An improved longitudinal vibration model and dynamic characteristic of sucker rod string

Considering the influence of the nonlinear characteristics of plunger load and the friction of sucker rod string (SRS) on the SRS’s longitudinal vibration, an improved simulation model of SRS’s longitudinal vibration is derived. In the details, based on the flow characteristic of non-Newtonian power law fluid (NNPLF), a velocity model of NNPLF between pump plunger and pump barrel is established. Then the law of the velocity distribution is solved out with Lagrange multiplier method. Therefore, with the law of the velocity distribution of NNPLF, the computing models of nonlinear friction of pump plunger and clearance leakage between pump plunger and barrel are derived. Taking account of the influence of some parameters on the plunger load, such as plunger friction, hydraulic loss of pump and clearance leakage, an improved simulation model of plunger load is derived. The dynamic response is solved out with fourth order Runge-Kutta method. Comparing experiment results with simulated results, good agreement is found, which shows the simulation model is feasible. The influences of the different parameters on pump pressure and pump plunger load are analyzed, such as stroke number, power law exponent, consistency coefficient and gap between plunger and pump barrel. Simulation result indicates that the opening time of standing valve and traveling valve is affected by the parameters, and the maximum and minimum loads of pump plunger are affected by stroke number. In addition, the influence of SRS absorber on SRS’s longitudinal vibration is analyzed

A New Terrain Classification Framework Using Proprioceptive Sensors for Mobile Robots

Mobile robots that operate in real-world environments interact with the surroundings to generate complex acoustics and vibration signals, which carry rich information about the terrain. This paper presents a new terrain classification framework that utilizes both acoustics and vibration signals resulting from the robot-terrain interaction. As an alternative to handcrafted domain-specific feature extraction, a two-stage feature selection method combining ReliefF and mRMR algorithms was developed to select optimal feature subsets that carry more discriminative information. As different data sources can provide complementary information, a multiclassifier combination method was proposed by considering a priori knowledge and fusing predictions from five data sources: one acoustic data source and four vibration data sources. In this study, four conceptually different classifiers were employed to perform the classification, each with a different number of optimal features. Signals were collected using a tracked robot moving at three different speeds on six different terrains. The new framework successfully improved classification performance of different classifiers using the newly developed optimal feature subsets. The greater improvement was observed for robot traversing at lower speeds

Hierarchical Structure with Highly Ordered Macroporous-Mesoporous Metal-Organic Frameworks as Dual Function for CO2 Fixation.

As a major greenhouse gas, the continuous increase of carbon dioxide (CO2) in the atmosphere has caused serious environmental problems, although CO2 is also an abundant, inexpensive, and nontoxic carbon source. Here, we use metal-organic framework (MOF) with highly ordered hierarchical structure as adsorbent and catalyst for chemical fixation of CO2 at atmospheric pressure, and the CO2 can be converted to the formate in excellent yields. Meanwhile, we have successfully integrated highly ordered macroporous and mesoporous structures into MOFs, and the macro-, meso-, and microporous structures have all been presented in one framework. Based on the unique hierarchical pores, high surface area (592 m2/g), and high CO2 adsorption capacity (49.51 cm3/g), the ordered macroporous-mesoporous MOFs possess high activity for chemical fixation of CO2 (yield of 77%). These results provide a promising route of chemical CO2 fixation through MOF materials

Non-ketotic Hyperglycemia Chorea-Ballismus and Intracerebral Hemorrhage: A Case Report and Literature Review

Non-ketotic hyperglycemia chorea-ballismus (NKH-CB) is a rare metabolical syndrome secondary to the hyperglycemic condition, which is characterized by a triad of acute or subacute hemichorea-hemiballismus, hyperglycemic state, and unique abnormalities limited to the striatum on neuroimaging. Several related case studies on this disorder have been reported previously, but NKH-CB had never been associated with intracerebral hemorrhage (ICH). Herein, we report an uncommon case of NKH-CB and ICH that occurred simultaneously in one patient, which provides a challenge for clinicians in making a correct diagnosis. An 88-year-old woman with a long-term history of poor-controlled type 2 diabetes mellitus and hypertension, who presented with a sudden-onset headache, restlessness, severe bilateral choreiform and ballistic movements, elevated levels of glucose and osmolality in the serum, an increased white blood cell count, and two-type hyperdense signs on CT imaging, was finally diagnosed with NKH-CB and ICH. Despite administrated active treatments, the patient's clinical status did not improve and ultimately passed away. This case is reported to remind clinicians to consider the possibility of NKH-CB when patients present sudden-onset choreiform and ballistic movements. It is also the first entity with two-type hyperdense signs on CT imaging simultaneously, which helps us distinguish NKH-CB from ICH more intuitively

In-wheel motor vibration control for distributed-driven electric vehicles:A review

Efficient, safe, and comfortable electric vehicles (EVs) are essential for the creation of a sustainable transport system. Distributed-driven EVs, which often use in-wheel motors (IWMs), have many benefits with respect to size (compactness), controllability, and efficiency. However, the vibration of IWMs is a particularly important factor for both passengers and drivers, and it is therefore crucial for a successful commercialization of distributed-driven EVs. This paper provides a comprehensive literature review and state-of-the-art vibration-source-analysis and -mitigation methods in IWMs. First, selection criteria are given for IWMs, and a multidimensional comparison for several motor types is provided. The IWM vibration sources are then divided into internally-, and externally-induced vibration sources and discussed in detail. Next, vibration reduction methods, which include motor-structure optimization, motor controller, and additional control-components, are reviewed. Emerging research trends and an outlook for future improvement aims are summarized at the end of the paper. This paper can provide useful information for researchers, who are interested in the application and vibration mitigation of IWMs or similar topics

Satellite proximate interception vector guidance based on differential games

This paper studies the proximate satellite interception guidance strategies where both the interceptor and target can perform orbital maneuvers with magnitude limited thrusts. This problem is regarded as a pursuit-evasion game since satellites in both sides will try their best to capture or escape. In this game, the distance of these two players is small enough so that the highly nonlinear earth-centered gravitational dynamics can be reduced to the linear Clohessy-Wiltshire (CW) equations. The system is then simplified by introducing the zero effort miss variables. Saddle solution is formulated for the pursuit-evasion game and time-to-go is estimated similarly as that for the exo-atmospheric interception. Then a vector guidance is derived to ensure that the interception can be achieved in the optimal time. The proposed guidance law is validated by numerical simulations. Keywords: Differential games, Saddle solution, Satellite interception, Time-to-go estimation, Zero effort miss trajector

Identifying Latent Causal Content for Multi-Source Domain Adaptation

Multi-source domain adaptation (MSDA) learns to predict the labels in target domain data, under the setting that data from multiple source domains are labelled and data from the target domain are unlabelled. Most methods for this task focus on learning invariant representations across domains. However, their success relies heavily on the assumption that the label distribution remains consistent across domains, which may not hold in general real-world problems. In this paper, we propose a new and more flexible assumption, termed \textit{latent covariate shift}, where a latent content variable $\mathbf{z}_c$ and a latent style variable $\mathbf{z}_s$ are introduced in the generative process, with the marginal distribution of $\mathbf{z}_c$ changing across domains and the conditional distribution of the label given $\mathbf{z}_c$ remaining invariant across domains. We show that although (completely) identifying the proposed latent causal model is challenging, the latent content variable can be identified up to scaling by using its dependence with labels from source domains, together with the identifiability conditions of nonlinear ICA. This motivates us to propose a novel method for MSDA, which learns the invariant label distribution conditional on the latent content variable, instead of learning invariant representations. Empirical evaluation on simulation and real data demonstrates the effectiveness of the proposed method

Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG

Predicting lower limb motion intent is vital for controlling exoskeleton robots and prosthetic limbs. Surface electromyography (sEMG) attracts increasing attention in recent years as it enables ahead-of-time prediction of motion intentions before actual movement. However, the estimation performance of human joint trajectory remains a challenging problem due to the inter- and intra-subject variations. The former is related to physiological differences (such as height and weight) and preferred walking patterns of individuals, while the latter is mainly caused by irregular and gait-irrelevant muscle activity. This paper proposes a model integrating two gait cycle-inspired learning strategies to mitigate the challenge for predicting human knee joint trajectory. The first strategy is to decouple knee joint angles into motion patterns and amplitudes former exhibit low variability while latter show high variability among individuals. By learning through separate network entities, the model manages to capture both the common and personalized gait features. In the second, muscle principal activation masks are extracted from gait cycles in a prolonged walk. These masks are used to filter out components unrelated to walking from raw sEMG and provide auxiliary guidance to capture more gait-related features. Experimental results indicate that our model could predict knee angles with the average root mean square error (RMSE) of 3.03(0.49) degrees and 50ms ahead of time. To our knowledge this is the best performance in relevant literatures that has been reported, with reduced RMSE by at least 9.5%