detection algorithm is more efficient for real-time detection,while the two-stage detection algorithm is more accurate than the single-stage detection algorithm.In the follow-up research,it is important to consider the balance between detection efficiency and detection accuracy.Additionally,vehicle missed detection and false detection in complex scenes,such as bad weather and vehicle overlap,should be taken into account.This will ensure better application of the research findings in engineering practice.展开更多
Energy consumption in agricultural products and its environmental damages has increased in recent centuries.Life cycle assessment(LCA)has been introduced as a suitable tool for evaluation environmental impacts related...Energy consumption in agricultural products and its environmental damages has increased in recent centuries.Life cycle assessment(LCA)has been introduced as a suitable tool for evaluation environmental impacts related to a product over its life cycle.In this study,optimization of energy consumption and environmental impacts of chickpea production was conducted using data envelopment analysis(DEA)and multi objective genetic algorithm(MOGA)techniques.Data were collected from 110 chickpea production enterprises using a face to face questionnaire in the cropping season of 2014-2015.The results of optimization revealed that,when applying MOGA,optimum energy requirement for chickpea production was significantly lower compared to application of DEA technique;so that,total energy requirement in optimum situation was found to be 31511.72 and 27570.61 MJ ha^-1 by using DEA and MOGA techniques,respectively;showing a reduction by 5.11%and 17%relative to current situation of energy consumption.Optimization of environmental impacts by application of MOGA resulted in reduction of acidification potential(ACP),eutrophication potential(EUP),global warming potential(GWP),human toxicity potential(HTP)and terrestrial ecotoxicity potential(TEP)by 29%,23%,10%,6%and 36%,respectively.MOGAwas capable of reducing the energy consumption from machinery,farmyard manure(FYM)diesel fuel and nitrogen fertilizer(the mostly contributed inputs to the environmental emissions)by 59%,28.5%,24.58%and 11.24%,respectively.Overall,the MOGA technique showed a superior performance relative to DEA approach for optimizing energy inputs and reducing environmental impacts of chickpea production system.展开更多
A climatology of extratropical cyclones （ECs） over East Asia （20~ 75~N, 60^-160~E） is analyzed by applying an improved objective detection and tracking algorithm to the 4-time daily sea level pressure fields from ...A climatology of extratropical cyclones （ECs） over East Asia （20~ 75~N, 60^-160~E） is analyzed by applying an improved objective detection and tracking algorithm to the 4-time daily sea level pressure fields from the European Centre for Medium-range Weather Forecasts （ECMWF） reanalysis data. A total of 12914 EC processes for the period of 1958-2001 are identified, with an EC database integrated and EC activities reanalyzed using the objective algorithm. The results reveal that there are three major cyclogenesis regions： West Siberian Plain, Mongolia （to the south of Lake Baikal）, and the coastal region of East China; whereas significant cyclolysis regions are observed in Siberia north of 60~N, Northeast China, and Okhotsk Se^Northwest Pacific. It is found that the EC lifetime is largely 1 7 days while winter ECs have the shortest lifespan. The ECs are the weakest in summer among the four seasons. Strong ECs often appear in West Siberia, Northeast China, and Okhotsk Sea-Northwest Pacific. Statistical analysis based on k-means clustering has identified 6 dominating trajectories in the area south of 55~N and east of 80~E, among which 4 tracks have important impacts on weather/climate in China. ECs occurring in spring （summer） tend to travel the longest （shortest）. They move the fastest in winter, and the slowest in summer. In winter, cyclones move fast in Northeast China, some areas of the Yangtze-Huaihe River region, and the south of Japan, with speed greater than 15 m s-1. Explosively-deepening cyclones are found to occur frequently along the east coast of China, Japan, and Northwest Pacific, but very few storms occur over the inland area. Bombs prefer to occur in winter, spring, and autumn. Their annual number and intensity in 1990 and 1992 in East Asia （EA） are smaller and weaker than their counterparts in North America.展开更多
Cadra(Ephestia)cautella(Walker)is a moth that attacks dates from ripening stages while on tree,throughout storage,and until consumption,causing enormous qualitative and quantitative damages,resulting in economic losse...Cadra(Ephestia)cautella(Walker)is a moth that attacks dates from ripening stages while on tree,throughout storage,and until consumption,causing enormous qualitative and quantitative damages,resulting in economic losses.Image-processing algorithms were developed for detecting and differentiating between three Cadra egg categories based on the success of Trichogramma bourarachae(Pintureau and Babaul)parasitization.These categories were parasitized(black and dark red),unparasitized fertile unhatched(yellow),and unparasitized hatched(white)eggs.Color,light intensity,and shape information was used to develop detection algorithms.Two image processing methods were developed based on three randomly selected images and were tested on a larger validation image set of 40 images:(i)segmentation and extractions of color and morphological features followed by Watershed delineation,and is referred to as Algorithm 1(ALGO1),(ii)finding circular objects by Hough Transformation followed by convolution filtering,and is referred to as Algorithm 2(ALGO2).ALGO1 and ALGO2 achieved correct classification rates(CCRs)for parasitized eggs of 92%and 96%,respectively.Their CCRs for unhatched eggs were 48%and 94%,and for hatched eggs were 42%and 73%,respectively.Regarding parasitized eggs,both methods performed satisfactorily,but,in general,ALGO2 outperformed ALGO1.These results ensure automatic evaluation of the efficiency of biological control of Cadra cautella by the egg parasitoid Trichogramma bourarachae by quantifying the rate of parasitization.The developed detection methods can be used by producers of biocontrol agents for online monitoring of Trichogramma and similar insect natural enemies during mass production and before release against crop pests.Moreover,with few adjustments these methods can be used in similar applications such as detecting plant diseases.展开更多
The results of experiments on the synthesis of the off-axis quantized kinoforms of binary objects with the use of the weighting iterative Fourier transform （WIFT） algorithm are presented. Kinoforms are registered wi...The results of experiments on the synthesis of the off-axis quantized kinoforms of binary objects with the use of the weighting iterative Fourier transform （WIFT） algorithm are presented. Kinoforms are registered with a liquid-crystal spatial light modulator （SLM）. A simple procedure to introduce the carrier frequency into the structure of an axial kinoform is proposed. An image reconstructed by an off-axis kinoform is free from the noises with the zero and close frequencies caused by the imperfection of both the phase mode of operation of the SLM and the effects of quantization of the registered phase. Data on the diffraction efficiency are also given.展开更多
基金support of RUSA-Phase 2.0 grant sanctioned vide Letter No.F.24-51/2014-U,Policy(TNMulti-Gen),Dep.of Edn.Govt.of India,Dt.09.10.2018.
文摘Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of the total world population.The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains.This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques.Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy.There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind,relative humidity,Instant Wind Speed in paddy cultivation.The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series.A Robust Optimized Artificial Neural Network(ROANN)Algorithm with Genetic Algorithm(GA)and Multi Objective Particle Swarm Optimization Algorithm(MOPSO)proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation.A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database.The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database.Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm.The reason for improving the growth of paddy was identified using the output of the Neural Network.Performance metrics such as Accuracy,Error Rate etc were used to measure the performance of the proposed algorithm.Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield.
基金National Key Research and Development Program of China(Nos.2022YFB4700600 and 2022YFB4700605)National Natural Science Foundation of China(Nos.61771123 and 62171116)+1 种基金Fundamental Research Funds for the Central UniversitiesGraduate Student Innovation Fund of Donghua University,China(No.CUSF-DH-D-2022044)。
文摘Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.
基金supported by a grant from R&D program of the Korea Evaluation Institute of Industrial Technology(20015047).
文摘With the growth of the online market,demand for logistics and courier cargo is increasing rapidly.Accordingly,in the case of urban areas,road congestion and environmental problems due to cargo vehicles are mainly occurring.The joint courier logistics system,a plan to solve this problem,aims to establish an efficient logistics transportation system by utilizing one joint logistics delivery terminal by several logistics and delivery companies.However,several courier companies use different types of courier invoices.Such a system has a problem of information data transmission interruption.Therefore,the data processing process was systematically analyzed,a practically feasible methodology was devised,and delivery invoice information processing standards were established for this.In addition,the importance of this paper can be emphasized in terms of data processing in the logistics sector,which is expected to grow rapidly in the future.The results of this study can be used as basic data for the implementation of the logistics joint delivery terminal system in the future.And it can be used as a basis for securing the operational reliability of the joint courier logistics system.
基金Supported by the National Natural Science Foundation of China(61271373,61571043)111 Project of China(B14010)
文摘An optimizing method of observation scheduling based on time-division multiplexing is proposed in this paper,and its efficiency is verified by outdoor experiments. The initial observation scheduling is first obtained by using a semi-random search algorithm,and secondly the connection time pair（ CTP） between adjacent objects is optimized by using a genetic algorithm. After obtaining these two parameters,the final observation scheduling can be obtained. According to pre-designed tracks between each adjacent objects in observation order,the seamless observation of neighboring targets is derived by automatically steering the antenna beam,so the observation efficiency is improved.
基金supported by the National Natural Science Foundation of China(No.52062027)the Key Research and Development Project of Gansu Province(No.22YF7GA142)+2 种基金the Soft Science Special Project of Gansu Basic Research Plan(No.22JR4ZA035)the Gansu Provincial Science and Technology Major Special Project-Enterprise Innovation Consortium Project(Nos.22ZD6GA010 and 21ZD3GA002)the Natural Science Foundation of Gansu Province(No.22JR5RA343).
文摘With the increasing number of vehicles,there has been an unprecedented pressure on the operation and maintenance of intelligent transportation systems and transportation infrastructure.In order to achieve faster and more accurate identification of traffic vehicles,computer vision and deep learning technology play a vital role and have made significant advancements.This study summarizes the current research status,latest findings,and future development trends of traditional detection algorithms and deep learning-based detection algorithms.Among the detection algorithms based on deep learning,this study focuses on the representative convolutional neural network models.Specifically,it examines the two-stage and one-stage detection algorithms,which have been extensively utilized in the field of intelligent transportation systems.Compared to traditional detection algorithms,deep learning-based detection algorithms can achieve higher accuracy and efficiency.The single-stage detection algorithm is more efficient for real-time detection,while the two-stage detection algorithm is more accurate than the single-stage detection algorithm.In the follow-up research,it is important to consider the balance between detection efficiency and detection accuracy.Additionally,vehicle missed detection and false detection in complex scenes,such as bad weather and vehicle overlap,should be taken into account.This will ensure better application of the research findings in engineering practice.
基金The financial support provided by the University of Tehran,Iran,is duly acknowledged.
文摘Energy consumption in agricultural products and its environmental damages has increased in recent centuries.Life cycle assessment(LCA)has been introduced as a suitable tool for evaluation environmental impacts related to a product over its life cycle.In this study,optimization of energy consumption and environmental impacts of chickpea production was conducted using data envelopment analysis(DEA)and multi objective genetic algorithm(MOGA)techniques.Data were collected from 110 chickpea production enterprises using a face to face questionnaire in the cropping season of 2014-2015.The results of optimization revealed that,when applying MOGA,optimum energy requirement for chickpea production was significantly lower compared to application of DEA technique;so that,total energy requirement in optimum situation was found to be 31511.72 and 27570.61 MJ ha^-1 by using DEA and MOGA techniques,respectively;showing a reduction by 5.11%and 17%relative to current situation of energy consumption.Optimization of environmental impacts by application of MOGA resulted in reduction of acidification potential(ACP),eutrophication potential(EUP),global warming potential(GWP),human toxicity potential(HTP)and terrestrial ecotoxicity potential(TEP)by 29%,23%,10%,6%and 36%,respectively.MOGAwas capable of reducing the energy consumption from machinery,farmyard manure(FYM)diesel fuel and nitrogen fertilizer(the mostly contributed inputs to the environmental emissions)by 59%,28.5%,24.58%and 11.24%,respectively.Overall,the MOGA technique showed a superior performance relative to DEA approach for optimizing energy inputs and reducing environmental impacts of chickpea production system.
基金Supported by the National Science and Technology Support Program of China (2007BAC03A01 and 2009BAC51B01)
文摘A climatology of extratropical cyclones （ECs） over East Asia （20~ 75~N, 60^-160~E） is analyzed by applying an improved objective detection and tracking algorithm to the 4-time daily sea level pressure fields from the European Centre for Medium-range Weather Forecasts （ECMWF） reanalysis data. A total of 12914 EC processes for the period of 1958-2001 are identified, with an EC database integrated and EC activities reanalyzed using the objective algorithm. The results reveal that there are three major cyclogenesis regions： West Siberian Plain, Mongolia （to the south of Lake Baikal）, and the coastal region of East China; whereas significant cyclolysis regions are observed in Siberia north of 60~N, Northeast China, and Okhotsk Se^Northwest Pacific. It is found that the EC lifetime is largely 1 7 days while winter ECs have the shortest lifespan. The ECs are the weakest in summer among the four seasons. Strong ECs often appear in West Siberia, Northeast China, and Okhotsk Sea-Northwest Pacific. Statistical analysis based on k-means clustering has identified 6 dominating trajectories in the area south of 55~N and east of 80~E, among which 4 tracks have important impacts on weather/climate in China. ECs occurring in spring （summer） tend to travel the longest （shortest）. They move the fastest in winter, and the slowest in summer. In winter, cyclones move fast in Northeast China, some areas of the Yangtze-Huaihe River region, and the south of Japan, with speed greater than 15 m s-1. Explosively-deepening cyclones are found to occur frequently along the east coast of China, Japan, and Northwest Pacific, but very few storms occur over the inland area. Bombs prefer to occur in winter, spring, and autumn. Their annual number and intensity in 1990 and 1992 in East Asia （EA） are smaller and weaker than their counterparts in North America.
文摘Cadra(Ephestia)cautella(Walker)is a moth that attacks dates from ripening stages while on tree,throughout storage,and until consumption,causing enormous qualitative and quantitative damages,resulting in economic losses.Image-processing algorithms were developed for detecting and differentiating between three Cadra egg categories based on the success of Trichogramma bourarachae(Pintureau and Babaul)parasitization.These categories were parasitized(black and dark red),unparasitized fertile unhatched(yellow),and unparasitized hatched(white)eggs.Color,light intensity,and shape information was used to develop detection algorithms.Two image processing methods were developed based on three randomly selected images and were tested on a larger validation image set of 40 images:(i)segmentation and extractions of color and morphological features followed by Watershed delineation,and is referred to as Algorithm 1(ALGO1),(ii)finding circular objects by Hough Transformation followed by convolution filtering,and is referred to as Algorithm 2(ALGO2).ALGO1 and ALGO2 achieved correct classification rates(CCRs)for parasitized eggs of 92%and 96%,respectively.Their CCRs for unhatched eggs were 48%and 94%,and for hatched eggs were 42%and 73%,respectively.Regarding parasitized eggs,both methods performed satisfactorily,but,in general,ALGO2 outperformed ALGO1.These results ensure automatic evaluation of the efficiency of biological control of Cadra cautella by the egg parasitoid Trichogramma bourarachae by quantifying the rate of parasitization.The developed detection methods can be used by producers of biocontrol agents for online monitoring of Trichogramma and similar insect natural enemies during mass production and before release against crop pests.Moreover,with few adjustments these methods can be used in similar applications such as detecting plant diseases.
文摘The results of experiments on the synthesis of the off-axis quantized kinoforms of binary objects with the use of the weighting iterative Fourier transform （WIFT） algorithm are presented. Kinoforms are registered with a liquid-crystal spatial light modulator （SLM）. A simple procedure to introduce the carrier frequency into the structure of an axial kinoform is proposed. An image reconstructed by an off-axis kinoform is free from the noises with the zero and close frequencies caused by the imperfection of both the phase mode of operation of the SLM and the effects of quantization of the registered phase. Data on the diffraction efficiency are also given.
