Recycling plastic waste into triboelectric nanogenerators(TENGs)presents a sustainable approach to energy harvesting,self-powered sensing,and environmental remediation.This study investigates the recycling of polyviny...Recycling plastic waste into triboelectric nanogenerators(TENGs)presents a sustainable approach to energy harvesting,self-powered sensing,and environmental remediation.This study investigates the recycling of polyvinyl chloride(PVC)pipe waste polymers into nanofibers(NFs)optimized for TENG applications.We focused on optimizing the morphology of recycled PVC polymer to NFs and enhancing their piezoelectric properties by incorporating ZnO nanoparticles(NPs).The optimized PVC/0.5 wt%ZnO NFs were tested with Nylon-6 NFs,and copper(Cu)electrodes.The Nylon-6 NFs exhibited a power density of 726.3μWcm^(-2)—1.13 times higher than Cu and maintained 90%stability after 172800 cycles,successfully powering various colored LEDs.Additionally,a 3D-designed device was developed to harvest energy from biomechanical movements such as finger tapping,hand tapping,and foot pressing,making it suitable for wearable energy harvesting,automatic switches,and invisible sensors in surveillance systems.This study demonstrates that recycling polymers for TENG devices can effectively address energy,sensor,and environmental challenges.展开更多
In this paper,an image processing algorithm which is able to synthesize material textures of arbitrary shapes is proposed.The presented approach uses an arbitrary image to construct a structure layer of the material.T...In this paper,an image processing algorithm which is able to synthesize material textures of arbitrary shapes is proposed.The presented approach uses an arbitrary image to construct a structure layer of the material.The resulting structure layer is then used to constrain the material texture synthesis.The field of second-moment matrices is used to represent the structure layer.Many tests with various constraint images are conducted to ensure that the proposed approach accurately reproduces the visual aspects of the input material sample.The results demonstrate that the proposed algorithm is able to accurately synthesize arbitrary-shaped material textures while respecting the local characteristics of the exemplar.This paves the way toward the synthesis of 3D material textures of arbitrary shapes from 2D material samples,which has a wide application range in virtual material design and materials characterization.展开更多
The advanced driver assistance system(ADAS)primarily serves to assist drivers in monitoring the speed of the car and helps them make the right decision,which leads to fewer fatal accidents and ensures higher safety.In...The advanced driver assistance system(ADAS)primarily serves to assist drivers in monitoring the speed of the car and helps them make the right decision,which leads to fewer fatal accidents and ensures higher safety.In the artificial Intelligence domain,machine learning(ML)was developed to make inferences with a degree of accuracy similar to that of humans;however,enormous amounts of data are required.Machine learning enhances the accuracy of the decisions taken by ADAS,by evaluating all the data received from various vehicle sensors.This study summarizes all the critical algorithms used in ADAS technologies and presents the evolution of ADAS technology.Initially,ADAS technology is introduced,along with its evolution,to understand the objectives of developing this technology.Subsequently,the critical algorithms used in ADAS technology,which include face detection,head-pose estimation,gaze estimation,and link detection are discussed.A further discussion follows on the impact of ML on each algorithm in different environments,leading to increased accuracy at the expense of additional computing,to increase efficiency.The aim of this study was to evaluate all the methods with or without ML for each algorithm.展开更多
In this paper,Isogeometric analysis(IGA)is effectively integrated with machine learning(ML)to investigate the bearing capacity of strip footings in layered soil profiles,with a focus on a sand-over-clay configuration....In this paper,Isogeometric analysis(IGA)is effectively integrated with machine learning(ML)to investigate the bearing capacity of strip footings in layered soil profiles,with a focus on a sand-over-clay configuration.The study begins with the generation of a comprehensive dataset of 10,000 samples from IGA upper bound(UB)limit analyses,facilitating an in-depth examination of various material and geometric conditions.A hybrid deep neural network,specifically the Whale Optimization Algorithm-Deep Neural Network(WOA-DNN),is then employed to utilize these 10,000 outputs for precise bearing capacity predictions.Notably,the WOA-DNN model outperforms conventional ML techniques,offering a robust and accurate prediction tool.This innovative approach explores a broad range of design parameters,including sand layer depth,load-to-soil unit weight ratio,internal friction angle,cohesion,and footing roughness.A detailed analysis of the dataset reveals the significant influence of these parameters on bearing capacity,providing valuable insights for practical foundation design.This research demonstrates the usefulness of data-driven techniques in optimizing the design of shallow foundations within layered soil profiles,marking a significant stride in geotechnical engineering advancements.展开更多
Scientific evidence sustains PM_(2.5)particles’inhalation may generate harmful impacts on human beings’health;therefore,theirmonitoring in ambient air is of paramount relevance in terms of public health.Due to the l...Scientific evidence sustains PM_(2.5)particles’inhalation may generate harmful impacts on human beings’health;therefore,theirmonitoring in ambient air is of paramount relevance in terms of public health.Due to the limited number of fixed stations within the air qualitymonitoring networks,development ofmethodological frameworks tomodel ambient air PM_(2.5)particles is primordial to providing additional information on PM_(2.5)exposure and its trends.In this sense,this work aims to offer a global easily-applicable tool to estimate ambient air PM_(2.5)as a function of meteorological conditions using a multivariate analysis.Daily PM_(2.5)data measured by 84 fixed monitoring stations and meteorological data from ERA5(ECMWF Reanalysis v5)reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested approach.Data from January 2017 to December 2020 were employed to build amathematical expression that related the dependent variable(PM_(2.5))to predictor ones(sea-level pressure,planetary boundary layer height,temperature,precipitation,wind direction and speed),while 2021 data tested the model.Evaluation indicators evidenced a good performance of model(maximum values of RMSE,MAE and MAPE:1.80μg/m^(3),3.24μg/m^(3),and 20.63%,respectively),compiling the current legislation’s requirements for modelling ambient air PM_(2.5)concentrations.A retrospective analysis of meteorological features allowed estimating ambient air PM_(2.5)concentrations from 2000 to 2021.The highest PM_(2.5)concentrations relapsed in theMid-and Southlands,while Northlands sustained the lowest concentrations.展开更多
The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the develop...The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJCSA-MLPnet.First of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances.This technique is expected to improve the accuracy of friction angle prediction models.The friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,i.e.FPA-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,i.e.YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJ-CSA-MLPnet.Among these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering scenarios.This improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.展开更多
The objective of this work was to develop a dynamic model for describing leaf curves and a the rice leaf （including sub-models for unexpanded leaf blades, expanded leaf blades, and dimensional （3D） dynamic visualiz...The objective of this work was to develop a dynamic model for describing leaf curves and a the rice leaf （including sub-models for unexpanded leaf blades, expanded leaf blades, and dimensional （3D） dynamic visualization of rice leaves by combining relevant models detailed spatial geometry model of leaf sheaths）, and to realize three- Based on the experimental data of different cultivars and nitrogen （N） rates, the time-course spatial data of leaf curves on the main stem were collected during the rice development stage, then a dynamic model of the rice leaf curve was developed using quantitative modeling technology. Further, a detailed 3D geometric model of rice leaves was built based on the spatial geometry technique and the non-uniform rational B-spline （NURBS） method. Validating the rice leaf curve model with independent field experiment data showed that the average distances between observed and predicted curves were less than 0.89 and 1.20 cm at the tilling and jointing stages, respectively. The proposed leaf curve model and leaf spatial geometry model together with the relevant previous models were used to simulate the spatial morphology and the color dynamics of a single leaf and of leaves on the rice plant after different growing days by 3D visualization technology. The validation of the leaf curve model and the results of leaf 3D visualization indicated that our leaf curve model and leaf spatial geometry model could efficiently predict the dynamics of rice leaf spatial morphology during leaf development stages. These results provide a technical support for related research on virtual rice.展开更多
Hybrid organic-inorganic perovskite solar cells(PSCs) are considered to be the most promising thirdgeneration photovoltaic(PV) technology with the most rapid rate of increase in the power conversion efficiency(PCE). T...Hybrid organic-inorganic perovskite solar cells(PSCs) are considered to be the most promising thirdgeneration photovoltaic(PV) technology with the most rapid rate of increase in the power conversion efficiency(PCE). To date, their PCE values are comparable to the established photovoltaic technologies such as crystalline silicon. Intensive research activities associated with PSCs have been being performed,since 2009, aiming to further boost the device performance in terms of efficiency and stability via different strategies in order to accelerate the progress of commercialization. The emerging 2 D black phosphorus(BP) is a novel class of semiconducting material owing to its unique characteristics, allowing them to become attractive materials for applications in a variety of optical and electronic devices, which have been comprehensively reviewed in the literature. However, comprehensive reviews focusing on the application of BP in PSCs are scarce in the community. This review discusses the research works with the incorporation of BP as a functional material in PSCs. The methodology as well as the effects of employing BP in different regions of PSCs are summarized. Further challenges and potential research directions are also highlighted.展开更多
Due to effectiveness of network layer on general performance of networks, designing routing protocols is very important for lifetime and traffic efficiency in wireless sensor networks. So in this paper, we are going t...Due to effectiveness of network layer on general performance of networks, designing routing protocols is very important for lifetime and traffic efficiency in wireless sensor networks. So in this paper, we are going to represent an efficient and scalable version of depth-based routing (DBR) protocol that is limited by depth divisions-policy. In fact the new version is a network information independent routing protocol for acoustic communications. Proposed method by use of depth clustering is able to reduce consumed energy and end-to-end delay in dense underwater sensor networks (DUSNs) and this issue is proved by simulation.展开更多
Smoking is a major cause of cancer,heart disease and other afflictions that lead to early mortality.An effective smoking classification mechanism that provides insights into individual smoking habits would assist in i...Smoking is a major cause of cancer,heart disease and other afflictions that lead to early mortality.An effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment initiatives.Smoking activities often accompany other activities such as drinking or eating.Consequently,smoking activity recognition can be a challenging topic in human activity recognition(HAR).A deep learning framework for smoking activity recognition(SAR)employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules(ResNetSE)to increase the effectiveness of the SAR framework.The proposed model was tested against basic convolutional neural networks(CNNs)and recurrent neural networks(LSTM,BiLSTM,GRU and BiGRU)to recognize smoking and other similar activities such as drinking,eating and walking using the UT-Smoke dataset.Three different scenarios were investigated for their recognition performances using standard HAR metrics(accuracy,F1-score and the area under the ROC curve).Our proposed ResNetSE outperformed the other basic deep learning networks,with maximum accuracy of 98.63%.展开更多
Automatic solution of vehicle operation adjustment is the important content in realizing vehicle traffic command automation on Internet of Things platform. Based on both the organization realization of Internet of Thi...Automatic solution of vehicle operation adjustment is the important content in realizing vehicle traffic command automation on Internet of Things platform. Based on both the organization realization of Internet of Things platform and the merging vehicle operation adjustment into the Flow-Shop scheduling problem in manufacturing systems,this paper has constructed the optimization model with a two-lane vehicle operation adjustment. With respect to the large model solution space and complex constraints,a better solution algorithm is proposed based on ant colony algorithm for optimal quick solution. The simulation results show that the algorithm is feasible and the approximate optimal solution can be quickly obtained.展开更多
An order morphology transform is presented to filter and segment which is done by different percentile. Filter Is done flexibly by different size structure element with several percent. The threshold which for normal ...An order morphology transform is presented to filter and segment which is done by different percentile. Filter Is done flexibly by different size structure element with several percent. The threshold which for normal segment way such as Ostu decides is more lower when a low SNR Image Is processing especially the foreground is small or dot. The foreground can not be identified effectively in those case. Adaptive multl-threshold segment Is defined by percent value of order morphology. Analysis and results indicate that this way is more adaptive to different SNR fluorescence images. It could be applied to process high-density chips.展开更多
This paper studies consensus control problems for a class of second-order multi-agent systems without relative velocity measurement. Some dynamic neighbour-based rules are adopted for the agents in the presence of ext...This paper studies consensus control problems for a class of second-order multi-agent systems without relative velocity measurement. Some dynamic neighbour-based rules are adopted for the agents in the presence of external disturbances. A sufficient condition is derived to make all agents achieve consensus while satisfying desired H∞ performance. Finally, numerical simulations are provided to show the effectiveness of our theoretical results.展开更多
This paper presents an extended Dyna-Q algorithm to improve efficiency of the standard Dyna-Q algorithm.In the first episodes of the standard Dyna-Q algorithm,the agent travels blindly to find a goal position.To overc...This paper presents an extended Dyna-Q algorithm to improve efficiency of the standard Dyna-Q algorithm.In the first episodes of the standard Dyna-Q algorithm,the agent travels blindly to find a goal position.To overcome this weakness,our approach is to use a maximum likelihood model of all state-action pairs to choose actions and update Q-values in the first few episodes.Our algorithm is compared with one-step Q-learning algorithm and the standard Dyna-Q algorithm for the path planning problem in maze environments.Experimental results show that the proposed algorithm is more efficient than the one-step Q-learning algorithm as well as the standard Dyna-Q algorithm,especially in the large environment of states.展开更多
This paper presents the design of a small printed ultra wideband antenna with Band Notched characteristics. Both the free space and on-body performances of this antenna were investigated through simulation. The newly ...This paper presents the design of a small printed ultra wideband antenna with Band Notched characteristics. Both the free space and on-body performances of this antenna were investigated through simulation. The newly designed UWB antenna is more revised small form factor sized, with the ability to avoid interference caused by WLAN (5.15 - 5.825 GHz) and WiMAX (5.25 - 5.85 GHz) systems with a band notch. The return loss response, gain, radiation pattern on free space of the antenna were investigated. After that, the on-body performances were tested on 3-layer human body model with radiation pattern, gain, return loss, and efficiency at 3.5, 5.7, 8, 10 GHz and all the results were compared with free space results. As the on-body performance was very good, the proposed antenna will be suitable to be used for multi-purpose medical applications and sports performance monitoring.展开更多
Estimating the crowd count and density of highly dense scenes witnessed in Muslim gatherings at religious sites in Makkah and Madinah is critical for developing control strategies and organizing such a large gathering...Estimating the crowd count and density of highly dense scenes witnessed in Muslim gatherings at religious sites in Makkah and Madinah is critical for developing control strategies and organizing such a large gathering.Moreover,since the crowd images in this case can range from low density to high density,detection-based approaches are hard to apply for crowd counting.Recently,deep learning-based regression has become the prominent approach for crowd counting problems,where a density-map is estimated,and its integral is further computed to acquire the final count result.In this paper,we put forward a novel multi-scale network(named 2U-Net)for crowd counting in sparse and dense scenarios.The proposed framework,which employs the U-Net architecture,is straightforward to implement,computationally efficient,and has single-step training.Unpooling layers are used to retrieve the pooling layers’erased information and learn hierarchically pixelwise spatial representation.This helps in obtaining feature values,retaining spatial locations,and maximizing data integrity to avoid data loss.In addition,a modified attention unit is introduced and integrated into the proposed 2UNet model to focus on specific crowd areas.The proposed model concentrates on balancing the number of model parameters,model size,computational cost,and counting accuracy compared with other works,which may involve acquiring one criterion at the expense of other constraints.Experiments on five challenging datasets for density estimation and crowd counting have shown that the proposed model is very effective and outperforms comparable mainstream models.Moreover,it counts very well in both sparse and congested crowd scenes.The 2U-Net model has the lowest MAE in both parts(Part A and Part B)of the ShanghaiTech,UCSD,and Mall benchmarks,with 63.3,7.4,1.5,and 1.6,respectively.Furthermore,it obtains the lowest MSE in the ShanghaiTech-Part B,UCSD,and Mall benchmarks with 12.0,1.9,and 2.1,respectively.展开更多
The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingfirm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new wor...The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingfirm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new worker.Various techniques for identifying and detecting worker performance in industrial applications are based on computer vision techniques.Despite widespread com-puter vision-based approaches,it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where cam-era deployment is problematic.Through the use of wearable inertial sensors,we propose a deep learning method for automatically recognizing the activities of construction workers.The suggested method incorporates a convolutional neural network,residual connection blocks,and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as con-struction worker tasks.The proposed approach has been evaluated using standard performance measures,such as precision,F1-score,and AUC,using a publicly available benchmark dataset known as VTT-ConIoT,which contains genuine con-struction work activities.In addition,standard deep learning models(CNNs,RNNs,and hybrid models)were developed in different empirical circumstances to compare them to the proposed model.With an average accuracy of 99.71%and an average F1-score of 99.71%,the experimentalfindings revealed that the suggested model could accurately recognize the actions of construction workers.Furthermore,we examined the impact of window size and sensor position on the identification efficiency of the proposed method.展开更多
Towards virtual keyboard design and realization, the work in this paper presents a robust key input method for deployment in virtual keyboard systems. The proposed scheme harnesses the information contained within sha...Towards virtual keyboard design and realization, the work in this paper presents a robust key input method for deployment in virtual keyboard systems. The proposed scheme harnesses the information contained within shadows towards robustifying virtual key input. This scheme allows for input efficiency to be guaranteed in situations of relatively lower illumination, a core challenge associated with virtual keyboards. Contributions of the paper are two-fold. Firstly the paper pre-sents an approach towards effectively applying shadow information towards robustifying virtual key input systems;Secondly, through morphological operations, the performance of this input method is boosted by means of effectively alleviating noise and its impacts on overall algorithm performance, while highlighting the necessary features towards an efficient performance. While previous contributions have followed a similar trend, the contribution of this paper stresses on the intensification and improvement of both shadow and finger-tip feature highlighting schemes towards overall performance improvement. Experimental results presented in the paper demon-strate the efficiency and robustness of the approach. The attained results suggest that the scheme is capable of attaining high performances in terms of accuracy while being capable of addressing false touch situations.展开更多
In medical diagnostics, therapeutic, laboratory, intensive care unit devices, and machines application, two form of Electrical Energy is utilized. Alternatives current (AC) and Direct current (DC) form. In this paper ...In medical diagnostics, therapeutic, laboratory, intensive care unit devices, and machines application, two form of Electrical Energy is utilized. Alternatives current (AC) and Direct current (DC) form. In this paper an inverter driver system with a display model is made using MATLAB and its specific tool box of Simulink, the process will involve converting single phase alternating current power to direct current using rectifier made from ordinary normal diodes then converted to three phase using three-arm insulated gate bipolar transistors this is commonly known as inverter bridge which is sufficient enough to run three phase loads depending on the application requirement. The system uses a five-level inverter with low levels of distortions and ripples in the equipment output, this increase and improves the performance of the system. Using carefully selected passive and active elements such as capacitor resistors, inductors, diodes, and transistor system in inverter, decreases the number of switches and boosts the efficiency of the system. This inverter drive system helps us to run three phase machines in the health facility at the same frequency of single phase. The inverter system allows a smaller smoothing capacitor in the DC-AC link as proposed. Large smoothing capacitors are conventionally essential in such converters to absorb power ripple at twice the frequency of the power supply. The proposed network topology consists of an indirect matrix converter and an active snubber to absorb the power ripple, and does not necessitate a reactor or large smoothing capacitor. Simulation result is shown using MATLAB software and used to verify system operation principle as well as circuit development and their control mechanism for a single-to-three-phase power inverter system. The results from experiment show that for a 1 kW-class prototype circuit system, the power ripple at twice the frequency of the power supply can be adequately suppressed using a buffer capacitor of low values.展开更多
In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic s...In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation data.The classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS domain.Recently,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual methods.The ability to extract important features is vital in making RST classification more accurate.This work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction models.We used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification performance.Comparative experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art models.The proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt roads.Moreover,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively.展开更多
基金supported by the research projects AP23486880 from the Ministry of Higher EducationScience of the Republic of Kazakhstan and 111024CRP2010,20122022FD4135 from Nazarbayev University.
文摘Recycling plastic waste into triboelectric nanogenerators(TENGs)presents a sustainable approach to energy harvesting,self-powered sensing,and environmental remediation.This study investigates the recycling of polyvinyl chloride(PVC)pipe waste polymers into nanofibers(NFs)optimized for TENG applications.We focused on optimizing the morphology of recycled PVC polymer to NFs and enhancing their piezoelectric properties by incorporating ZnO nanoparticles(NPs).The optimized PVC/0.5 wt%ZnO NFs were tested with Nylon-6 NFs,and copper(Cu)electrodes.The Nylon-6 NFs exhibited a power density of 726.3μWcm^(-2)—1.13 times higher than Cu and maintained 90%stability after 172800 cycles,successfully powering various colored LEDs.Additionally,a 3D-designed device was developed to harvest energy from biomechanical movements such as finger tapping,hand tapping,and foot pressing,making it suitable for wearable energy harvesting,automatic switches,and invisible sensors in surveillance systems.This study demonstrates that recycling polymers for TENG devices can effectively address energy,sensor,and environmental challenges.
文摘In this paper,an image processing algorithm which is able to synthesize material textures of arbitrary shapes is proposed.The presented approach uses an arbitrary image to construct a structure layer of the material.The resulting structure layer is then used to constrain the material texture synthesis.The field of second-moment matrices is used to represent the structure layer.Many tests with various constraint images are conducted to ensure that the proposed approach accurately reproduces the visual aspects of the input material sample.The results demonstrate that the proposed algorithm is able to accurately synthesize arbitrary-shaped material textures while respecting the local characteristics of the exemplar.This paves the way toward the synthesis of 3D material textures of arbitrary shapes from 2D material samples,which has a wide application range in virtual material design and materials characterization.
文摘The advanced driver assistance system(ADAS)primarily serves to assist drivers in monitoring the speed of the car and helps them make the right decision,which leads to fewer fatal accidents and ensures higher safety.In the artificial Intelligence domain,machine learning(ML)was developed to make inferences with a degree of accuracy similar to that of humans;however,enormous amounts of data are required.Machine learning enhances the accuracy of the decisions taken by ADAS,by evaluating all the data received from various vehicle sensors.This study summarizes all the critical algorithms used in ADAS technologies and presents the evolution of ADAS technology.Initially,ADAS technology is introduced,along with its evolution,to understand the objectives of developing this technology.Subsequently,the critical algorithms used in ADAS technology,which include face detection,head-pose estimation,gaze estimation,and link detection are discussed.A further discussion follows on the impact of ML on each algorithm in different environments,leading to increased accuracy at the expense of additional computing,to increase efficiency.The aim of this study was to evaluate all the methods with or without ML for each algorithm.
文摘In this paper,Isogeometric analysis(IGA)is effectively integrated with machine learning(ML)to investigate the bearing capacity of strip footings in layered soil profiles,with a focus on a sand-over-clay configuration.The study begins with the generation of a comprehensive dataset of 10,000 samples from IGA upper bound(UB)limit analyses,facilitating an in-depth examination of various material and geometric conditions.A hybrid deep neural network,specifically the Whale Optimization Algorithm-Deep Neural Network(WOA-DNN),is then employed to utilize these 10,000 outputs for precise bearing capacity predictions.Notably,the WOA-DNN model outperforms conventional ML techniques,offering a robust and accurate prediction tool.This innovative approach explores a broad range of design parameters,including sand layer depth,load-to-soil unit weight ratio,internal friction angle,cohesion,and footing roughness.A detailed analysis of the dataset reveals the significant influence of these parameters on bearing capacity,providing valuable insights for practical foundation design.This research demonstrates the usefulness of data-driven techniques in optimizing the design of shallow foundations within layered soil profiles,marking a significant stride in geotechnical engineering advancements.
基金supported by the Collaborative Research Project(CRP)grant,Nazarbayev University(Nos.11022021CRP1512,211123CRP1604)PK acknowledges the support from the NERC-funded projects ASAP-Delhi(NE/P016510/1),GreenCities(NE/P016510/1)RECLAIM Network Plus(EP/W034034/1).
文摘Scientific evidence sustains PM_(2.5)particles’inhalation may generate harmful impacts on human beings’health;therefore,theirmonitoring in ambient air is of paramount relevance in terms of public health.Due to the limited number of fixed stations within the air qualitymonitoring networks,development ofmethodological frameworks tomodel ambient air PM_(2.5)particles is primordial to providing additional information on PM_(2.5)exposure and its trends.In this sense,this work aims to offer a global easily-applicable tool to estimate ambient air PM_(2.5)as a function of meteorological conditions using a multivariate analysis.Daily PM_(2.5)data measured by 84 fixed monitoring stations and meteorological data from ERA5(ECMWF Reanalysis v5)reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested approach.Data from January 2017 to December 2020 were employed to build amathematical expression that related the dependent variable(PM_(2.5))to predictor ones(sea-level pressure,planetary boundary layer height,temperature,precipitation,wind direction and speed),while 2021 data tested the model.Evaluation indicators evidenced a good performance of model(maximum values of RMSE,MAE and MAPE:1.80μg/m^(3),3.24μg/m^(3),and 20.63%,respectively),compiling the current legislation’s requirements for modelling ambient air PM_(2.5)concentrations.A retrospective analysis of meteorological features allowed estimating ambient air PM_(2.5)concentrations from 2000 to 2021.The highest PM_(2.5)concentrations relapsed in theMid-and Southlands,while Northlands sustained the lowest concentrations.
文摘The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJCSA-MLPnet.First of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances.This technique is expected to improve the accuracy of friction angle prediction models.The friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,i.e.FPA-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,i.e.YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJ-CSA-MLPnet.Among these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering scenarios.This improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.
基金supported by the National High-Tech R&D Program of China (2013AA100404)the National Natural Science Foundation of China (31201130,61471269,31571566)+3 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD),Chinathe Natural Science Foundation of Shandong Province,China (BS2015DX001)the Science and Technology Development Project of Weifang,China (2016GX019)the Doctoral Foundation of Weifang University,China
文摘The objective of this work was to develop a dynamic model for describing leaf curves and a the rice leaf （including sub-models for unexpanded leaf blades, expanded leaf blades, and dimensional （3D） dynamic visualization of rice leaves by combining relevant models detailed spatial geometry model of leaf sheaths）, and to realize three- Based on the experimental data of different cultivars and nitrogen （N） rates, the time-course spatial data of leaf curves on the main stem were collected during the rice development stage, then a dynamic model of the rice leaf curve was developed using quantitative modeling technology. Further, a detailed 3D geometric model of rice leaves was built based on the spatial geometry technique and the non-uniform rational B-spline （NURBS） method. Validating the rice leaf curve model with independent field experiment data showed that the average distances between observed and predicted curves were less than 0.89 and 1.20 cm at the tilling and jointing stages, respectively. The proposed leaf curve model and leaf spatial geometry model together with the relevant previous models were used to simulate the spatial morphology and the color dynamics of a single leaf and of leaves on the rice plant after different growing days by 3D visualization technology. The validation of the leaf curve model and the results of leaf 3D visualization indicated that our leaf curve model and leaf spatial geometry model could efficiently predict the dynamics of rice leaf spatial morphology during leaf development stages. These results provide a technical support for related research on virtual rice.
基金the Scientific Research Grant from Ministry of Education and Science of the Republic of Kazakhstan(AP08856931)the Nazarbayev University(110119FD4506,021220CRP0422)。
文摘Hybrid organic-inorganic perovskite solar cells(PSCs) are considered to be the most promising thirdgeneration photovoltaic(PV) technology with the most rapid rate of increase in the power conversion efficiency(PCE). To date, their PCE values are comparable to the established photovoltaic technologies such as crystalline silicon. Intensive research activities associated with PSCs have been being performed,since 2009, aiming to further boost the device performance in terms of efficiency and stability via different strategies in order to accelerate the progress of commercialization. The emerging 2 D black phosphorus(BP) is a novel class of semiconducting material owing to its unique characteristics, allowing them to become attractive materials for applications in a variety of optical and electronic devices, which have been comprehensively reviewed in the literature. However, comprehensive reviews focusing on the application of BP in PSCs are scarce in the community. This review discusses the research works with the incorporation of BP as a functional material in PSCs. The methodology as well as the effects of employing BP in different regions of PSCs are summarized. Further challenges and potential research directions are also highlighted.
文摘Due to effectiveness of network layer on general performance of networks, designing routing protocols is very important for lifetime and traffic efficiency in wireless sensor networks. So in this paper, we are going to represent an efficient and scalable version of depth-based routing (DBR) protocol that is limited by depth divisions-policy. In fact the new version is a network information independent routing protocol for acoustic communications. Proposed method by use of depth clustering is able to reduce consumed energy and end-to-end delay in dense underwater sensor networks (DUSNs) and this issue is proved by simulation.
基金support provided by Thammasat University Research fund under the TSRI,Contract No.TUFF19/2564 and TUFF24/2565,for the project of“AI Ready City Networking in RUN”,based on the RUN Digital Cluster collaboration schemeThis research project was also supported by the Thailand Science Research and Innonation fund,the University of Phayao(Grant No.FF65-RIM041)supported by King Mongkut’s University of Technology North Bangkok,Contract No.KMUTNB-65-KNOW-02.
文摘Smoking is a major cause of cancer,heart disease and other afflictions that lead to early mortality.An effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment initiatives.Smoking activities often accompany other activities such as drinking or eating.Consequently,smoking activity recognition can be a challenging topic in human activity recognition(HAR).A deep learning framework for smoking activity recognition(SAR)employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules(ResNetSE)to increase the effectiveness of the SAR framework.The proposed model was tested against basic convolutional neural networks(CNNs)and recurrent neural networks(LSTM,BiLSTM,GRU and BiGRU)to recognize smoking and other similar activities such as drinking,eating and walking using the UT-Smoke dataset.Three different scenarios were investigated for their recognition performances using standard HAR metrics(accuracy,F1-score and the area under the ROC curve).Our proposed ResNetSE outperformed the other basic deep learning networks,with maximum accuracy of 98.63%.
基金Sponsored by the Natural Science Foundation of Shandong Province(Grant No.ZR2011FL006)2012 International Cooperation Training Fund of Outstanding Young Backbone Teachers of Colleges and Universities in Shandong Province,and Shandong Province Science,2012 Shandong ProvinceSpark Program and Technology Development Plan(Grant No.2011YD01044)
文摘Automatic solution of vehicle operation adjustment is the important content in realizing vehicle traffic command automation on Internet of Things platform. Based on both the organization realization of Internet of Things platform and the merging vehicle operation adjustment into the Flow-Shop scheduling problem in manufacturing systems,this paper has constructed the optimization model with a two-lane vehicle operation adjustment. With respect to the large model solution space and complex constraints,a better solution algorithm is proposed based on ant colony algorithm for optimal quick solution. The simulation results show that the algorithm is feasible and the approximate optimal solution can be quickly obtained.
文摘An order morphology transform is presented to filter and segment which is done by different percentile. Filter Is done flexibly by different size structure element with several percent. The threshold which for normal segment way such as Ostu decides is more lower when a low SNR Image Is processing especially the foreground is small or dot. The foreground can not be identified effectively in those case. Adaptive multl-threshold segment Is defined by percent value of order morphology. Analysis and results indicate that this way is more adaptive to different SNR fluorescence images. It could be applied to process high-density chips.
基金supported by the National High Technology Research and Development Program of China (Grant Nos. 2007AA041104,2007AA041105 and 2007AA04Z163)
文摘This paper studies consensus control problems for a class of second-order multi-agent systems without relative velocity measurement. Some dynamic neighbour-based rules are adopted for the agents in the presence of external disturbances. A sufficient condition is derived to make all agents achieve consensus while satisfying desired H∞ performance. Finally, numerical simulations are provided to show the effectiveness of our theoretical results.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,Science and Technology(2010-0012609)
文摘This paper presents an extended Dyna-Q algorithm to improve efficiency of the standard Dyna-Q algorithm.In the first episodes of the standard Dyna-Q algorithm,the agent travels blindly to find a goal position.To overcome this weakness,our approach is to use a maximum likelihood model of all state-action pairs to choose actions and update Q-values in the first few episodes.Our algorithm is compared with one-step Q-learning algorithm and the standard Dyna-Q algorithm for the path planning problem in maze environments.Experimental results show that the proposed algorithm is more efficient than the one-step Q-learning algorithm as well as the standard Dyna-Q algorithm,especially in the large environment of states.
文摘This paper presents the design of a small printed ultra wideband antenna with Band Notched characteristics. Both the free space and on-body performances of this antenna were investigated through simulation. The newly designed UWB antenna is more revised small form factor sized, with the ability to avoid interference caused by WLAN (5.15 - 5.825 GHz) and WiMAX (5.25 - 5.85 GHz) systems with a band notch. The return loss response, gain, radiation pattern on free space of the antenna were investigated. After that, the on-body performances were tested on 3-layer human body model with radiation pattern, gain, return loss, and efficiency at 3.5, 5.7, 8, 10 GHz and all the results were compared with free space results. As the on-body performance was very good, the proposed antenna will be suitable to be used for multi-purpose medical applications and sports performance monitoring.
基金This research work is supported by the Deputyship of Research&Innovation,Ministry of Education in Saudi Arabia(Grant Number 758).
文摘Estimating the crowd count and density of highly dense scenes witnessed in Muslim gatherings at religious sites in Makkah and Madinah is critical for developing control strategies and organizing such a large gathering.Moreover,since the crowd images in this case can range from low density to high density,detection-based approaches are hard to apply for crowd counting.Recently,deep learning-based regression has become the prominent approach for crowd counting problems,where a density-map is estimated,and its integral is further computed to acquire the final count result.In this paper,we put forward a novel multi-scale network(named 2U-Net)for crowd counting in sparse and dense scenarios.The proposed framework,which employs the U-Net architecture,is straightforward to implement,computationally efficient,and has single-step training.Unpooling layers are used to retrieve the pooling layers’erased information and learn hierarchically pixelwise spatial representation.This helps in obtaining feature values,retaining spatial locations,and maximizing data integrity to avoid data loss.In addition,a modified attention unit is introduced and integrated into the proposed 2UNet model to focus on specific crowd areas.The proposed model concentrates on balancing the number of model parameters,model size,computational cost,and counting accuracy compared with other works,which may involve acquiring one criterion at the expense of other constraints.Experiments on five challenging datasets for density estimation and crowd counting have shown that the proposed model is very effective and outperforms comparable mainstream models.Moreover,it counts very well in both sparse and congested crowd scenes.The 2U-Net model has the lowest MAE in both parts(Part A and Part B)of the ShanghaiTech,UCSD,and Mall benchmarks,with 63.3,7.4,1.5,and 1.6,respectively.Furthermore,it obtains the lowest MSE in the ShanghaiTech-Part B,UCSD,and Mall benchmarks with 12.0,1.9,and 2.1,respectively.
基金supported by University of Phayao(Grant No.FF66-UoE001)Thailand Science Research and Innovation Fund+1 种基金National Science,Research and Innovation Fund(NSRF)King Mongkut’s University of Technology North Bangkok with Contract No.KMUTNB-FF-65-27.
文摘The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingfirm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new worker.Various techniques for identifying and detecting worker performance in industrial applications are based on computer vision techniques.Despite widespread com-puter vision-based approaches,it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where cam-era deployment is problematic.Through the use of wearable inertial sensors,we propose a deep learning method for automatically recognizing the activities of construction workers.The suggested method incorporates a convolutional neural network,residual connection blocks,and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as con-struction worker tasks.The proposed approach has been evaluated using standard performance measures,such as precision,F1-score,and AUC,using a publicly available benchmark dataset known as VTT-ConIoT,which contains genuine con-struction work activities.In addition,standard deep learning models(CNNs,RNNs,and hybrid models)were developed in different empirical circumstances to compare them to the proposed model.With an average accuracy of 99.71%and an average F1-score of 99.71%,the experimentalfindings revealed that the suggested model could accurately recognize the actions of construction workers.Furthermore,we examined the impact of window size and sensor position on the identification efficiency of the proposed method.
文摘Towards virtual keyboard design and realization, the work in this paper presents a robust key input method for deployment in virtual keyboard systems. The proposed scheme harnesses the information contained within shadows towards robustifying virtual key input. This scheme allows for input efficiency to be guaranteed in situations of relatively lower illumination, a core challenge associated with virtual keyboards. Contributions of the paper are two-fold. Firstly the paper pre-sents an approach towards effectively applying shadow information towards robustifying virtual key input systems;Secondly, through morphological operations, the performance of this input method is boosted by means of effectively alleviating noise and its impacts on overall algorithm performance, while highlighting the necessary features towards an efficient performance. While previous contributions have followed a similar trend, the contribution of this paper stresses on the intensification and improvement of both shadow and finger-tip feature highlighting schemes towards overall performance improvement. Experimental results presented in the paper demon-strate the efficiency and robustness of the approach. The attained results suggest that the scheme is capable of attaining high performances in terms of accuracy while being capable of addressing false touch situations.
文摘In medical diagnostics, therapeutic, laboratory, intensive care unit devices, and machines application, two form of Electrical Energy is utilized. Alternatives current (AC) and Direct current (DC) form. In this paper an inverter driver system with a display model is made using MATLAB and its specific tool box of Simulink, the process will involve converting single phase alternating current power to direct current using rectifier made from ordinary normal diodes then converted to three phase using three-arm insulated gate bipolar transistors this is commonly known as inverter bridge which is sufficient enough to run three phase loads depending on the application requirement. The system uses a five-level inverter with low levels of distortions and ripples in the equipment output, this increase and improves the performance of the system. Using carefully selected passive and active elements such as capacitor resistors, inductors, diodes, and transistor system in inverter, decreases the number of switches and boosts the efficiency of the system. This inverter drive system helps us to run three phase machines in the health facility at the same frequency of single phase. The inverter system allows a smaller smoothing capacitor in the DC-AC link as proposed. Large smoothing capacitors are conventionally essential in such converters to absorb power ripple at twice the frequency of the power supply. The proposed network topology consists of an indirect matrix converter and an active snubber to absorb the power ripple, and does not necessitate a reactor or large smoothing capacitor. Simulation result is shown using MATLAB software and used to verify system operation principle as well as circuit development and their control mechanism for a single-to-three-phase power inverter system. The results from experiment show that for a 1 kW-class prototype circuit system, the power ripple at twice the frequency of the power supply can be adequately suppressed using a buffer capacitor of low values.
基金funded by National Research Council of Thailand (NRCT):An Integrated Road Safety Innovations of Pedestrian Crossing for Mortality and Injuries Reduction Among All Groups of Road Users,Contract No.N33A650757supported by the Thailand Science Research and Innovation Fund+1 种基金the University of Phayao (Grant No.FF66-UoE001)King Mongkut’s University of Technology North Bangkok underContract No.KMUTNB-66-KNOW-05.
文摘In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation data.The classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS domain.Recently,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual methods.The ability to extract important features is vital in making RST classification more accurate.This work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction models.We used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification performance.Comparative experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art models.The proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt roads.Moreover,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively.
