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Abstract To study the degradation of lncRNAs in EPMI in rat brain tissue, this study provides a new direction for the estimation of EPMI. LncRNA high-throughput sequencing was performed on the brain tissues of hemorrhagic shock model rats at 0 h and 24 h, and the target lncRNAs were screened. Samples at 0, 1, 3, 6, 12, 18 and 24 h after death were collected, and miRNA-9 and miRNA-125b were used as reference genes. The relative expression levels of lncRNAs at each PMI were detected by RT–qPCR, and a functional model involving lncRNAs and EPMI was established. Samples were collected at 6, 9, 15, and 21 h after death for functional model verification. The expression of several lncRNAs decreased with the prolongation of EPMI, and the mathematical model established by several lncRNA indices exhibited good fit. The verification results of the multi-index joint function model are significantly better than those of the single-index function model, and the established model is more practical. There is a linear relationship between lncRNAs and EPMI, and the multi-index function model is significantly better than the single-index function model, which is important for EPMI inference in forensic pathology practice.

Abstract Yutu-2 rover conducted an exciting expedition on the 41st lunar day to investigate a fin-shaped rock at Longji site (45.44°S, 177.56°E) by extending its locomotion margin on perilous peaks. The varied locomotion encountered, especially multi-form wheel slippage, during the journey to the target rock, established unique conditions for a fin-grained lunar regolith analysis regarding bearing, shear and lateral properties based on terramechanics. Here, we show a tri-aspect characterization of lunar regolith and infer the rock’s origin using a digital twin. We estimate internal friction angle within 21.5°−42.0° and associated cohesion of 520-3154 Pa in the Chang’E-4 operational site. These findings suggest shear characteristics similar to Apollo 12 mission samples but notably higher cohesion compared to regolith investigated on most nearside lunar missions. We estimate external friction angle in lateral properties to be within 8.3°−16.5°, which fills the gaps of the lateral property estimation of the lunar farside regolith and serves as a foundational parameter for subsequent engineering verifications. Our in-situ spectral investigations of the target rock unveil its composition of iron/magnesium-rich low-calcium pyroxene, linking it to the Zhinyu crater (45.34°S, 176.15°E) ejecta. Our results indicate that the combination of in-situ measurements with robotics technology in planetary exploration reveal the possibility of additional source regions contributing to the local materials at the Chang’E-4 site, implying a more complicated geological history in the vicinity.

This paper presents the development, control, and experimental validation of a novel bipedal robot with a passive tendon. The robot, featuring foldable legs, coaxial actuation, and compact folded size, is endowed with a leg configuration with a five-bar mechanism. Based on biological observations of human walking, a passive artificial tendon made of emulsion is fabricated to work in conjunction with a tensioning device, providing adaptive heel touchdown and toe push-off in sync with single-leg movement. The tailored control framework for the bipedal robot is further established with the double-layer architecture. The regulation layer employs the linear inverted pendulum (LIP) model to generate reference trajectory of the center of mass (CoM) with a dead-beat style of parameter adjustment. An inverse-dynamics-based whole-body controller (WBC) is applied to enforce the full-order dynamics of the bipedal robot to reproduce the LIP model’s behavior. We carry out the experiments on the physical prototype to evaluate the walking performance of the developed bipedal robot. The results show that the robot achieves stable walking at the speed of 0.8 m/s (almost twice the leg length/s) and exhibits robustness to external push disturbance.

Active power-assist lower limb exoskeleton robots aim to enhance wearer assistance while ensuring wearer comfort and simplifying the exoskeleton’s design and control. This study proposes an active assistance method known as Equivalent Force on Connection (EFOC). The EFOC method effectively addresses the limitations encountered in conventional Joint Torque Proportional Compensation (JTPC) approaches. These limitations include the necessity for exoskeleton robot configurations to align with human limb structures for parallel assistance at each lower limb joint, as well as the exoskeleton’s inability to contribute a greater proportion of assistance due to the excessive load on specific skeletal and muscular structures, resulting in wearer discomfort. Furthermore, the effectiveness of the EFOC method is evaluated and validated for assistance during both the stance and swing phases of single-leg movements. Finally, the proposed EFOC method is implemented on a hydraulic-driven lower limb exoskeleton robot to assist wearers in squatting, stepping, and jumping locomotion. The experimental results demonstrate that the proposed EFOC method can effectively achieve the desired assistance effect.

This paper presents a three-dimensional (3D)-atomic force microscopy (AFM) method based on magnetically driven (MD)-orthogonal cantilever probes (OCPs), in which two independent scanners with three degrees of freedom are used to achieve the vector tracking of a sample surface with a controllable angle. A rotating stage is integrated into the compact AFM system, which helps to achieve 360° omnidirectional imaging. The specially designed MD-OCP includes a horizontal cantilever, a vertical cantilever, and a magnetic bead that can be used for the mechanical drive in a magnetic field. The vertical cantilever, which has a protruding tip, can detect deep grooves and undercut structures. The design, simulation, fabrication, and performance analysis of the MD-OCP are described first. Then, the amplitude compensation and home positioning for 360° rotation are introduced. A comparative experiment using an AFM step grating verifies the ability of the proposed method to characterize steep sidewalls and corner details, with a 3D topography reconstruction method being used to integrate the images. The effectiveness of the proposed 3D-AFM based on the MD-OCP is further confirmed by the 3D characterization of a micro-electromechanical system (MEMS) device with microcomb structures. Finally, this technique is applied to determine the critical dimensions (CDs) of a microarray chip. The experimental results regarding the CD parameters show that, in comparison with 2D technology, from which it is difficult to obtain sidewall information, the proposed method can obtain CD information for 3D structures with high precision and thus has excellent potential for 3D micro–nano manufacturing inspection.

Abstract Compact visible wavelength achromats are essential for miniaturized and lightweight optics. However, fabrication of such achromats has proved to be exceptionally challenging. Here, using subsurface 3D printing inside mesoporous hosts we densely integrate aligned refractive and diffractive elements, forming thin high performance hybrid achromatic imaging micro-optics. Focusing efficiencies of 51–70% are achieved for 15μm thick, 90μm diameter, 0.3 numerical aperture microlenses. Chromatic focal length errors of less than 3% allow these microlenses to form high-quality images under broadband illumination (400–700 nm). Numerical apertures upwards of 0.47 are also achieved at the cost of some focusing efficiency, demonstrating the flexibility of this approach. Furthermore, larger area images are reconstructed from an array of hybrid achromatic microlenses, laying the groundwork for achromatic light-field imagers and displays. The presented approach precisely combines optical components within 3D space to achieve thin lens systems with high focusing efficiencies, high numerical apertures, and low chromatic focusing errors, providing a pathway towards achromatic micro-optical systems.

To rapidly determine 9 biogenic amines including spermine, spermidine, putrescine, cadaverine, histamine, tryptamine, octopamine, tyramine and β-phenethylamine in aquatic products, the ultra high-performance liquid chromatography-tandem mass spectrometry method was established with Larimichthys polyactis using as matrix sample, and mobile phase and extract solvents were optimized. Matrix sample was extracted by 0.1% trichloroacetic acid (TCA) in duplicate, the supernatant was combined and filtered by 0.22 μm PTFE filters, and separated by HSS T3 column in 4 min. The mobile phase A was acetonitrile containing 0.1% formic acid, while mobile phase B was 10 mmol/L ammonium formate containing 0.2% formic acid. Nine biogenic amines were divided into three groups based on their different response intensity, with a concentration range of 1 to 40 ng/mL (tyramine and β-phenethylamine), 5 to 200 ng/mL (putrescine, cadaverine, histamine, tryptamine and octopamine), and 25 to 1000 ng/mL (spermine and spermidine). Their method quantification limits (LOQs) were 0.01, 0.05 and 0.25 mg/kg, respectively. The recovery rate of low, medium and high concentrations was 81.7%~111.2%, 80.0%~110.3% and 80.0%~99.0%, respectively, with the corresponding degree of precision (n=6) of 1.7%~7.3%, 1.3%~9.1% and 0.7%~4.6%. This method is simple, fast and with good sensitivity, recovery and precision, which is suitable for the determination of biogenic amines in Larimichthys polyactis.

Abstract The electrically driven large-load-ratio six-legged robot with engineering capability can be widely used in outdoor and planetary exploration. However, due to the particularity of its parallel structure, the effective utilization rate of energy is not high, which has become an important obstacle to its practical application. To research the power consumption characteristics of robot mobile system is beneficial to speed up it toward practicability. Based on the configuration and walking modes of robot, the mathematical model of the power consumption of mobile system is set up. In view of the tripod gait is often selected for the six-legged robots, the simplified power consumption model of mobile system under the tripod gait is established by means of reducing the dimension of the robot's statically indeterminate problem and constructing the equal force distribution. Then, the power consumption of robot mobile system is solved under different working conditions. The variable tendencies of the power consumption of robot mobile system are respectively obtained with changes in the rotational angles of hip joint and knee joint, body height, and span. The articulated rotational zones and the ranges of body height and span are determined under the lowest power consumption. According to the walking experiments of prototype, the variable tendencies of the average power consumption of robot mobile system are respectively acquired with changes in duty ratio, body height, and span. Then, the feasibility and correctness of theory analysis are verified in the power consumption of robot mobile system. The proposed analysis method in this paper can provide a reference on the lower power research of the large-load-ratio multi-legged robots.

The reusable launch vehicle (RLV) presents a new avenue for reducing cost of space transportation. The landing mechanism, which provides landing support and impact absorption, is a vital component of the RLV at final stage of recovery. This study proposes a novel legged deployable landing mechanism (LDLM) for RLV. The Watt-II six-bar mechanism is adopted to obtain the preferred configuration via the application of the linkage variation approach. To endow the proposed LDLM with advantages of large landing support region, lightweight, and reasonable linkage internal forces, a multi-objective optimization paradigm is developed. Furthermore, the optimal scale parameters for guiding the LDLM prototype design is obtained numerically using the non-dominated sorting genetic algorithm-II (NSGA-II) evolutionary algorithm. A fully-functional scaled RLV prototype is developed by integrating the gravity-governed deploying scheme to facilitate unfolding action to avoid full-range actuation, a dual-backup locking mechanism to enhance reliability of structure stiffening as fully deployed, and a shock absorber (SA) with multistage honeycomb to offer reliable shock absorbing performance. The experimental results demonstrate that the proposed LDLM is capable of providing rapid and smooth deployment (duration less than 1.5 s) with mild posture disturbance to the cabin (yaw and pitch fluctuations less than 6°). In addition, it provides satisfactory impact attenuation (acceleration peak less than 10g (g is the gravitational acceleration)) in the 0.2 m freefall test, which makes the proposed LDLM a potential alternative for developing future RLV archetype.

The most common cause of electric motor failure is the bearings, and so methods for fast and accurate diagnosis of motor bearing failure are urgently needed. Traditional fault diagnosis methods have high uncertainty and complexity since they require manual extraction of features. Deep learning has shown good performance in electrical equipment fault detection, and it can directly complete end-to-end diagnosis of motor faults, avoiding human involvement. Here, a new fault diagnosis method is presented which combines Gramian angular field (GAF) image coding, extreme learning machine (ELM) and convolutional neural network (CNN). The method has three main stages: First of all, GAF is utilized to convert the acquired vibration break signals into 2-D pictures. Next, the enhanced CNN model is taken to identify the elements of the converted image quickly and accurately. Finally, the ELM is used as the final classifier to gain further accuracy and diagnostic speed of fault classification. Experiments were designed to validate the proposed method using two different motor bearing fault datasets at Case Western Reserve University and autonomous experiment and performance is compared with several commonly used intelligent diagnosis algorithms. The proposed method’s accuracy in the experiment designed in this paper can reach 99.2% at most, and it only takes 0.835s to complete the diagnosis, which outperforms traditional diagnostic methods on both datasets and improving the maximum diagnostic accuracy by 33.6%. The findings indicate that this method can classify various fault types efficaciously, and has the benefits of quick diagnosis, high accuracy, and good generalization ability.

Objective: This study was aimed at investigating the correlations between heart failure onset age and all-cause mortality. Methods: The study examined 186,249 patients treated at Kailuan Group hospitals who underwent medical evaluations between 2006 and 2018. Biennial health assessments were conducted, and, as of December 31, 2020, 4022 heart failure instances were identified. For each patient with new-onset heart failure, four control participants were randomly selected, matched for age (within ±1 year) and sex. Cox regression models were used to calculate the hazard ratios of all-cause mortality across age groups. Results: The median follow-up duration was 5.25 (2.65, 8.63) years. All-cause mortality occurred in 1783 participants in the new-onset heart failure group and 2633 participants in in the control group. Refined multivariable Cox regression analysis revealed that patients with heart failure under 55 years of age had the highest relative mortality risk, with an HR (95% CI) 6.86 (4.42–10.64) with respect to their matched controls. Moreover, the relative mortality risk systematically decreased with increasing age of heart failure onset: HR (95% CI) 4.70 (3.73–5.92) for ages 55–64, HR (95% CI) 3.23 (3.73–3.81) for ages 65–74, and HR (95% CI) 1.69 (1.48–1.94) for 75 years or older. Conclusion: Heart failure significantly elevates the risk of all-cause mortality, and the risk is more pronounced with earlier manifestation of the condition.

The analysis of infrared video images is becoming one of the methods used to detect thermal hazards in many large-scale engineering sites. The fusion of infrared thermal imaging and visible image data in the target area can help people to identify and locate the fault points of thermal hazards. Among them, a very important step is the registration of thermally visible images. However, the direct registration of images with large-scale differences may lead to large registration errors or even failure. This paper presents a novel two-stage thermal–visible-image registration strategy specifically designed for exceptional scenes, such as a substation. Firstly, the original image pairs that occur after binarization are quickly and roughly registered. Secondly, the adaptive downsampling unit partial-intensity invariant feature descriptor (ADU-PIIFD) algorithm is proposed to correct the small-scale differences in details and achieve finer registration. Experiments are conducted on 30 data sets containing complex power station scenes and compared with several other methods. The results show that the proposed method exhibits an excellent and stable performance in thermal–visible-image registration, and the registration error on the entire data set is within five pixels. Especially for multimodal images with poor image quality and many detailed features, the robustness of the proposed method is far better than that of other methods, which provides a more reliable image registration scheme for the field of fire safety.

Abstract Background Perinatal blood including umbilical cord blood and placental blood are splendid sources for allogeneic NK cell generation with high cytotoxicity of combating pathogenic microorganism and malignant tumor. Despite the generation of NK cells from the aforementioned perinatal blood, yet the systematical and detailed information of the biological and transcriptomic signatures of UC-NKs and P-NKs before large-scale clinical applications in disease remodeling is still largely obscure. Methods Herein, we took advantage of the “3IL”-based strategy for high-efficient generation of NK cells from umbilical cord blood and placental blood (UC-NKs and P-NKs), respectively. On the one hand, we conducted flow cytometry (FCM) assay and coculture to evaluate the subpopulations, cellular vitality and cytotoxic activity of the aforementioned NK cells. On the other hand, with the aid of RNA-SEQ and multiple bioinformatics analyses, we further dissected the potential diversities of UC-NKs and P-NKs from the perspectives of transcriptomes. Results On the basis of the “3IL” strategy, high-efficient NKs were generated from mononuclear cells (MNCs) in perinatal blood. P-NKs revealed comparable ex vivo expansion but preferable activation and cytotoxicity upon K562 cells over UC-NKs. Both of the two NKs showed diversity in cellular vitality and transcriptome including apoptotic cells, cell cycle, gene expression profiling and the accompanied multifaceted biological processes. Conclusions Our data revealed the multifaceted similarities and differences of UC-NKs and P-NKs both at the cellular and molecular levels. Our findings supply new references for allogeneic NK cell-based immunotherapy in regenerative medicine and will benefit the further exploration for illuminating the underlying mechanism as well.

By spontaneously emitting midinfrared radiation to outer space through the atmospheric window and reflecting sunlight, daytime radiative coolers achieve notable passive cooling performance. However, existing daytime radiative cooling systems generally lack the ability to adaptively switch between heating and cooling states based on ambient conditions. Herein a passive thermal regulation system that features a temperature‐dependent switchable solar reflectance from 0.05 (low temperature) to 0.8 (high temperature) is presented. This, along with a ≈0.95 midinfrared emittance, it enables automatic switching between radiative cooling and solar heating. Switchablity is enabled using a poly(N‐isopropylacrylamide) (PNIPAM) hydrogel which exhibits high solar scattering above its tunable lower critical solution temperature (LCST) and transparency below its LCST. The lower part of the hydrogel is loaded with graphite to absorb solar energy in the heating state. In testing under sunny and partly cloudy outside conditions, this system maintains a temperature close to the set LCST.

Identifying terrain parameters is important for high-fidelity simulation and high-performance control of planetary rovers. The wheel–terrain interaction classes (WTICs) are usually different for rovers traversing various types of terrain. Every terramechanics model corresponds to its wheel–terrain interaction class (WTIC). Therefore, for terrain parameter identification of the terramechanics model when rovers traverse various terrains, terramechanics model switching corresponding to the WTIC needs to be solved. This paper proposes a speed-independent vibration-based method for WTIC recognition to switch the terramechanics model and then identify its terrain parameters. In order to switch terramechanics models, wheel–terrain interactions are divided into three classes. Three vibration models of wheels under three WTICs have been built and analyzed. Vibration features in the models are extracted and non-dimensionalized to be independent of wheel speed. A vibration-feature-based recognition method of the WTIC is proposed. Then, the terrain parameters of the terramechanics model corresponding to the recognized WTIC are identified. Experiment results obtained using a Planetary Rover Prototype show that the identification method of terrain parameters is effective for rovers traversing various terrains. The relative errors of estimated wheel–terrain interaction force with identified terrain parameters are less than 16%, 12%, and 9% for rovers traversing hard, gravel, and sandy terrain, respectively.

Moisture plays a pivotal role in the establishment of vegetation in sandy areas, underscoring the need to comprehend the water utilization strategies employed by established trees for the judicious use of water resources. Despite this significance, there exists a research gap concerning the water uptake patterns and consumption disparities between the dominant trees, namely the dry willow (Salix matsudana) and small-leafed poplar (Populus simonii), in the Mu Us sandy region. Consequently, our study sought to investigate the water utilization patterns and transpiration water consumption of these two plants. This was achieved through the analysis of hydrogen and oxygen isotope compositions in xylem water, soil water, and groundwater, coupled with the assessment of stem flow rates of tree trunks. The findings reveal that both Salix matsudana and Populus simonii exhibited variations in soil water content with soil depth, characterized by an initial increase followed by a subsequent decrease. During the months of July, August, and September, both species demonstrated the ability to absorb water from multiple sources concurrently. Specifically, Salix matsudana and Populus simonii predominantly utilized middle and shallow soil water sources in July and September, respectively. However, in August, both species primarily relied on shallow soil water for absorption. Over the period from July to September, the sap flow rate of Salix matsudana surpassed that of Populus simonii by 1888.2 mL·h−1 to 2499.04 mL·h−1, representing a 1.5 to 2.2 times increase. This underscores the necessity for Salix matsudana to draw water from middle and deep soil layers to compensate for shallow water deficits. In summary, schemes for the establishment of vegetation in sandy areas should consider the dynamic nature of water uptake and evapotranspiration, emphasizing the importance of regulating these processes for efficient water conservation and utilization.

Abstract Soft robots have become important members of the robot community with many potential applications owing to their unique flexibility and security embedded at the material level. An increasing number of researchers are interested in their designing, manufacturing, modeling, and control. However, the dynamic simulation of soft robots is difficult owing to their infinite degrees of freedom and nonlinear characteristics that are associated with soft materials and flexible geometric structures. In this study, a novel multi-flexible body dynamic modeling and simulation technique is introduced for soft robots. Various actuators for soft robots are modeled in a virtual environment, including soft cable-driven, spring actuation, and pneumatic driving. A pneumatic driving simulation was demonstrated by the bending modules with different materials. A cable-driven soft robot arm prototype and a cylindrical soft module actuated by shape memory alley springs inspired by an octopus were manufactured and used to validate the simulation model, and the experimental results demonstrated adequate accuracy. The proposed technique can be widely applied for the modeling and dynamic simulation of other soft robots, including hybrid actuated robots and rigid-flexible coupling robots. This study also provides a fundamental framework for simulating soft mobile robots and soft manipulators in contact with the environment.

Increasingly, the melting of Arctic ice due to global warming has provided opportunities for commercial shipping between Asia and Europe. Given the vulnerability of the Arctic environment, especially due to emissions of short-lived pollutants from shipping activities, a more effective propulsion system with a comprehensive control strategy is required to reduce fuel consumption, thus potentially mitigating the impacts of shipping activities on the northern sea route (NSR). In this paper, a shipboard DC hybrid system powered by a combination of diesel generator sets and batteries is proposed and analysed in terms of its application on a ship in the NSR. The specific fuel consumption and various losses in the power sources were analysed to develop an efficiency-optimisation control strategy for the proposed DC hybrid power system. To evaluate the performance of the hybrid power system with the proposed optimisation control strategy, lab-scale experiments have been conducted in the Shanghai Marine Diesel Engine Research Institute to compare the proposed system with a conventional hybrid system. The experimental results indicate that the proposed DC hybrid power plant with the energy optimisation control contributes a 5.35% fuel saving compared with the DC fixed-speed diesel electric configuration during a scaled-down NSR scenario.

In order to investigate the landing process of a vertical landing reusable vehicle, a dynamic model with a complex nonlinear dissipative element is established based on the discrete impulse step approach, which includes a three-dimensional multi-impact model considering friction and material compliance, and a multistage aluminum honeycomb theoretical model. The normal two-stiffness spring model is adopted in the foot–ground impact model, two motion patterns (stick and slip) are considered on the tangential plane and the structural changes caused by buffering behavior are included, and the energy conversion during the impact follows the law of conservation of energy. The state transition method is used to solve the dynamic stability convergence problem of the vehicle under the coupling effect of impact and buffering deformation in the primary impulse space. Landing experiments on a scaled physical reusable vehicle prototype are conducted to demonstrate that the theoretical results exhibit good agreement with the experimental data.

Objectives Many factors should be taken into consideration in the selection of a fin stabilizer for a polar cruise ship, such as environmental adaptation and device performance, and it should not be chosen via subjective judgment. To this end, a comprehensive fin stabilizer selection and evaluation method based on a combined weighting-TOPSIS (technique for order preference by similarity to ideal solution) method is proposed. Methods The selection principle of a fin stabilizer for a polar cruise ship with a gross tonnage of 8 035 is confirmed according to the PC6 guidelines for polar class ships, and the initial area of the fin stabilizer is calculated using the experimental mathematical model. Three design schemes are put forward for the fin stabilizer. A selection and evaluation index system is built by consulting experts, including five primary indexes and fourteen secondary indexes. The weights of the indexes are determined using the analytic hierarchy process (AHP) and entropy weight method (EWM), and the TOPSIS method is introduced to calculate the degree of closeness between the alternative and ideal solutions. Results According to the ranking results, Design Scheme 1 (Aquarius A100 fin stabilizer) is selected as the optimal fin stabilizer for meeting the decision-makers' requirements. Conclusions The method proposed in this study has valuable guiding significance for the selection of fin stabilizers for polar cruise ships by reducing the blindness of the shipbuilders and decision-makers.

Pseudomonas aeruginosa is an opportunistic pathogen that infects patients by regulating virulence factors and biofilms through a quorum sensing (QS) system to protect itself from antibiotics and environmental stress. Therefore, the development of quorum sensing inhibitors (QSIs) is expected to become a new strategy for studying drug resistance to P. aeruginosa infections. Marine fungi are valuable resources for screening QSIs. A marine fungus, Penicillium sp. JH1, with anti-QS activity was isolated from the offshore waters of Qingdao (China), and citrinin, a novel QSI, was purified from secondary metabolites of this fungus. Citrinin could significantly inhibit the production of violacein in Chromobacterium violaceum CV12472 and the production of three virulence factors (elastase, rhamnolipid and pyocyanin) in P. aeruginosa PAO1. It could also inhibit the biofilm formation and motility of PAO1. In addition, citrinin downregulated the transcript levels of nine genes (lasI, rhlI, pqsA, lasR, rhlR, pqsR, lasB, rhlA and phzH) associated with QS. Molecular docking results showed that citrinin bound to PqsR and LasR with better affinity than the natural ligands. This study laid a foundation for the further study of the structure optimization and structure–activity relationship of citrinin.

Innovation training in engineering is an important facet of technical education in universities across China. Robotics training is a new course that has been developed using an informatization-based engineering training system at the Engineering Innovation Practice Center of the Harbin Institute of Technology. In this study, we propose a pedagogical method that combines virtual and on-site modes of teaching. Feedback obtained from students enrolled in the course showed a positive response to it. They claimed that they gained valuable knowledge from it that enabled them to carry out the relevant tasks in a better manner and more quickly. The feasibility of the proposed scheme was verified by a virtual simulation.

To precisely and timely control the shaft speed for a marine electric propulsion system under normal sea conditions, a new shaft speed control technique combining the evidential reasoning rule with the traditional PID controller was proposed in this study. First, an intelligent adaptive PID controller based on the evidential reasoning rule was designed for a marine electric propulsion system to obtain the PID parameters KP, KI, and KD. Then, a local iterative optimization strategy for model parameters was proposed. Furthermore, the parameters of the adaptive PID controller model were optimized in real time by using the sequential linear programming algorithm, which enabled the adaptive adjustment of KP, KI, and KD. Finally, the performance of the adaptive PID controller regarding the shaft speed control was compared with that of other controllers. The results showed that the adaptive PID controller designed in this study had better control performance, and the shaft speed control method based on the adaptive PID controller could better control the shaft speed of the marine electric propulsion system.

Abstract Background Arginine vasopressin (AVP) is elevated in patients with heart failure, and the increase in the AVP concentration in plasma is positively correlated with disease severity and mortality. Metoprolol (Met) is a beta blocker that is widely used in the clinic to treat pathological cardiac hypertrophy and to improve heart function. However, the specific mechanism by which Met alleviates AVP-induced pathological cardiac hypertrophy is still unknown. Our current study aimed to evaluate the inhibitory effects of Met on AVP-induced cardiomyocyte hypertrophy and the underlying mechanisms. Methods AVP alone or AVP plus Met was added to the wild type or AKT1-overexpressing rat cardiac H9C2 cell line. The cell surface areas and ANP/BNP/β-MHC expressions were used to evaluate the levels of hypertrophy. Western bolting was used to analyze AKT1/P-AKT1, AKT2/P-AKT2, total AKT, SERCA2, and Phospholamban (PLN) expression. Fluo3-AM was used to measure the intracellular Ca2+ stores. Results In the current study, we found that AKT1 but not AKT2 mediated the pathogenesis of AVP-induced cardiomyocyte hypertrophy. Sustained stimulation (48 h) with AVP led to hypertrophy in the H9C2 rat cardiomyocytes, resulting in the downregulation of AKT1 (0.48 fold compared to control) and SERCA2 (0.62 fold), the upregulation of PLN (1.32 fold), and the increase in the cytoplasmic calcium concentration (1.52 fold). In addition, AKT1 overexpression increased the expression of SERCA2 (1.34 fold) and decreased the expression of PLN (0.48 fold) in the H9C2 cells. Moreover, we found that Met could attenuate the AVP-induced changes in AKT1, SERCA2 and PLN expression and decreased the cytoplasmic calcium concentration in the H9C2 cells. Conclusions Our results demonstrated that the AKT1–SERCA2 cascade served as an important regulatory pathway in AVP-induced pathological cardiac hypertrophy.

A novel convolutional neural network namely the modified CNN-GAP model is proposed for fast fault diagnosis of the DC-DC inverter. This method improves the model structure of the traditional CNN by using a global average pooling layer to replace the fully connected layer of 2~3 layers. The improved CNN-GAP method mainly contains an input layer, a feature extraction layer, a global average pooling (GAP) layer, and a Softmax output layer. Firstly, the raw 1-D time-series data directly input into the input layer of the established CNN-GAP diagnosis model. The 2-D feature maps are reconstructed in the input layer. Secondly, the representative features are automatically extracted from the 2-D feature maps by using multiple convolutional layers and pooling layers. Thirdly, the dimension transformation and size compression of the output image of the feature extraction layer is completed by the GAP layer. Finally, the fault diagnosis result of the DC-DC inverter is automatically output in the Softmax output layer. The proposed method is used for diagnosing the open-circuit fault of the IGBT in the isolated DC-DC inverter. The proposed method is more accurate and effective than other mainstream intelligent diagnosis methods including the SVM, KNN, DNN, and traditional CNN. The experiment results show that the diagnostic accuracy is up to 99.95%, and the testing time can reduce by more than 15%. The improved CNN-GAP method could greatly reduce the model parameter quantity of the traditional CNN more than 80%, which is more suitable for rapid fault diagnosis in electronic devices.

Legged robots have demonstrated significant achievements in recent years. Some legged robots have considerable flexibility and movement abilities. However, certain obstacles restrict the practical application of legged robots, such as their high energy consumption. The energy consumption of a legged robot is significantly higher than that of a wheel robot of the equivalent size for the same walking distance. Reducing the energy consumption of legged robots is important for their practical application and further development. This study proposes a quadratic-programming force-distribution controller, which minimizes the energy consumption of hexapod robots. The controller reduces the energy consumption by optimizing the instantaneous power of the robot at each time step. In a simulation environment, the proposed method reduced the energy consumption by up to 9.43% and 6.30% in flat terrain and sloped terrain, respectively, compared with two other methods. In hexapod robot experiment, the proposed method can reduce energy consumption by 5.72% compared with position control.

The tractive capability of Multi-wheeled mobile robots (WMRs) depends heavily on the performance of the driving control of their steerable wheels. The recently proposed concept of a pseudo-driven wheel (PDW) provides a simple, versatile, and effective way to improve the tractive capability. However, its focus is to eliminate the internal forces between the wheels and save energy; therefore, it cannot provide sufficient driving force to the body on rough terrains. This paper proposes a control strategy to adjust the power output of a PDW along the directions of two degrees of freedom, an approach that improves the trajectory tracking control of WMRs, especially on rough terrains. To achieve the desired active following control of the PDW, an active disturbance rejection controller is used to compensate the disturbances arising from the wheel-terrain interaction. The Adams and Simulink are used for a joint simulation of which results show that the proposed control strategy is necessary for accurate trajectory tracking, for which the feasibility and effectiveness were verified by the experimental results. In summary, the trajectory tracking accuracy of the WMR and its tractive capabilities on rough, sandy terrain were improved.

A recursive dynamic modeling and control is presented for the dual-arm manipulator with elastic joints carrying a common object. For decoupling the effect of elastic joints, the dynamic modeling approach is based on the classic recursive Newton-Euler method but involves high-order derivatives of motion and force variables. The high-order inverse kinematics which is needed in the motion control is presented firstly. With the classic Recursive Newton-Euler (RNEA) method, two-order dynamic model of the dual-arm robot is established, for decoupling the effect of the elastic joints, the form of four-order dynamic model is presented, meanwhile, combining with the high-order dynamic model of the carried object, the completed Dual-Arm Elastic Joints Newton-Euler Algorithm (DA-EJNEA) is established. Then the feedback linearization method is adopted to the motion control based on the DA-EJNEA. Finally, to verify the effectiveness of the proposed method, feedback linearization method based on the DA-EJNEA and the computed torque method based on the dynamic model with rigid joints are used to control the dual-arm coordinated system respectively, the simulation results illustrate the feedback linearization method based on the DA-EJNEA has an obvious advantage for trajectory tracking of the object in the operation space, it behaves reasonable potentials for the model-based control.

The task of performing locomotion and manipulation simultaneously poses several scientific challenges, such as how to deal with the coupling effects between them and how to cope with unknown disturbances introduced by manipulation. This paper presents an inverse dynamics-based whole-body controller for a torque-controlled quadrupedal manipulator capable of performing locomotion while executing manipulation tasks. Unlike existing methods that deal with locomotion and manipulation separately, the proposed controller can handle them uniformly, which can take into account the coupling effects between the base, limbs and manipulated object. The controller tracks the desired task–space motion references based on a hierarchical optimization algorithm, given a set of hierarchies that define strict priorities and the importance of weighting each task within a hierarchy. The simulation results show the robot is able to follow multiple task–space motion reference trajectories with reasonable deviation, which proved the effectiveness of the proposed controller.

It is important for Mars exploration rovers to achieve autonomous and safe mobility over rough terrain. Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the accuracy of terrain classification has been less than 90% in read operations. A high-accuracy vision-based method for Mars terrain classification is presented in this paper. By analyzing Mars terrain characteristics, novel image features, including multiscale gray gradient-grade features, multiscale edges strength-grade features, multiscale frequency-domain mean amplitude features, multiscale spectrum symmetry features, and multiscale spectrum amplitude-moment features, are proposed that are specifically targeted for terrain classification. Three classifiers, K-nearest neighbor (KNN), support vector machine (SVM), and random forests (RF), are adopted to classify the terrain using the proposed features. The Mars image dataset MSLNet that was collected by the Mars Science Laboratory (MSL, Curiosity) rover is used to conduct terrain classification experiments. The resolution of Mars images in the dataset is 256 × 256. Experimental results indicate that the RF classifies Mars terrain at the highest level of accuracy of 94.66%.

With the rapid development of network technology, more and more digital images are transmitted on the network, and gradually become one important means for people to access the information. The security problem of the image information data increasingly highlights and has become one problem to be attended. The current image encryption algorithm basically focuses on the simple encryption in the frequency domain or airspace domain, and related methods also have some shortcomings. Based on the characteristics of wavelet transform, this paper puts forward the image compression and encryption based on the wavelet transform and chaos by combining the advantages of chaotic mapping. This method introduces the chaos and wavelet transform into the digital image encryption algorithm, and transforms the image from the spatial domain to the frequency domain of wavelet transform, and adds the hybrid noise to the high frequency part of the wavelet transform, thus achieving the purpose of the image degradation and improving the encryption security by combining the encryption approaches in the spatial domain and frequency domain based on the chaotic sequence and the excellent characteristics of wavelet transform. Testing experiments show that such algorithm reduces the memory consumption and implements the complexity, not only can decrease the key spending and compress the time spending, but also can improve the quality of decoded and reconstructed image, thus showing good encryption features with better encryption effect.

Image enhancement is a very important process of image preprocessing and it plays a critical role in the improvement of image quality and the follow-up image analysis, which makes the research of image enhancement algorithm a hot research field. Image enhancement not only needs to strengthen image determination and recognition, but also needs to avoid the consequential color distortion. Pseudo-color enhancement is the technique to map different gray scales of a black-and-white image into a color image. As humans have extremely strong ability in distinguishing different colors visually and relatively weak capacity in discriminating gray scales, so, color the gray-scale changes which cannot be differentiated by human eyes so that they can tell them apart. The mapping function in conventional gray-scale transform method is not working well in dark and low-contrast images. So, this paper comes up with an improved gray-scale transformation algorithm. This algorithm can achieve the enhancement, preserve the image colors, process dark and low-contrast images, reinforce the enhancement and improve the blocking effect. The experiment proves that the enhanced image obtained by the method of this paper can have improved average brightness, natural colors and more detail information and it has good application value.

The articulated rotating speed is one of the important parameters to determine the drive devices and actuating devices of joints for legged robots. Compared with the small-scale multi-legged robots, the range of the output speed of the joint should be as accurate as possible for the large-load-ratio multi-legged robots. To reasonably select the devices of joints, the maximum walking speed method is proposed to quickly and accurately obtain the range of articulated rotating speed by taking an electrically driven large-load-ratio six-legged robot as an example. To prove the rapidity, accuracy, and conciseness of the maximum walking speed method, the analyses of the forward kinematics and inverse kinematics of the robot are implemented based on the Denavit-Hartenberg method. However, only one range of articulated rotating speed is effectively confirmed in a single leg. Through rotating one of the joints to achieve the maximum speed index of the robot, the maximum walking speed method is employed to establish the mathematical relationships between the articulated rotating angles and the maximum walking speed index of the robot. The ranges of the output speeds of all joints are accurately obtained. The simulation verification and walking experiments of the prototype are, respectively, carried out. The results of the simulation and walking experiments show that the maximum walking speed method is reasonable and effective in calculating the range of articulated rotating speed. The proposed method in this paper can be reliably applied to the development of large-load-ratio multi-legged robots.

The foot-terrain interaction mechanics of human is important in understanding the biped locomotion mechanism. This paper aims to investigate the effect of walking speed and the hardness of the terrain on foot-terrain interaction mechanics. In the experiments, mobile portable plantar mechanical measuring insoles with 107 sensor elements were used to gauge the plantar force and contact area in real-time. Seven subjects with healthy feet have participated in typical experiments. The statistical methods including correlation analysis, univariance analysis, and systematic identification are primarily used to obtain several main results. Bimodal functions in describing the total pressure-time relations were performed at slow and regular speeds but only one hump is left if walking at fast paces; they can be unified and summed by three subsections' unimodal functions. The forefoot produces a large peak impact force in slow and regular quasi-static walking on both hard ground and deformable terrains, while the heel absorbs more dynamic impact shocks in fast walking on hard ground. The torques generated by the ankle joints are calculated based on plantar force or its derived information such as zero moment point. Some implications are drawn, for example, the dynamically changed positions of zero moment point (ZMP) for humans are similar to that generated by reptiles wiggling through the flowable terrains; increasing the stride length on the hard ground produces more impact vibrations than quickening paces but more effective in accelerating walks on deformable terrains.

This paper discusses a problem that has plagued researchers for a long time regarding the dynamic modeling of a multiple degree-of-freedom (multi-DOF) manipulator such that its manipulation exhibits a higher computational efficiency and accuracy. Unknown friction, unknown gravitational torque, an uncertain moment of inertia, and severe joint coupling are the primary disturbing factors in multi-DOF manipulator modeling. In addition, joint flexible problems caused by the integration of harmonic drives increase the modeling complexity. Hitherto, no effective method has been found to address these problems. The virtual decomposition (VD)-based method exhibits the advantages of joint dynamics decoupling and minor computation compared with the traditional Lagrangian formulation or Newton-Euler formulation. In this study, an estimation method for the deformation-related torque of harmonic drives is established based on a novel experimental model; subsequently, this method is utilized in the VD-based model for the multi-DOF manipulator. Hence, the decoupling dynamic model for the manipulator considering joint flexibility is established. The performance of this new method has been evaluated by a contrast simulation with the Newton-Euler formulation, and the multi-DOF manipulator control simulation and experiment have been conducted with a VD-based model as a feedforward compensator to verify its performance in real-time control. The results demonstrated the validity and efficiency of e proposed approach.

Door-opening is a critical problem for robots used for rescue purposes in nuclear power plants (NPPs). A force and torque (F/T) sensor is not used in the rescue task for door-opening in the NPPs environment. It is, therefore, necessary to study the dynamic model of door-opening. The NPPs door and a door handle dynamic model are significant to the mechanical design, simulation, and dynamic feed-forward control of an NPP robot. To obtain the accurate force data of door-opening, a method based on a gravity compensation algorithm combined with a transformation of the contact force of the end effector is proposed. This paper analyzes the structural features and dynamics model of a fire door and a door handle. Methods for identifying the model parameters are also developed by combining a Nelder-Mead simplex search with the least-squares algorithm. The extensive door-opening experiments were carried out in this paper, and the results validate the fidelity of the derived dynamic models of the door and the door handle.

This article presents a centroid variability model–based controller of HITUWV (Underwater Welding Vehicle by Harbin Institute of Technology), an underwater welding vehicle, for automatic welding with high stability and accuracy. First, an accurate centroid variability model, which considers the coefficient changes of the HITUWV caused by the movements of a 3-degree-of-freedom manipulator, is presented to perform the dynamic characteristics of the HITUWV precisely. Second, a centroid variability model–based adaptive sliding model controller is developed for the HITUWV to complete centroid variability compensation. Experimental results indicate that the proposed centroid variability model–based adaptive sliding model controller demonstrates better performances in stability and accuracy than the conventional proportional–integral–derivative controller and the model-based proportional–integral–derivative controller. As a result, the centroid variability model–based adaptive sliding model controller holds great practicality and utility on the control of underwater operation with high stability and accuracy.

Industrial robots have advantages in the processing of large-scale components in the aerospace industry. Compared to CNC machine tools, robot arms are cheaper and easier to deploy. However, due to the poor consistency of incoming materials, large-scale and lightweight components make it difficult to automate robotic machining. In addition, the stiffness of the tandem structure is quite low. Therefore, the stability of the milling process is always a concern. In this paper, the robotic milling research is carried out for the welding pre-processing technology of large-scale components. In order to realize the automatic production of low-conformity parts, the on-site measurement–planning–processing method is adopted with the laser profiler. On the one hand, the laser profiler hand–eye calibration method is optimized to improve the measurement accuracy. On the other hand, the stiffness of the robot’s processing posture is optimized, combined with the angle of the fixture turntable. Finally, the experiment shows the feasibility of the on-site measurement–planning–processing method and verifies the correctness of the stiffness model.

A symmetrical hybrid driving waveform (SHDW) is proposed in this paper, which includes a symmetrical saw-tooth driving waveform and a sinusoidal friction regulation waveform, and the sinusoidal friction regulation waveform is applied to the shrinkage period of the symmetrical saw-tooth driving waveform. In other words, the proposed SHDW can be achieved when the waveform symmetry of the hybrid driving method is 50%. The SHDW can effectively drive the designed symmetrical linear piezoelectric stickslip actuator, and the motion direction is also easily regulated. The excitation principle of the actuator excited by the SHDW is explained in detail. A prototype is fabricated and the experimental investigations of the actuator characteristics are carried on. The higher velocity and the larger driving capacity are realized using by the SHDW relative to the asymmetrical hybrid driving waveform. Testing results show that the prototype excited by the SHDW can obtain the peak no-load speeds of 0.41 and 0.39 mm/s in the forward and reverse directions when the saw-tooth driving waveform voltage is 10 Vp-p for 800 Hz and the sinusoidal friction regulation waveform voltage is 2 Vp-p for 39 kHz. The step efficiencies can reach 92% and 90%. The driving capacities can reach 10.52 and 9.85 [(mm/s)g/mW] with the load of 70 g under the locking force of 0.1 N. The actuator excited by the SHDW will make it ideal for miniature information technology devices.

Wheeled mobile robots are widely utilized for environment-exploring tasks both on earth and in space. As a basis for global path planning tasks for wheeled mobile robots, in this study we propose a method for establishing an energy-based cost map. Then, we utilize an improved dual covariant Hamiltonian optimization for motion planning method, to perform point-to-region path planning in energy-based maps. The method is capable of efficiently handling high-dimensional path planning tasks with non-convex cost functions through applying a robust active set algorithm, that is, non-monotone gradient projection algorithm. To solve the problem that the path planning process is locked in weak minima or non-convergence, we propose a randomized variant of the improved dual covariant Hamiltonian optimization for motion planning based on simulated annealing and Hamiltonian Monte Carlo methods. The results of simulations demonstrate that the final paths generated can be time efficient, energy efficient and smooth. And the probabilistic completeness of the method is guaranteed.

We propose a new local path planning approach based on optimization methods with probabilistic completeness in this article. This approach adds a linear constraint to the original covariant Hamiltonian optimization for motion planning problem with a new cost function. By deducing the dual form, the path planning problem is described as a box-constrained quadratic programming problem. The nonmonotone gradient projection algorithm is introduced to solve the dual problem, which makes the algorithm adaptable to non-convex cost functions. In order to prevent early convergence at local minima that can occur when applying optimization methods, this article introduces Hamiltonian Monte Carlo to the modification, which constantly forces the initial path to jump out of the local extremum, thus improving the robustness and success rate of the path planning approach. Compared with other methods through simulations, this approach is proven to provide balanced planning efficiency and path quality. The feasibility in a real environment is experimentally validated by applying the approach to a wheeled mobile robot.

In this article, a novel localization approach incorporating attitude and heading reference system and underwater altimeters is presented to accurately localize the underwater welding vehicles in spent fuel pools of the nuclear power stations. Different from the conventional underwater localization technologies, the presented localization approach is a more suitable approach in cases of confined structured water areas. Firstly, a multi-regions division localization algorithm is proposed for calculating the coordinate of the underwater welding vehicle through data from sensors. Also, considering the attitude errors of the underwater welding vehicle, the beam angle of the altimeters, and the boundary effects of cross-regions, an optimized multi-regions division localization algorithm is introduced for general applicability of the multi-regions division localization. Then, computer simulations are employed to evaluate the validity and the performance of multi-regions division localization and optimized multi-regions division localization. Finally, the efficiency of the proposed approach is confirmed via system experiments. The experimental results are consistent with simulation results which further indicate that the presented approach holds great potential in effective underwater vehicles localization for confined structured water scenarios.

Wheeled mobile robots (WMRs) in real complex environments such as on extraterrestrial planets are confronted with uncertain external disturbances and strong coupling of wheel-ground interactions while tracking commanded trajectories. Methods based on sliding mode control (SMC) are popular approaches for these situations. Traditional SMC has some potential problems, such as slow convergence, poor robustness, and excessive output chattering. In this paper, a kinematic-based feed-forward control model is designed for WMRs with longitudinal slippage and applied to the closed-loop control system for active compensation of time-varying slip rates. And a new adaptive SMC method is proposed to guide a WMR in trajectory tracking missions based on the kinematic model of a general WMR. This method combines the adaptive control method and a fast double-power reaching law with the SMC method. A complete control loop with active slip compensation and adaptive SMC is thus established. Simulation results show that the proposed method can greatly suppress chattering and improve the robustness of trajectory tracking. The feasibility of the proposed method in the real world is demonstrated by experiments with a skid-steered WMR on the loose-soil terrain.

It is necessary to switch the control strategies for propulsion system frequently according to the changes of sea states in order to ensure the stability and safety of the navigation. Therefore, identifying the current sea state timely and effectively is of great significance to ensure ship safety. To this end, a reasoning model that is based on maximum likelihood evidential reasoning (MAKER) rule is developed to identify the propeller ventilation type, and the result is used as the basis for the sea states identification. Firstly, a data-driven MAKER model is constructed, which fully considers the interdependence between the input features. Secondly, the genetic algorithm (GA) is used to optimize the parameters of the MAKER model in order to improve the evaluation accuracy. Finally, a simulation is built to obtain experimental data to train the MAKER model, and the validity of the model is verified. The results show that the intelligent sea state identification model that is based on the MAKER rule can identify the propeller ventilation type more accurately, and finally realize intelligent identification of sea states.

In some hazardous or inaccessible applications, such as earthquake rescue, as a substitute for mankind, robots are expected to perform missions reliably. Unfortunately, the failure of components is difficult to avoid due to the complexity of robot composition and the interference of the environment. Thus, improving the reliability of robots is a crucial problem. The hexapod robot has redundant degrees of freedom due to its multiple joints, making it possible to tolerate the failure of one leg. In this paper, the Fault-Tolerant Tripod (F-TT) gait dealing with the failure of one leg is researched. The Denavit–Hartenberg (D-H) method is exploited to establish a kinematic model for the hexapod robot, the Jacobian matrix is analyzed, and it is proved that the body can be controlled when three legs are supported. Then, an F-TT gait phase sequence planning method based on a stability margin is established, and a method to improve stability is proposed. The trajectory for the center of gravity (COG) and foot is studied. Finally, a simulation model and prototype robot experiments are developed, and the effectiveness of the proposed method is verified.

Due to the rising price of rare-earth materials, permanent-magnet (PM) machines in different applications have a trend of reducing the use of rare-earth materials. Since iron-core poles replace half of PM poles in the consequent pole (CP) rotor, the PM machine with CP rotor can be a promising candidate for less rare-earth PM machine. Additionally, the investigation of CP rotor in special electrical machines, like hybrid excitation permanent-magnet PM machine, bearingless motor, etc., has verified the application feasibility of CP rotor. Therefore, this paper focuses on design and performance of PM machines when traditional PM machine uses the CP rotor. In the CP rotor, all the PMs are of the same polarity and they are inserted into the rotor core. Since the fundamental PM flux density depends on the ratio of PM pole to iron-core pole, the combination rule between them is investigated by analytical and finite-element methods. On this basis, to comprehensively analyze and evaluate PM machine with CP rotor, four typical schemes, i.e., integer-slot machines with CP rotor and surface-mounted PM (SPM) rotor, fractional-slot machines with CP rotor and SPM rotor, are designed to investigate the performance of PM machine with CP rotor, including electromagnetic performance, anti-demagnetization capacity and cost.

In order to meet the demands of small race car dynamics simulation, a new method of parameter identification in the Magic Formula tire model is presented in this work, based on an analysis of the Magic Formula tire model structure. A high-precision tire model used for vehicle dynamics simulation is established via this method. It is difficult for students to build a high-precision tire model because of the complexity of widely used tire models such as Magic Formula and UniTire. At a pure side slip condition, building a lateral force model is an example, which illustrate the utilization of a multilayer feed-forward neural network to build an intelligent tire model conveniently. In order to fully understand the difference between the two models, a two-degrees-of-freedom (2 DOF) vehicle model is established. The advantages, disadvantages, and applicable scope of the two tire models are discussed after comparing the simulation results of the 2 DOF model with the Magic Formula and intelligent tire model.

This study firstly analyses the driver’s manipulation behaviour and relates the different components of the driver model. Then, a model controlling the driver directions is built according to the prediction-follower theory with the aim of improving the point search algorithm. A model of the driving system of an electric vehicle is used to establish the longitudinal speed control model of the driver by using a feedforward-PID feedback control strategy. Our approach is to release the coupling between direction and speed control and build an integrated model that includes the direction and speed for an arbitrary path. Finally, the characteristics of an actual racing track are considered to establish the fastest driver control model. We simulated the typical operating conditions of our driver operation model. The simulation confirmed the effectiveness of the improved predictive point search algorithm and the integrated driver model to control the direction and speed for an arbitrary path.

The growth of the Internet of Things (IOT) industry has become a new mark of the communication domain. As the development of the technology of the IOT and the fiber-optical sensor, the combination of the both is a big question to be discussed, and the fiber-optical IOT also has a good development prospect. This article first introduces IOT’s current status, the key technology, the theoretical frame and the applications. Then, it discusses the classification of the optical fiber sensor as well as the development and its application’s situation. Lastly, it puts the optical fiber sensing technology into the IOT, and introduces a specific application which is used in the mine safety based on the fiber-optical IOT.

We have systematically studied Zinc Oxynitride (ZnON) materials for thin film transistors in advanced display applications. The ZnON materials were deposited using a reactive sputtering process with a metallic Zn target in a gas mixture of Ar, N2 and O2. We previously optimized the gas mixture and RF power and found optimized process parameters for O2 and N2 flow rates and RF power. In this contribution, we report the material properties as a function of process pressure and temperature. We observed that at a relatively lower pressure of 5 mTorr, the material shows a cubic Zn3N2-like structure with a narrow band gap of 1.1 eV, high conductivity, high carrier density, and high carrier mobility; at medium pressure of approximately 13-15 mTorr, the material becomes nanostructured or amorphous ZnON (nc-ZnON or a-ZnON) with a band gap of approximately 1.3-1.5 eV; and at pressures higher than 20 mTorr, the material shows a hexagonal polycrystalline ZnO-like structure with a band gap of 3.1 eV. The deposition rate decreases but the band gap increases monotonically with increasing pressure; the Hall electron mobility decreases with increasing pressure in the range from 5 mTorr to 13 mTorr and changes very little in the high pressure regime; the conductivity and carrier density decrease with the increase of pressure from 5 mTorr to 17 mTorr and then increase with further increase of pressure, which is related to the material structure changes from Zn3N2-like to a-ZnON, and then to ZnO-like materials. The substrate temperature has little effect on the material properties. Increasing substrate temperature slightly increases the band gap, carrier concentration, and conductivity, but slightly decreases the carrier mobility. Finally, under the optimized conditions, the nc-ZnON films are made with an optical band gap of 1.3-1.5 eV, electron mobility above 80 cm2/Vs and electron density of 1 × 1018 cm-3, which are suitable for high quality TFTs in advanced display applications.