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State of charge (SOC) and state of health (SOH) of batteries are the indispensable control decision variables for online energy management system (EMS) in modern internal combustion engine vehicles. The real-time and accurate determination of SOC and SOH is essential to the reliability and safety of EMS operation. Obtaining good accuracy for the SOC estimation is difficult without considering SOH because of their coupling relationship. Although several works on the joint estimation of SOC and SOH of lithium–ion batteries are available, these studies cannot be applied to lead–acid batteries because of the differences in physical structure and characteristics. This study handles the problem of modeling the relationship between SOC and SOH of lead–acid battery and their online collaborative estimation. First, the structure and control strategy of a bus-based EMS is discussed, and the improper energy control actions of EMS due to the inaccurate SOC estimation are analyzed. Second, an instantaneous correlation factor β for SOC and SOH is defined as a new state estimating variable, and the simplified linear relationship model between β and open circuit voltage is established through the battery experiments. Third, a discretized augmented system equation of β is deduced according to the relationship model and the Randles circuit model. The least square circuit parameter identification (LSCPI) algorithm is presented to identify the time-varying circuit model parameters, while the adaptive Kalman filter for augmented system (AKFAS) algorithm is employed to estimate β online. A collaborative estimation algorithm is proposed on the basis of the LSCPI and AKFAS to determine SOC and SOH of lead–acid battery in real time, and a demo intelligent battery sensor is developed for its implementation. The results of battery charging and discharging experiments indicate that the proposed method has high accuracy. The estimation accuracy of SOC of this method reaches 3.13%, which is 7% higher than that of the existing method.

The triple-frequency linear combination with a low noise, a long wavelength, and a weak ionosphere is beneficial to effectively eliminate or weaken the common errors, advance the reliability of cycle slip detection and repair, and speed up the convergence time of fixed ambiguity. By establishing the Galileo triple-frequency carrier linear combination model, three types of linear combinations are derived: Geometry-free (GF) combinations, minimum noise (MN) combinations, and ionosphere-free (IF) combinations. The geometric relationships of these linear combinations are displayed in the form of image. The results indicate that the angle formed by the IF combinations and the MN combinations is between 75.02° and 86.01°, which also illustrates that it is more difficult to meet the carrier phase combinations with a low noise and a weak ionosphere. Moreover, to guarantee the integer cycle characteristics of ambiguity, the combination coefficient must be an integer. Galileo triple-frequency linear combination is solved utilizing the extremum method. To sum up, the sum of the coefficients of the extra wide lane (EWL) combinations and wide lane (WL) combinations is zero, and the sum of the coefficients of the narrow lane (NL) combinations is one. (0, 1, −1) is the optimal triple-frequency linear combination in Galileo. Three independent linear combinations are selected separately from the EWL, WL, and NL to jointly solve the integer ambiguity. Further, it creates a prerequisite for high-precision and real-time kinematic positioning.

In order to realize the tram’s low-floor structure, most of the trams that have been recently introduced adopt an independently rotating wheelset. In the case of trains driving in two regions with different gauges, an independently rotating wheelset may be applied to utilize the variable track technology. Since the independent rotation type wheelset has no rotational restraint of the left and right wheels, the difference in rotational speed between the outer and inner wheels occurs naturally during curved driving, and it is applied to railroad vehicles traveling in sharp curve sections because it smoothly drives curved driving. However, the longitudinal creep force and the lateral restoring force are weakened as the left and right rotational constraints disappear. Lack of lateral direction restoring force weakens stability while causing continuous flange contact driving or zigzag phenomenon against disturbance. Under the conditions of driving in a sharp curve, these railway vehicles generate excessive wear, noise, and lateral pressure, as well as deterioration of ride comfort and derailment. In order to overcome these drawbacks, a method has been proposed in which the torque of a motor mounted on each wheel is individually controlled to generate lateral restoring force or to improve driving performance through lateral displacement control using a yaw moment. In this paper, development using HIL (hardware in the loop) simulator was performed to check the performance and stability of the individual motor torque control technology before verifying by applying the individual motor torque control to the actual vehicle. HIL simulator were constructed by combining a real-time dynamic analysis model of a railway vehicle with a drive motor to which real individual motor control was applied. Under the conditions of driving the test track where the actual test vehicle was tested, the analysis of the driving characteristics and the control characteristics of the disturbance was performed to confirm the proposed individual motor torque control performance.

In this study, we have developed a thin film adhesion evaluation system for diamond coating tool, which is mainly used for CFRP processing, which is widely used in aviation and automobile industries. Carbon fiber reinforced plastic (CFRP), which is a composite material is widely used for high efficiency and light weight in the aerospace industry and automobile industry. CFRP has a low specific gravity and a high specific strength. However, it has difficulty in processing because it has difficulty property. In order to cope with this problem, CFRP is processed using a diamond coating tool with a higher surface hardness. Because of the high surface hardness of the diamond coating tool, it is difficult to evaluate the adhesive strength of the tool, and it is difficult to quantitatively evaluate the existing adhesion force evaluation method and it is costly. This study has developed an evaluation method to measure the adhesive force based on the data of the grinding process using a low cost polishing pad. When the frictional force and the shearing force are applied to the specimen by the sanding belt, friction is continuously generated between the coating layer and the belt, and peeling occurs at the moment when a specific load is applied. Acceleration, load, and torque values that occur during each experiment are collected through acceleration sensors, load cells, and torque sensors. The data obtained through the experiments are subjected to FFT processing and analysis. As a result, the peeling point and the critical load value at this point are identified and referred to as the adhesion force of the coating layer.

Backlash is the inherent characteristic of gear transmission system. It is an important mean to measure and compensate backlash to improve transmission performance. When the driving gear reverses, due to the existence of backlash, the driven gear will still keep its original steering due to inertial motion until the driven gear collision with the driving gear. In this paper, based on the analysis of the tooth gap of the driving gear and the driven gear, an on-line measurement method of the meshing tooth gap combining the angular velocity of the load end with the information of the Angle position of the two axes of the master-slave gear is presented. This method uses the gyroscope at the load end (driven wheel) to monitor the sudden change of angular velocity at the moment of gear reverse impact and re contact, and the signal is used as the judgment basis for the reverse contact moment of the driving gear and driven gear. Then, the angular position data of driving gear and driven gear are fused at the time of reverse rotation and contact impact, and the backlash of the gear is calculated online. Finally, setting up an experimental platform for measuring backlash, and the proposed method is verified. The experimental results show that the proposed method can achieve accurate backlash measurement.

In this paper, the dynamics and control strategies of a biped robot with 6-DOF parallel leg mechanism are studied. Firstly, the multi-body kinematic model and dynamic model of the robot are established. Secondly, the insufficient stiffness of robot’s feet and the coupling effect between the kinematic chains are considered in dynamics modeling, and the rigid-flexible coupling model is established by using ADAMS and finite element method. Finally, aiming at the position deviation and system vibration caused by the flexible foot, a strategy based on the combination of a computed torque controller (CTC) and a second-order sliding-mode super twisting algorithm (STA) is designed. At the same time, the control strategy based on CTC and PID and the control strategy based on CTC and sliding mode control (SMC) are designed to compare with CTC-STA. The results show that CTC-STA has faster regulation ability and stronger robustness than CTC-SMC and CTC-PID.

We propose an approach for floating bridge installation and operation. A floating bridge aims to carry heavy vehicles, trucks, and people over a body of water. However, bridge installation and operation are mainly performed by humans regardless of adverse conditions, such as active combat or disaster occurrence. For installing and operating floating bridges under such conditions, we devised a solution based on control system design and automatic installation. The floating bridge system is controlled and positioned by a power propulsion system that is attached to floating units of the bridge. An optimal control system based on state estimator, which is designed using a robust control framework, is applied to install and operate the bridge. A simulation analysis and experiments demonstrate the effectiveness of proposed method on a bridge system model comprising five floating units.

Given users and products that he/she reviews, can we recognize fake reviews just using the text information, or determine whether a reviewer is a fraud or not? Automatically detecting fake reviews and reviewers is an urgent problem and lots of work attempts for discovering linguistics, behaviors and graph patterns. However, in reality, there are new kinds of fraudsters who can change their behaviors to camouflage as genuine reviewers to avoid detection systems. With the fraudsters become distributed, dynamic, and adversarial, anti-spam tasks face a new challenge. In this paper, we tackle the challenge of adversarial fraudsters in online app review platform and propose a system called DDF (Detect, Defense, and Forecast) to uncover camouflage accounts. Firstly, we select a small set of seed with high-precision based on text and behavior features; Secondly, we build our graph-based detection model for uncovering hidden (distant) users who serve structurally similar to the seed by utilizing Graph Convolutional Network (GCN) algorithm. Thirdly, we evaluate DDF using real-world data set from Tencent APP Store and analyze the potential fraudsters detected by DDF. It is worth mentioning that precision can achieve 0.95+. Finally, we validate the efficiency and scalability of DDF and show that it can be well transferred to other anti-spam tasks.

During the outbreak of the COVID-19 (2019 coronavirus disease), misinformation related to the virus spread rapidly online and have led to serious difficulties in controlling the disease. The term infodemic is coined to outline the bad effect from the extensive dissemination of misinformation during the outbreak. With regards to this phenomenon, the World Health Organization emphasized the need to fight against infodemic and asked all countries not only to make efforts in slowing down the spread of the COVID-19 but also in countering the risk caused by infodemic. Due to its negative impact, this paper analyzes infodemic on Chinese social media at the initial stage of the COVID-19 outbreak and presents a 4P framework standing for the four features of Chinese infodemic: Prevention Attention, Problem Orientation, Patterns Interaction and Points Globalization. Furthermore, a selective review of existing datasets in the neural networks domain is synthesized based on the 4P framework. Finally, research directions, including recommendations, about constructing a large-scale dataset for Chinese infodemic automatic detection are proposed.

This paper investigated the flow performance around a near-wall cylinder and its effect on heat transfer enhancement in the laminar and early transitional flow region. The numerical model is resolved by finite volume method through FORTRAN code. The results show that the flow field becomes a transitional flow state when Re = 100 due to the insertion of a cylinder. In the transitional flow sate, the heat transfer enhancement is regional, mainly concentrating in the region of –2 ≤ x/D≤ 10, and the region increases with the increase of Re; There are three or four peaks in the distribution of instantaneous local Nusselt number. The first peak is caused by the acceleration of the fluid between the cylinder and the bottom wall. The other peaks are caused by the interaction between the cylinder wake and the bottom wall boundary layer. The vortical structure induced by the periodic instability of the fluid in the transitional flow is the main factor for explaining the local heat transfer enhancement of the cylinder downstream wall. Re has a direct impact on the vortical structure in the flow field. The greater Re, the greater the heat transfer enhancement of the cylinder downstream wall. Under the same blocking ratio of D/H, the greater Re, the smaller the optimal clearance ratio of C/D. The guidelines are suggested for the design on heat dissipation of electronic equipment.

This paper investigates the influence of carrier gas flow on the external flow field of coaxial powder feeding nozzle. FLUENT software was adopted to establish gas-solid two-phase flow. The simulation of powder stream field under different carrier gas flow was also carried out. Results show that the larger the flow of carrier gas is, the higher the gas flow field velocity at the nozzle outlet is. At the same time, the concentration at the convergence point is lower, and the convergent point is maintained at 0.015 m. Under the condition of 4 L/min, the powder flow convergence is good. When it exceeds 4 L/min, powder spot diameter increases. The experiment of powder aggregation and laser cladding forming were completed, which shows that the forming effect is the best one under the condition of 4 L/min. It is consistent with the simulation analysis results and has a high reference to the optimization of the process parameters of coaxial nozzle.

In this paper, the effect of the interdigital transducer (IDT) position parameters on the surface acoustic wave (SAW) yarn tension sensor sensitivity is investigated. The stress–strain characteristic of substrate was studied by the combination of finite element simulation and regression analysis method. According to this characteristic, the function relationship between the SAW yarn tension sensor sensitivity and the IDT position parameters was built using the regression analysis method. The monotonicity of the regression function was also given. On this basis, a novel sensitivity optimal scheme was proposed and solved by the quadratic programming method. Its solution demonstrates that the optimum sensitivity can be obtained when the IDT is 8.9 mm to the left side of the substrate and the IDT is 0.3 mm to the top edge of the substrate within a domain of the IDT position parameters. The SAW yarn tension sensor with corresponding IDT position parameters was fabricated to validate the correctness of the sensitivity optimal scheme. The measured results indicate that the SAW yarn tension sensor sensitivity can reach 813.69 Hz/g, which confirms that the novel scheme is effective.

Research on integrated process planning and scheduling (IPPS) is of great significance to the improvement of the overall quality of machinery manufacturing system. In the actual manufacturing process, the manufacturing system is often accompanied by some unpredictable uncertain disturbance factors, for instance uncertain processing time of jobs and changes of due date, etc. These uncertain disturbance events will ultimately affect production efficiency and customer satisfaction. Consequently, this paper considers the multi-objective IPPS problem with uncertain processing time and uncertain due date. A multi-layer collaborative optimization (MLCO) method is designed for the fuzzy multi-objective IPPS (FMOIPPS) problem, including three layers. For the process planning layer, the basic genetic algorithm is used to provide various near optimal process plans for the process selection system. For the process selection layer, a multi-objective genetic algorithm (MOGA) is designed to optimize the process selection population. A sharing function method is introduced to maintain population diversity. An individual comprehensive evaluation method is introduced to evaluate non-dominated solutions. The crowded distance, fast non-dominated sorting and elite strategy based on NSGAII is adopted in the proposed MOGA. The external archive method is employed to preserve the non-dominated solutions generated during population evolution. For the scheduling layer, a MOGA with a boundary search strategy is proposed. The boundary search strategy is designed to improve the search ability of boundary solutions. Three optimization objectives are minimizing the spread of fuzzy makespan, minimizing fuzzy makespan and maximizing average customer satisfaction simultaneously. The target of scheduling layer is to make scheduling arrangements for the process information obtained by process selection layer. Through mutual cooperation among each layer, guide the overall optimization process, and finally get satisfactory solutions. Different problem examples of various scales are employed to verify feasibility and effectiveness of the MLCO method. The experimental results indicate that the MLCO method can effectively address FMOIPPS problem.

In indoor environments, accurate location or positioning becomes an essential requirement, driven by the need for autonomous moving devices, or to identify the position of people in large spaces. Single technology schemes which use WiFi and Bluetooth are affected by fading effects as well as by signal noise, providing inaccuracies in location estimation. Hybrid locating or positioning schemes have been used in indoor situations and scenarios in order to improve the location accuracy. Hence, this paper proposes a hybrid scheme (technique) to implement fingerprint-based indoor positioning or localization, which uses the Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points as well as Wireless Sensor Networks (WSNs) technologies. Our approach consists of performing a virtual tessellation of the indoor surface, with a set of square tiles encompassing the whole area. The model uses an Artificial Neural Network (ANN) approach for position estimate, in which related RSS is associated to a 1 m × 1 m tile. The ANN was trained to match the RSS signal strength to the corresponding tile. Experimental results indicate that the average distance error, based on tile identification accuracy, is 0.625 m from tile-to-tile, showing a remarkable improvement compared to previous approaches.

Automated guided vehicles (AGVs) are extensively used in many applications such as intelligent transportation, logistics, and industrial factories. In this paper, we address the path planning problem for an AGV system (i.e. a team of identical AGVs) with logic and time constraints using Petri nets. We propose a method to model an AGV system and its static environment by timed Petri nets. Combining the structural characteristics of Petri nets and integer linear programming technique, a path planning method is developed to ensure that all task regions are visited by AGVs in time and forbidden regions are always avoided. Finally, simulation studies are presented to show the effectiveness of the proposed path planning methodology.

Unmodelled dynamic factors are either the left-over or the estimated factors such as lower or upper bound values while modelling any mechatronic system. Hence, with the inclusion of under-actuation in a system such as fewer number of actuators as compared to degrees of freedom, this will lead the system to high instability. These factors are changing instantly during multiple flights of quadrotor that is, the values of these factors in path following may vary from the values in hovering mode. Hence, it is one of the strenuous tasks to tackle these unmodelled dynamic factors for the multiple flight modes of an underactuated quadrotor craft. One of the better ways for tracking control of a quadrotor aerial vehicle with unmodelled dynamics is to observe and estimate the instant change in parameters. Thus, this paper exhibits an extensive review of several hybrid observer design methods being fused with some novel control strategies. In addition to this, the survey paper also summarises the limitations of the current state of the art approaches. This paper demonstrates an unexplored field of study where researchers must need to evaluate the performance of hybrid observer design methods.

The flexible job shop problem (FJSP), as one branch of the job shop scheduling, has been studied during recent years. However, several realistic constraints including the transportation time between machines and energy consumptions are generally ignored. To fill this gap, this study investigated a FJSP considering energy consumption and transportation time constraints. A sequence-based mixed integer linear programming (MILP) model based on the problem is established, and the weighted sum of maximum completion time and energy consumption is optimized. Then, we present a combinational meta-heuristic algorithm based on a simulated annealing (SA) algorithm and an artificial immune algorithm (AIA) for this problem. In the proposed algorithm, the AIA with an information entropy strategy is utilized for global optimization. In addition, the SA algorithm is embedded to enhance the local search abilities. Eventually, the Taguchi method is used to evaluate various parameters. Computational comparison with the other meta-heuristic algorithms shows that the improved artificial immune algorithm (IAIA) is more efficient for solving FJSP with different problem scales.

In the hardware-in-the-loop simulation, the goal of electric loading is to realize the accurate tracking of the torque signal and test the performance of the aircraft actuator system. For some high dynamic aircraft, it is necessary to reduce the influence of the surplus torque to increase the system frequency band. This paper introduces a new electric loading system which adopts a double-loop servo motor as the torque loading mechanism. It applies two loops to track the position of the rudder and the aerodynamic load spectrum respectively. For the purpose of reducing the disturbance between two loops of the scheme, a two-DOF H∞ robust controller is designed, which improves the robustness of the system effectively. The simulation results show that the new system increases the upper limit of 25 Hz frequency band of the traditional single-loop system with PID control to the maximum of 40 Hz. The double-loop system thereby meets the technical requirements of the hardware-in-the-loop simulation experiment for high dynamic aircrafts.

In this paper, we proposed an adaptive ACS algorithm by introducing an adaptive pheromone volatility coefficient and the algorithm diversity dynamically varying in different iterations of the algorithm. It incorporates a shunting hierarchical hybrid neural network application algorithm (Shunting HHNN Application Algorithm, SHAA) to overcome the drawbacks of global optimization capabilities of ant colony system (ACS) in solving robot path and easily being trapped into the local optimal solution. Considering the influence of the activation value size on the selection of the grid in the SHAA neural network algorithm, the distance factor and the activation value are combined to improve the heuristic function. This will not only ensure the convergence speed, but also avoid the premature stagnation and being trapped into a local optimal path. Simulation results show that the algorithm discussed in this paper outperforms better in both the global optimization ability and the robustness.

The bridge crane system is widely used in the industrial production for transporting large loads. Its anti-sway positioning control is quite crucial for enhancing handling efficiency and safety, but it is also difficult due to underactuated dynamics and various disturbances. In this paper, an anti-sway positioning control algorithm for unmanned crane is proposed based on the load generalized position tracking control algorithm (GPTC), which combines with a disturbance observer to effectively reject the lumped disturbances. The test results show that the proposed method can effectively achieve anti-sway and positioning with prominent disturbance suppression improvements.

In this paper, the output feedback disturbance rejection control (OFDRC) problem is considered for buildings structures subject seismic excitations. First, based on a mild assumption and a linear transformation, the addressed problem of building structure system is translated into the output feedback disturbance rejection control problem large-scale system with disturbances. Then, by utilizing generalized-proportional-integral observer (GPIO) technique and output feedback domination approach, an output feedback decentralized disturbance rejection control law is derived via a systematic design manner. The multi-overlapping output feedback disturbance rejection controller is obtained according to the inverse transformation of a linear transformation. Strict theory analysis demonstrates that the states of the structure system will be stabilized to a small bounded region. Finally, an 8-story structure system is employed to evaluate the effectiveness of the proposed control strategy. Simulation results demonstrate that the proposed OFDRC exhibits better seismic loads attenuation ability and strong robustness against model uncertainties.

The force-to-rebalanced control mode is the main operating mechanism of the HRG, and the standing wave azimuth of the resonator points to the 0° direction. But the bias of the HRG is very sensitive to the change of external physical field environment and it is urgent to improve the bias stability. The relationship between the bias of the HRG and the standing wave azimuth is periodic. When the standing wave azimuth and the damping axis are aligned, the bias error of the HRG will be greatly reduced, and the bias stability of the HRG affected by the temperature can also be improved by changing the azimuth of standing wave. In this paper, the control algorithm and the hardware circuit of the HRG are designed to control the HRG at any standing wave azimuths. The control of the HRG in different standing wave azimuths is realized, and the system temperature experiment is carried out. The experimental results show that the bias of the HRG are significantly different at different standing wave azimuths, and the bias stability of the HRG affected by the temperature can be improved 3 times by changing the standing wave azimuth.

Nonlinearities, uncertainties and external disturbances commonly exist in a wastewater treatment process (WWTP). Those issues present great challenges to the control of the dissolved oxygen (DO) concentration in a WWTP. In this paper, an active disturbance rejection control (ADRC) is utilized to estimate the total disturbance and drive the DO concentration to track the set-value. Simultaneously, an iterative learning strategy is employed to adjust the parameters of an extended state observer (ESO) to improve the accuracy of the estimation and reduce the dependence on experience in determining parameters. By combining the advantages of the ADRC and the iterative learning strategy, an iterative learning based active disturbance rejection control (ILADRC) is constructed, and the close-loop stability is analyzed. The benchmark simulation model No.1 (BSM1) is utilized to confirm the ILADRC. Numerical results show that the ILADRC is more effective in the DO concentration control.

Traditional named entity recognition methods mainly explore the application of hand-crafted features. Currently, with the popularity of deep learning, neural networks have been introduced to capture deep features for named entity recognition. However, most existing methods only aim at modern corpus. Named entity recognition in ancient literature is challenging because names in it have evolved over time. In this paper, we attempt to recognise entities by exploring the characteristics of characters and strokes. The enhanced character embedding model, named ECEM, is proposed on the basis of bidirectional encoder representations from transformers and strokes. First, ECEM can generate the semantic vectors dynamically according to the context of the words. Second, the proposed algorithm introduces morphological-level information of Chinese words. Finally, the enhanced character embedding is fed into the bidirectional long short term memory-conditional random field model for training. To explore the effect of our proposed algorithm, experiments are carried out on both ancient literature and modern corpus. The results indicate that our algorithm is very effective and powerful, compared with traditional ones.

Liquid sloshing in moving or stationary containers and flexible uncertainty caused by the slosh are considered to be the most probable causing unexpected coupling effects on the dynamics of many systems such as aerospace, ground vehicles, and high speed industries arms. The coupling of dynamic liquid slosh in a container system with the uncertainty caused by the sensors or dampers is rare documented and this coupling can be considered as a highly nonlinear system. In this paper, an investigation is presented to demonstrate a new approach for enabling the reduction of the liquid slosh and uncertainty by implementing adaptive robust wavelet control technique. Starting by creating the mathematical dynamic model for the nonlinear slosh coupled by uncertainty, adaptive robust control based wavelet transform is applied for calculating optimal motion that minimize residual slosh and uncertainty. Subsequently the adaptive robust control based wavelet network approximation and the appropriate parameter algorithms for the container system with slosh and uncertainty are derived to achieve the feedback linearization, adaptive control, and H∞ tracking performance. The simulation results show that the effects of slosh errors and external uncertainty can be successfully attenuated within a desired attenuation level.

This article reviews remotely operated underwater vehicle (ROUV) and its different types focusing on the control systems. This study offers a brief introduction of unmanned underwater vehicle (UUV) together with ROUV. Underwater robots are designed to work as an alternative to humans because of a difficult and hazardous underwater environment. The applications and demand of marine robots are increasing with the passage of time. There are several research articles and publications available on these topics but, a complete review of old and recent research about this technology is still hard to find. This article also assesses some recently published research papers on underwater systems. It presents the comparison of different control systems and designs of underwater vehicles. There have been major developments in marine technology depending on the needs, applications and cost of different missions. Scientists design many remotely operated vehicles based on the educational or industrial purposes. This article is presented in order to help and assist the future researchers as a massive review of the field of remotely operated underwater vehicles and their possible future developments are presented.

Changes in soil electrical parameters can affect the distribution of earth potential in high-voltage direct-current (HVDC) ground electrodes when climatic conditions changes. This paper proposes a model to describe the relationship between surface-layer soil moisture and surface-layer soil resistivity under short-term climatic influence, by using image recognition technology. Based on the relatively stable resistivity of lower soil layers, a soil model more reflective of the actual operating conditions is established for soils near the ground electrode, and a finite element method is adopted to calculate the earth-surface potential (ESP). The experimental results indicate the following: (1) Compared with other measurement methods, image recognition of surface soil resistivity is a low-cost, real-time, online, and accurate method; and (2) changes in surface-layer soil moisture affect both ESP and step voltage. These effects are large in the case of high resistivity for the soil layer where the ground electrode is buried. This large fluctuation in step voltage particularly results in a potential safety hazards during ground electrode operation. Therefore, in order to ensure personal safety and obtain more accurate electrical parameters, it is necessary to consider the effect of natural climate on the soil surface resistivity. Finally, the value of the step voltage can be observed using image recognition, this also provides a new method for the safety monitoring of the DC ground electrode.

With the development of mobile communication and global positioning system navigation and positioning technology, analysis of user behavior on mobile Internet has become a hot topic in research area. Sign in sharing-bicycles’ app, find bicycle location, and selected has become a part of mobile Internet user’s daily life. Based on the data analysis of the spatial and temporal characteristics, find out sharing-bicycle’s user behavior obeys power-law distribution. In the time interval, user behavior of sharing-bicycle has strong intermittency and weak memory; the exponent of probability that K edge nodes is three by fitting the distance of sharing-bicycle’s data curve. It is verified that mobile Internet is long to scale-free networks. We conclude seven characteristics of user’s behavior of sharing-bicycle in mobile Internet application from experimental results. The analysis of sharing-bicycle’s behavior has become a complement and extension in human dynamics research field.

This study proposes an effective solution to the problem of attitude control for a rigid body satisfying angular velocity constraint as well as providing fault-tolerant capability. More specifically, a finite-time sliding surface containing attitude quaternion and angular velocity is first defined. Then, a novel tan-type prescribed performance control (PPC) with simple structure is presented to confine the sliding surface within a predefined performance boundary. Not only the attitude quaternion and angular velocity are indirectly constrained, but also it is thoroughly proved that the rotation velocity constraint is met even when severe actuators faults occur. The closed-loop attitude system is confirmed to be finite-time stable in the sense of Lyapunov stability. Numerical simulations clearly illustrate the effectiveness and usefulness of the suggested finite-time PPC despite actuator faults and environmental disturbances.

Opportunistic network is emerging as a research domain nowadays with the introduction of Internet of things phenomena. In recent years, storage level congestion issue due to handheld devices is considered as a key challenge to be handled in the opportunistic networks. The prime objective of conducting this research is to develop artificial intelligence rule-based fire engine model to be tested using artificial intelligence latest classification algorithms further implemented using ONE simulator tool over MaxProp protocol. The achieved results show 98% accuracy in terms of classification using k-fold validation technique over six algorithms. The achieved results have been compared with MaxProp protocol over evaluation parameters such as delivery ratio, throughput, routing load, and overhead; whereas delivery ratio increase about 20% for node level and 5% for buffer level and throughput tends to increase 500 and 150 kbps for network and buffer levels, respectively.

This study considers the visual stabilization problem of nonholonomic mobile robots and proposes a novel optimization stabilization method for visual servo control of nonholonomic mobile robots with monocular cameras fixed onboard. The main idea of the method is to utilize control Lyapunov functions of discrete-time nonlinear systems to design a family of explicit stabilization control laws of the visual servo error system. The parameters of the control laws can indirectly reflect the performance of the visual servo controllers. Then taking account of visibility constraints and actuator limitations, a set of optimal parameters of the control laws is calculated by offline solving a constrained finite horizon optimal control problem. Moreover, the stabilization results on the optimal visual servo controller are established based on the properties of control Lyapunov functions. Finally, some simulation experiments are used to illustrate and evaluate the performance of the visual servo control scheme proposed here.

Every video stream possesses temporal redundancy based on the amount of motion presenting in it. An ample amount of motion in a video sequence may cause distorting artifacts, and in order to avoid them, there is a possibility to mask the motion or temporal activity that is not noticeable to a human eye in real time. The artifacts such as blockiness and blurriness are instigated in the video sequence as soon as it is subjected to the process of compression, and they tend to become more and more intense with the increase in temporal activity. In this paper, an algorithm is proposed to mask the temporal activity using temporal masking coefficient ( q) that is unnoticeable by a human eye to bring down the distortion levels. It is possible to adjust the quality of the video sequence by varying the q parameter and thus controlling its overall quality index. Frames are extracted from the video sequence, and displacement or motion vectors are also calculated from the consecutive frames using a bi-directional block matching algorithm. These motion vectors are used to estimate the quantity of motion present between consecutive frames of the same scene. Video sequences used for this purpose are basically H.264 format. Temporal masking is performed on a video sequence with and without the implementation of motion vector. Structural similarity index and peak signal-to-noise ratio are the quality measurement tools used to assess the performance of the proposed algorithm. A bit rate of 1.2% was saved by implementing proposed algorithm at q = 1 in contrast to the standard H.264/Advanced Video Coding.

As a clean energy engine, the gas turbine is widely used for the generation of the power plant and the propulsion of the warship. Its control is becoming more and more challenging for the reason that internal coupling exists and the load command changes frequently and extensively. However, advanced controllers are difficult to implement on the distributed control system and conventional proportional–integral–derivative controllers are unable to handle with aforementioned challenges. To solve this problem, this article designs a decentralized active disturbance rejection control for the power and exhaust temperature of the gas turbine. Simulation results illustrate that the decentralized active disturbance rejection control is able to obtain satisfactory tracking and disturbance rejection performance with strong robustness. Eventually, a numerical simulation is carried out which shows advantages of active disturbance rejection control in the control of power and exhaust temperature when the gas turbine is under variable working condition. This successful application of decentralized active disturbance rejection control to the gas turbine indicates its promising prospect of field tests in future power industry with increasing demand on integrating more renewable energy into the grid.

This paper presents a novel calibration method for micro-electro mechanical system gyroscope in attitude measurement system of small rotor unmanned aerial vehicles. This method is based on an observation vector and its cross product, which is especially valuable for the in-field calibration without the aid of external equipment. By analysing the error model of the tri-axial gyroscope, the principle of calibration is proposed. Compared with other algorithms, numerical simulations are performed to evaluate the effectiveness of integral form of the cross product calibration method. Experiment on the hex-rotor unmanned aerial vehicle platform shows that the proposed method has great advantages in low-cost integrated navigation system.

The research on face recognition has become an integral part in the field of many engineering areas. The variation in the appearance of an human image makes proper face recognition a difficult task. However, face identification is an extremely important aspect of human identification system especially in gender identification system. Thus, this paper has aimed to detect the gender of human beings based on different frontal facial features. In this case we have considered facial images with different emotions like neutral, happy, sad, angry and surprised, respectively. To perform gender identification, frontal facial features are detected and extracted based on Region of Interest principle. Then, Fast Fourier Transform and Discrete Cosine Transform algorithmic logics have been incorporated to transform the input data from spatial domain to frequency domain, and on the resultant data further operations are performed to accomplish the task of gender identification. This has made the proffered algorithm different and unique from the existing algorithms. In addition, an alternate approach has also been proposed for gender identification and revelation based on the shape and structure of human finger nails. The latter method has primarily emphasized on the structural organization of finger nails in both male and female. The propounded algorithm has also succeeded in determining the gender of human being based on the shape and structure of human finger nails.

Stable and accurate attitude estimation is the key to the autonomous control of unmanned aerial vehicle. The Attitude Heading Reference System using micro-electro-mechanical system inertial measurement unit and magnetic sensor as measurement sensors is an indispensable system for attitude estimation of the unmanned aerial vehicle. Aiming at the problem of low precision of the Attitude Heading Reference System caused by the nonlinear attitude model of the micro unmanned aerial vehicle, an attitude heading reference algorithm based on cubature Kalman filter is proposed. Aiming at the nonlocal sampling problem of cubature Kalman filter, the transformed cubature Kalman filter using orthogonal transformation of the sampling point is presented. Meanwhile, an adaptive estimation algorithm of motion acceleration using Kalman filter is proposed, which realizes the online estimation of motion acceleration. The car-based tests show that the algorithm proposed in this paper can accurately estimate the carrier’s motion attitude and motion acceleration without global positioning system. The accuracy of acceleration reaches 0.2 m/s2, and the accuracy of attitude reaches 1°.

In this work, a double-cylinder viscometer is designed to measure dynamic viscosity over a pressure range from atmospheric pressure up to 150 MPa and a temperature range of 278.15–333.15 K. A high-pressure closed cavity is designed innovatively and the magnetic coupling is adopted to transfer the torque to reduce the friction; the inner cylinder with ruby bearing is designed to reduce the friction torque, thus the accuracy of the viscosity measurement is improved. The experiment of measuring the standard viscosity liquid (N10 and N35) under normal pressure and measuring the viscosity of methylbenzene under the pressure of 0.1–150 MPa were carried out, and considering all the experimental data, the uncertainty of the viscosity measurements is approximately ±3%.

Power allocation plays an important and challenging role in fuel cell and supercapacitor hybrid electric vehicle because it influences the fuel economy significantly. We present a novel Q-learning strategy with deterministic rule for real-time hybrid electric vehicle energy management between the fuel cell and the supercapacitor. The Q-learning controller (agent) observes the state of charge of the supercapacitor, provides the energy split coefficient satisfying the power demand, and obtains the corresponding rewards of these actions. By processing the accumulated experience, the agent learns an optimal energy control policy by iterative learning and maintains the best Q-table with minimal fuel consumption. To enhance the adaptability to different driving cycles, the deterministic rule is utilized as a complement to the control policy so that the hybrid electric vehicle can achieve better real-time power allocation. Simulation experiments have been carried out using MATLAB and Advanced Vehicle Simulator, and the results prove that the proposed method minimizes the fuel consumption while ensuring less and current fluctuations of the fuel cell.

This paper presents mutual time-varying delay-coupled temporal Boolean network model and investigates synchronization issue for mutual time-varying delay-coupled temporal Boolean networks. The necessary and sufficient conditions for the synchronization are given, and the check criterion of the upper bound is presented. An example is given to illustrate the correctness of the theoretical analysis.

When low-resolution face images are used for face recognition, the model accuracy is substantially decreased. How to recover high-resolution face features from low-resolution images precisely and efficiently is an essential subtask in face recognition. In this study, we introduce shuffle block SRGAN, a new image super-resolution network inspired by the SRGAN structure. By replacing the residual blocks with shuffle blocks, we can achieve efficient super-resolution reconstruction. Furthermore, by considering the generated image quality in the loss function, we can obtain more realistic super-resolution images. We train and test SB-SRGAN in three public face image datasets and use transfer learning strategy during the training process. The experimental results show that shuffle block SRGAN can achieve desirable image super-resolution performance with respect to visual effect as well as the peak signal-to-noise ratio and structure similarity index method metrics, compared with the performance attained by the other chosen deep-leaning models.

The ongoing development of wireless sensor networks has been greatly restricted because of scarce spectrum resources, limited battery power, and ineffective topologic structures. Thus, how to construct a suitable topological structure and allocate the appropriate node channels has become an urgent problem. In this study, we built a game model that took into account the influence of channel allocation and topology structure on network performance. The game model considered the nodes in wireless sensor networks to be players and took transmitting power, node channels, and node rest energy into account to establish the income function. Then, the model certified that it has Nash equilibrium. Next, we propose an energy-optimized game algorithm joint topology and channel for wireless sensor network (CETGA) in accordance with the game model. The CETGA algorithm improved each node’s income by changing the transmitting power and node channel gradually, assuming that the network retained connectivity. Then, we demonstrated that the algorithm could converge to a Pareto optimal. Finally, we used MATLAB software to verify the simulation. The results show that the topology created by CETGA is with low interference and long lifetime. In addition, the nodes’ average residual energy is more balanced and the network robustness and real time are improved.

Load-carrying exoskeletons need to cope with load variations, outside disturbances, and other uncertainties. This paper proposes an adaptive trajectory tracking control scheme for the load-carrying exoskeleton. The method is mainly composed of a computed torque controller and a fuzzy cerebellar model articulation controller. The fuzzy cerebellar model articulation controller is used to approximate model inaccuracies and load variations, and the computed torque controller deals with tracking errors. Simulations of an exoskeleton in squatting movements with model parameter changes and load variations are carried out, respectively. The results show a precise tracking response and high uncertainties toleration of the proposed method.

Concerning first-order unstable processes with time delays that are typical in chemical processes, a modified 2-degree-of-freedom proportional–integral–derivative control method is put forward. The system presents a two-loop structure: inner loop and outer loop. The inner loop is in a classical feedback control structure with a proportional controller intended for implementing stable control of the unstable process; the outer loop is in a 2-degree-of-freedom structure with feedforward control of set points, where the system’s tracking response of set points is separated from its disturbance response. To be specific, the system has a feedforward controller that is designed based on the controlled object models and mainly used for regulating the system’s set point tracking characteristics; besides, it has a feedback controller that is designed on the ground of direct synthesis of disturbance suppression characteristics to improve the system’s disturbance rejection. To verify the effectiveness, the system is put into a theoretical analysis and simulated comparison with other methods. Simulation results show that the system has good set point tracking characteristics and disturbance suppression characteristics.

Workpiece quality prediction is very important in modern manufacturing industry. However, traditional machine learning methods are very sensitive to their hyperparameters, making the tuning of the machine learning methods essential to improve the prediction performance. Hyperparameter optimization (HPO) approaches are applied attempting to tune hyperparameters, such as grid search and random search. However, the hyperparameters space for workpiece quality prediction model is high dimension and it consists with continuous, combinational and conditional types of hyperparameters, which is difficult to be tuned. In this article, a new automatic machine learning based HPO, named adaptive Tree Pazen Estimator (ATPE), is proposed for workpiece quality prediction in high dimension. In the proposed method, it can iteratively search the best combination of hyperparameters in the automatic way. During the warm-up process for ATPE, it can adaptively adjust the hyperparameter interval to guide the search. The proposed ATPE is tested on sparse stack autoencoder based MNIST and XGBoost based WorkpieceQuality dataset, and the results show that ATPE provides the state-of-the-art performances in high-dimensional space and can search the hyperparameters in reasonable range by comparing with Tree Pazen Estimator, annealing, and random search, showing its potential in the field of workpiece quality prediction.

Based on the clinical evidence that head position measured by the multisensory system contributes to motion control, this study suggests a biomechanical human-central nervous system modeling and control framework for sit-to-stand motion synthesis. Motivated by the evidence for a task-oriented encoding of motion by the central nervous system, we propose a framework to synthesize and control sit-to-stand motion using only head position trajectory in the high-level-task-control environment. First, we design a generalized analytical framework comprising a human biomechanical model and an adaptive neuro-fuzzy inference system to emulate central nervous system. We introduce task-space training algorithm for adaptive neuro-fuzzy inference system training. The adaptive neuro-fuzzy inference system controller is optimized in the number of membership functions and training cycles to avoid over-fitting. Next, we develop custom human models based on anthropometric data of real subjects. Using the weighting coefficient method, we estimate body segment parameter. The subject-specific body segment parameter values are used (1) to scale human model for real subjects and (2) in task-space training to train custom adaptive neuro-fuzzy inference system controllers. To validate our modeling and control scheme, we perform extensive motion capture experiments of sit-to-stand transfer by real subjects. We compare the synthesized and experimental motions using kinematic analyses. Our analytical modeling-control scheme proves to be scalable to real subjects’ body segment parameter and the task-space training algorithm provides a means to customize adaptive neuro-fuzzy inference system efficiently. The customized adaptive neuro-fuzzy inference system gives 68%–98% improvement over general adaptive neuro-fuzzy inference system. This study has a broader scope in the fields of rehabilitation, humanoid robotics, and virtual characters’ motion planning based on high-level-task-control scheme.

As increasing data-driven control strategies are applied in electric arc furnace systems, the problem of novelty detection has drawn more attentions than before. The presence of outliers should be the main obstacle in practical applications for these advanced control techniques. To this end, this paper proposes a dynamically selective support vector data description model to discover novelties in electric arc furnace. In this model, support vector data description plays the role of base detector. Artificial outliers are generated with two objectives, one is to assist the dynamic selection, and the other is to optimize two parameters of support vector data description. Then clustering technique is used to determine the validation set for each test point. Finally, a probabilistic method is used to compute the competence of base detectors. In contrast to other novelty ensembles that have parallel structures, our ensemble model has a dynamic selection mechanism that could facilitate the mining of the potential of base detectors. Three synthetic and three real-world datasets are used to validate the effectiveness of the proposed detection model. Experimental results have approved our method by comparing it with several competitors.

Modifying the existing two-way street into a one-way street can alleviate the traffic congestion in a megacity and has the characteristics of economy saving, convenience, and quickness. However, at present, the setting of one-way street in China is mostly determined by human experience and lack of scientific basis. In this context, a one-way street network planning model is established in this paper. According to this model, the optimal method of adding a one-way street on the existing traffic network is found, solved by genetic algorithm. The abstract things are quantified, and some feasible, specific, efficient implementation steps to design a one-way street network are provided for the traffic control department. In addition, taking the traffic in the core area of Tongzhou New City as an example, this paper uses the one-way street planning model to construct the actual street network model and the one-way street network model in the core area of the Tongzhou New City and compares their simulation results. The results show that using the one-way street planning model to modify part of the two-way street into a one-way street in the core area of the Tongzhou New City can indeed improve the traffic operation of the area.

In lithium-ion batteries, the accuracy of estimation of the state of charge is a core parameter which will determine the power control accuracy and management reliability of the energy storage systems. When using unscented Kalman filtering to estimate the charge of lithium-ion batteries, if the pulse current change rate is too high, the tracking effects of algorithms will not be optimal, with high estimation errors. In this study, the unscented Kalman filtering algorithm is improved to solve the above problems and boost the Kalman gain with dynamic function modules, so as to improve system stability. The closed-circuit voltage of the system is predicted with two non-linear transformations, so as to improve the accuracy of the system. Meanwhile, an adaptive algorithm is developed to predict and correct the system noises and observation noises, thus enhancing the robustness of the system. Experiments show that the maximum estimation error of the second-order Circuit Model is controlled to less than 0.20V. Under various simulation conditions and interference factors, the estimation error of the unscented Kalman filtering is as high as 2%, but that of the improved Kalman filtering algorithm are kept well under 1.00%, with the errors reduced by 0.80%, therefore laying a sound foundation for the follow-up research on the battery management system.

Postural inaccuracies in persistent dental tasks indicated an upsurge in the prevalence of musculoskeletal disorders in dentists. This makes it imperative to restrain awkward postural movements while working. Biased results in self-reporting surveys; discomfort, expense, and time consumption involved in using wearable sensors; and expert’s opinion are required in observational methods. Hence, it is important to use significantly reliable, cheap technology as a substitute to overcome the shortcomings of the mentioned techniques. In this study, the markerless Kinect V2–based system was developed and compared with the conventional imaging technique for real-time postural assessment of dental seating tasks. The study assessed the angle parameters related to the dentist’s bodily movement of upper arm, lower arm, wrist, neck, and trunk. Ten dentists from the local dental institution volunteered for the study. Dentists were monitored with both techniques while performing real-time dental procedures. The agreement between the techniques was assessed using Bland–Altman plot at 95% bias, Pearson’s (r1) and concordance (r2) correlation coefficients, mean difference, and percentage error. For conclusive agreement analysis, contingency coefficient, proportion agreement index, Cohen’s kappa, and Mann–Whitney U at 95% confidence interval (CI) were evaluated. Data acquired from both techniques possessed strong correlations (r1 and r2 >0.90). Good agreement in Rapid Upper Limb Assessment data using Cohen’s kappa (0.67) at standard Landis and Koch’s scale was also observed. Postural analysis of slow-motion tasks like dentistry using the Kinect V2 system proved to be unobtrusive and efficient. This may be used by dentists to have periodic postural check. In future, Kinect V2–based feedback system may be used to develop an assistive technology using predictive algorithms, which may help in reducing the probability of occurrence of work-related musculoskeletal disorders in dentists.

This work solves the stability problem of a vehicle suspension with stochastic disturbance by designing an adaptive controller. The model of a quarter vehicle subjected to noise excitation is considered. The stochastic perturbance is realized by the roughness of the road and the vehicle moving with constant velocity. In the control design procedure, fuzzy logic systems are used to approximate unknown nonlinear functions. Meanwhile, the mean value theorem is employed to ensure the existence of the affine virtual control variables and control input. The backstepping technique is applied to construct the ideal controller. On the basis of Lyapunov stability theory, the proposed control method proves that the displacement and speed of the vehicle is reduced to a level ascertained by a true “desired” conceptual suspension reference model. Finally, the effectiveness of the proposed method is verified by simulation of electromagnetic actuator servo system.