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The exponential increment of commodity exchange has raised the need for maritime border security in recent years. One of the most critical tasks for naval border security is ship detection inside and outside the territorial sea. Conventionally, the task requires a substantial human workload. Fortunately, with the rapid growth of the digital camera and deep-learning technique, computer programs can handle object detection tasks well enough to replace human labor. Therefore, this paper studies how to apply recent state-of-the-art deep-learning networks to the ship detection task. We found that with a suitable number of object queries, the Deformable-DETR method will improve the performance compared to the state-of-the-art ship detector. Moreover, comprehensive experiments on different scale datasets prove that the technique can significantly improve the results when the training sample is limited. Last but not least, feature maps given by the method will focus well on key objects in the image.

The purpose of this study is to uncover evidence on the factors influencing switching intention from home delivery to smart lockers in the last-mile delivery service. The research model is constructed based on the Pull-Push-Mooring Theory and the Customer Perceived Value Theory using structural equation modelling to analyze data collected from 557 smart locker users in Vietnam. The results indicated a significantly positive influence of pull factors (convenience, environmental friendliness, and security) and push factors (delivery failure experience and risk), and confirmed that mooring factors (habit and switching cost) negatively impacted customers’ intention to switch from home delivery to smart lockers. The study also revealed that mooring factors moderate the relationship between pull factors and the intention to switch. Moreover, gender, age and frequency of shopping online are significant to switching intention, and usefulness mediates between them and switching intention. Several managerial implications were suggested for stakeholders in order to enhance customers’ switching intentions to use smart lockers, thereby improving the quality, efficiency, and sustainability of the last-mile delivery service in the future.

In this study, a finite-time disturbance observer (FTDOB) with a new structure is originally put forward for the motion tracking problem of a class of nonlinear systems subject to model uncertainties and exogenous disturbances. Compared to existing disturbance estimator designs in the literature, in which the estimation error only converges to the origin asymptotically under assumptions that the first and/or second derivatives are vanishing, the suggested DOB is able to estimate the disturbance exactly in finite time. Firstly, uncertainties (parametric and unstructured uncertainties), unknown dynamics, and external disturbances in system dynamics are lumped into a generalized disturbance term that is subsequently estimated by the proposed DOB. Based on this, a DOB-based backstepping controller is synthesized to ensure high-accuracy tracking performance under various working conditions. The stability analysis of not only the DOB but also the overall closed-loop system is theoretically confirmed by the Lyapunov stability theory. Finally, the advantages of the proposed FTDOB and the FTDOB-based controller over other DOBs and existing DOB-based controllers are explicitly simultaneously demonstrated by a series of numerical simulations on a second-order mechanical system and comparative experiments on an actual DC motor system.

This article proposes a novel real-time optimization-based energy management strategy (EMS) for proton membrane exchange fuel cell (PEMFC)-battery-supercapacitors-driven hybridized-electric tramways (HETs). The proposed algorithm is derived based on an enhanced extremum seeking (ES) algorithm, with a new equivalent state-of-charge (SOC) and a new adaptive co-state introduced. Thereby, optimized reference power for each power source can be distributed appropriately when using three components. The workability and prominent of the proposed technique are demonstrated through comparative simulations with fuzzy-rule-based EMS (FEMS) and equivalent consumption minimization strategy (ECMS) in two case studies: with and without considering the supercapacitors, as an important factor in the EMS design to stabilize the SOC of energy storage devices (ESDs). Briefly, under the proposed ES-based method, the PEMFC power can be regulated such that high-efficiency can be performed, approximately by 46.7%. Subsequently, the hydrogen consumption is reduced about 31.2% compared to a comparative fuzzy-based EMS. Besides, the supplements’ SOCs at the end of a driving cycle are also regulated to be equal to the initial ones.

Hybrid construction machinery (HCM), in recent times, has been widely used in building and other industries due to its outstanding advantages in reducing greenhouse gas (GHG) emissions, preventing harmful impacts on the environment, and limiting fossil fuel consumption. Many scholars have demonstrated that the development of an energy management strategy (EMS) is important to determine sufficient power distribution and ensure output performance for HCM. Although conventional EMSs can deliver the required energy effectively, advances in technology indicate that the optimization of EMS is indispensable to optimize fuel consumption, enhance working efficiency, cost-saving, and prolong the lifetime of devices. Aiming to provide a study on the relevant works and prospects, this paper presents a comprehensive review of optimization-based EMS techniques for types of engine hybrid and fuel cell hybrid construction machinery to deal with the existing limitations and make an underpinning for future improvements. First, HCM configurations are briefly described including engine hybrid and fuel cell hybrid construction machinery. Second, the optimization-based EMSs for engine hybrid configuration are clearly analyzed with the optimization rules, objectives, and parameters. Third, optimization techniques of EMSs for fuel cell hybrid configuration are reviewed involving global optimization-based, local optimization-based, and the combination of global and local optimization-based. Finally, technological challenges, commercial opportunities, and recommendations for future development are discussed to contribute more ideas for the innovation of optimization-based EMSs of HCM.

Abstract Background Circular RNA (circRNA), a class of RNA molecule with a loop structure, has recently attracted researchers due to its diverse biological functions and potential biomarkers of human diseases. Most of the current circRNA detection methods from RNA-sequencing (RNA-Seq) data utilize the mapping information of paired-end (PE) reads to eliminate false positives. However, much of the practical RNA-Seq data such as cross-linking immunoprecipitation sequencing (CLIP-Seq) data usually contain single-end (SE) reads. It is not clear how well these tools perform on SE RNA-Seq data. Results In this study, we present a systematic evaluation of six advanced RNA-based methods and two CLIP-Seq based methods for detecting circRNAs from SE RNA-Seq data. The performances of the methods are rigorously assessed based on precision, sensitivity, F1 score, and true discovery rate. We investigate the impacts of read length, false positive ratio, sequencing depth and PE mapping information on the performances of the methods using simulated SE RNA-Seq simulated datasets. The real datasets used in this study consist of four experimental RNA-Seq datasets with ≥100bp read length and 124 CLIP-Seq samples from 45 studies that contain mostly short-read (≤50bp) RNA-Seq data. The simulation study shows that the sensitivities of most of the methods can be improved by increasing either read length or sequencing depth, and that the levels of false positive rates significantly affect the precision of all methods. Furthermore, the PE mapping information can improve the method’s precision but can not always guarantee the increase of F1 score. Overall, no method is dominant for all SE RNA-Seq data. The RNA-based methods perform better for the long-read datasets but are worse for the short-read datasets. In contrast, the CLIP-Seq based methods outperform the RNA-Seq based methods for all the short-read samples. Combining the results of these methods can significantly improve precision in the CLIP-Seq data. Conclusions The results provide a systematic evaluation of circRNA detection methods on SE RNA-Seq data that would facilitate researchers’ strategies in circRNA analysis.

This paper develops a novel output feedback control scheme for the motion-tracking problem of an electro-hydrostatic actuator (EHA) in the presence of model uncertainties and external disturbances. Firstly, a simplified third-order system model of the studied EHA is established using theoretical methods. For the first time, an extended sliding mode observer (ESMO) is introduced to simultaneously account for the shortage of unknown system states and modeling imperfections. Based on this, a robust nonlinear controller is developed using the backstepping control framework to stabilize the closed-loop system. This controller integrates estimates of immeasurable system states and lumped disturbances to deal with their adverse impacts. Moreover, the dynamic surface control (DSC) technique is employed to effectively mitigate the computational burden of the traditional backstepping framework. An ultimately uniformly bounded (UUB) performance is assured by using the recommended method. Furthermore, the stability of not only the observer but also the closed-loop system is concretely analyzed by using the Lyapunov theory. Finally, experiment results under various working scenarios are given to convincingly demonstrate the advantage of the suggested method in comparison with some reference control approaches.

In this paper, a nonlinear functional observer (NFO) is first proposed for the control design of robot manipulators under model uncertainties, external disturbances, and a lack of joint velocity information. In principle, the proposed NFO can estimate not only lumped disturbances and uncertainties but also unmeasurable joint velocities, which are then fed back into the main controller. Compared to the well-known ESO design, the proposed NFO has a simpler structure, more accurate estimations, and less computational effort, and consequently, it is easier for practical implementation. Moreover, unnecessary observations of joint displacements are avoided when compared to the well-known extended state observer (ESO). Based on the Lyapunov theory, globally uniformly ultimately bounded estimation performance is guaranteed by the proposed NFO. Consequently, it is theoretically proven that the estimation performances of the NFO are better than those of the ESO. Simulations with a two-degree-of-freedom (2-DOF) robot manipulator are conducted to verify the effectiveness of the proposed algorithm in terms of not only the estimation performance but also the closed-loop control performance.

The kinetics for the procedure of preparing iron nanopowder from α-FeOOH by hydrogen reduction under non-isothermal conditions were studied. The reduction of α-FeOOH nanopowder was shown to occur within the temperature range from 180 to 550 °С, with a maximum specific rate value attained at 500 °С. The activation energy for the reduction process α-FeOOH nanopowder was measured to be ~43 kJ/mol, evidencing a mixed reaction mode. Performing the reduction of α-FeOOH at 500 °С accelerated the process while ensuring the required properties of the product obtained. The Fe nanoparticles thus prepared were of rounded shape, the size ranging from 70 to 100 nm.

In this paper, a novel simple, but effective output feedback robust control (OFRC) for achieving a highly accurate position tracking of a pump-controlled electro-hydraulic system is presented. To cope with the unavailability of all system state information, an extended state observer (ESO) was adopted to estimate the angular velocity and load-pressure-related state variable of the actuator and total matched disturbance, which enters the system through the same channel as the control input in the system dynamics. In addition, for the first time, another ESO acting as a disturbance observer (DOB) was skillfully integrated to effectively compensate for the adverse effects of the lumped mismatched uncertainty caused by parameter perturbation and external loads in the velocity dynamics. Then, a dynamic surface-control-based backstepping controller (DSC-BC) based on the constructed ESOs for the tracking control of the studied electro-hydraulic system was synthesized to guarantee that the system output closely tracks the desired trajectory and avoid the inherent computational burden of the conventional backstepping method because of repetitive analytical derivative calculation at each backstepping iteration. Furthermore, the stability of the two observes and overall closed-loop system was verified by using the Lyapunov theory. Finally, several extensive comparative experiments were carried out to demonstrate the advantage of the recommended control approach in comparison with some reference control methods.

Trong bài báo này, chúng tôi nghiên cứu chế tạo điện cực có cấu trúc 3D phân lớp dị thể (cây – cành – nhánh) CdS/ZnO/Pt/WO3 ứng dụng cho tách nước quang điện hóa. Điện cực được tổng hợp bằng phương pháp thủy nhiệt và lắng đọng lớp nguyên tử. Hình thái học, cấu trúc tinh thể, và thành phần nguyên tố của điện cực này được nghiên cứu bằng kính hiển vi điện tử quét (FE–SEM), kính hiển vị điện tử truyền qua (HR–TEM) và nhiễu xạ tia X (XRD). Nghiên cứu tính chất quang điện hóa của cấu trúc CdS/ZnO/Pt/WO3, chúng tôi thu được mật độ dòng quang điện là 8,5 mA·cm-2 và hiệu suất chuyển đổi năng lượng ánh sáng mặt trời thành năng lượng hydro 7,9 %% tại thế cung cấp –0,85 V trong dung dịch chất điện ly Na2S với nồng độ 0,5 mol/L. Cấu trúc 3D phân lớp này có mật độ dòng quang điện của điện cực cao hơn gấp hai lần so với các cấu trúc CdS/ZnO trên điện cực thủy tinh phủ oxit thiếc pha tạp bằng flo. Đây là một hướng tiếp cận rất hứa hẹn tổng hợp các cấu trúc nano phân lớp dị thể nhằm nâng cao hiệu quả sản xuất hydro.

This paper presents the results of an experimental study to measure the shortening of reinforced concrete (RC) columns under long-term maintaining concentric axial load. Long-term axial deformation due to shrinkage and creep of the concrete were recorded beside deformation due to mechanical load. Eight RC cylinder - columns (content of reinforcement 1.5% and 2%) with diameter of 150 mm and height of 600 mm were tested during the period of 600 days to determine their shortening. The experimental results showed that the long-term deformation of RC columns occurs primarily during the first year of loading. The deformation creep of concrete is much greater than the shrinkage deformation. The reinforcement content has a significant effect on the long-term deformation of concrete columns.

This paper aims to provide a novel control framework for exactly regulating the output voltage of floating interleaved boost converters (FIBCs), which have been widely employed in fuel cell applications in recent years. Firstly, a mathematical model of the FIBC is constructed according to Kirchhoff’s current and voltage loop principles. Then a cascade control structure with a current inner loop and voltage outer loop is developed to achieve the desired voltage regulation performance. The current controller is established based on the generalized super-twisting algorithm (GSTA) to ensure that the inductor current exactly follows the current reference, which is generated by the outer loop. Meanwhile, an active disturbance rejection control (ADRC) framework is utilized for robustly regulating the output voltage despite the presence of input variation and load change in the voltage control loop based on a nonlinear continuous GSTA-based extended state observer (GSTA-based ESO). The stability of a closed loop system based on the GSTA controller and the GSTA-based ESO is conclusively proven using the Lyapunov theory. The Simscape model of the FIBC is developed, which is used to verify the feasibility and the appropriateness of the recommended control algorithm. Finally, numerical simulations are carried out to demonstrate the effectiveness of the proposed method compared to several previous works.

This paper proposes a novel active disturbance compensation framework for exactly positioning control of electro-hydraulic systems (EHSs) subject to parameter deviations, unknown dynamics, and uncertain external load without velocity measurement mechanism. In order to accurately estimate and then actively compensate for the effects of these uncertainties and disturbances on the system dynamics, a combination between an extended sliding mode observer (ESMO) and a linear extended state observer (LESO) is firstly established for position control of EHSs. In addition, an inherited nonlinear filter-based trajectory planner with minor modifications is utilized to overcome the barriers of inappropriate desired trajectories which do not consider the system kinematic and dynamic constraints. Furthermore, for the first time, the command filtered (CF) approach and prescribed performance control (PPC) are successfully coordinated together and dexterously integrated into the backstepping framework to not only mitigate the computational cost significantly and avoid the “explosion of complexity” of the traditional backstepping design but also satisfy the predetermined transient tracking performance indexes including convergence rate, overshoot, and steady-state error. The stabilities of the observers and overall closed-loop system are rigorously proven by using the Lyapunov theory. Finally, comparative numerical simulations are conducted to demonstrate the advantages of the proposed approach.

Heart disease classification plays an important role in clinical diagnoses. The performance improvement of an Electrocardiogram classifier is therefore of great relevance, but it is a challenging task too. This paper proposes a novel classification algorithm using the kernel method. A kernel is constructed based on wavelet coefficients of heartbeat signals for a classifier with high performance. In particular, a wavelet packet decomposition algorithm is applied to heartbeat signals to obtain the Approximation and Detail coefficients, which are used to calculate the parameters of the kernel. A principal component analysis algorithm with the wavelet-based kernel is employed to choose the main features of the heartbeat signals for the input of the classifier. In addition, a neural network with three hidden layers in the classifier is utilized for classifying five types of heart disease. The electrocardiogram signals in nine patients obtained from the MIT-BIH database are used to test the proposed classifier. In order to evaluate the performance of the classifier, a multi-class confusion matrix is applied to produce the performance indexes, including the Accuracy, Recall, Precision, and F1 score. The experimental results show that the proposed method gives good results for the classification of the five mentioned types of heart disease.

Abstract Recently, with the express growth of social network, users have joined more and more of these networks and live their life virtually. Consequently, they create a huge data on these social networks: their profile, interest, and behavior such as post, comment, like, joining groups or communities, etc. This brings some new challenges to researchers: do users having the same profile/interest show the same behavior? And vice versa, do users having the same behavior have interest in the same things? One of the basic issues in these challenges is the problem of estimating the similarity among users on these social networks based on their profile, interest, and behavior. This paper presents a model for estimating the similarity between users based on their behavior on social networks. The considered behaviors are activities including posting entries, liking these entries, commenting and liking the comment in these entries. The model is then evaluated with a dataset-collected users from Twitter. The results show that the model estimates correctly the similarity among users in the majority of the cases.

In this paper, a novel adaptive robust control (ARC) scheme is proposed for electro-hydraulic servo systems (EHSSs) with uncertainties and disturbances. All dynamic functions in system dynamics are effectively approximated by multi-layer radial basis function neural network (RBF NN)-based approximators with online adaptive mechanisms. Moreover, neural network-based disturbance observers (NN-DOBs) are established to actively estimate and efficiently compensate for the effects of not only the matched/mismatched but also the imperfections of RBF NN-based approximators on the control system. Based on that, the nonlinear robust control law which integrates RBF NNs and NN-DOBs is synthesized via the sliding mode control (SMC) approach to guarantee the high-accuracy position tracking performance of the overall control system. Furthermore, the problem of the combination between DOBs and RBF NNs is first introduced in this paper to treat both disturbances and uncertainties in the EHSS. The stability of the recommended control mechanism is proven by using Lyapunov theory. Finally, numerical simulations with several distinct frequency levels of reference trajectory are conducted to convincingly demonstrate the effectiveness of the proposed approach.

In this paper, an active disturbance rejection control is designed to improve the position tracking performance of an electro-hydraulic actuation system in the presence of parametric uncertainties, non-parametric uncertainties, and external disturbances as well. The disturbance observers (Dos) are proposed to estimate not only the matched lumped uncertainties but also mismatched disturbance. Without the velocity measurement, the unmeasurable angular velocity is robustly calculated based on the high-order Levant’s exact differentiator. These disturbances and angular velocity are integrated into the control design system based on the backstepping framework which guarantees high-accuracy tracking performance. The system stability analysis is analyzed by using the Lyapunov theory. Simulations based on an electro-hydraulic rotary actuator are conducted to verify the effectiveness of the proposed control method.

The exponential increment of commodity exchange has raised the need for maritime border security in recent years. One of the most critical tasks for naval border security is ship detection inside and outside the territorial sea. Conventionally, the task requires a substantial human workload. Fortunately, with the rapid growth of the digital camera and deep-learning technique, computer programs can handle object detection tasks well enough to replace human labor. Therefore, this paper studies how to apply recent state-of-the-art deep-learning networks to the ship detection task. We found that with a suitable number of object queries, the Deformable-DETR method will improve the performance compared to the state-of-the-art ship detector. Moreover, comprehensive experiments on different scale datasets prove that the technique can significantly improve the results when the training sample is limited. Last but not least, feature maps given by the method will focus well on key objects in the image. [ABSTRACT FROM AUTHOR]