Adjustment Strategy Research Articles

Research on optimal deep learning‐based formation flight positioning of UAV

SummaryThe article examines the positional relationships and transmission/reception signal direction information within unmanned aerial vehicle (UAV) cluster formations. Considering the distinctive characteristics of various formation shapes (such as circle and cone), a study is conducted on the polar and Cartesian coordinate systems in neural network models to address position deviation issues arising in UAV formation flights. The passive receiving signal UAV positioning model is established by applying the triangular cosine theorem. Additionally, the position relationship model of the UAV is formulated using optimization theory. The article introduces a deep learning‐based deviated UAV adjustment algorithm designed to automatically adjust UAV positions. This is achieved by establishing a triangular relationship between the passive signal receiving UAV and other UAVs. The implementation involves C# programming and MATLAB visualization. The proposed method not only resolves UAV positioning and position adjustment challenges but also optimizes the adjustment strategy. This optimization leads to maximum savings in time and distance costs associated with sending and receiving signals among UAVs, thereby enhancing the overall performance of UAVs during flights.

Tilt adjustment strategy and applicability of four-bucket jacket foundation for offshore wind turbines

With the rapid development of offshore wind turbine industry, the development of sea areas will gradually move from offshore to deep sea. In this process, multi-bucket jacket foundation will become a highly potential infrastructure solution in the water depth of about 50 m due to its unique advantages. The closed space is formed inside the bucket foundation during the penetration process, so the suction sinking method can be used for penetration, the accurate grasp of the critical suction during the penetration process of the four-bucket jacket foundation determines whether the penetration process can be carried out smoothly. At the same time, appropriate tilt adjustment strategies need to be adopted promptly after the inclination occurs during the penetration process. In this paper, the tilt adjustment technique in the penetration process of the four-bucket jacket foundation was taken as the research object. Firstly, the model test was used to study the critical suction of the foundation at three different penetration depths, and the test results are compared with the theoretical calculation results. Subsequently, foundation on the above research results, the applicability of the tilt adjustment strategy when the tilt angles of 1°, 2° and 3° occurred at three penetration depths was analyzed, which provides technical guidance for the development of practical projects.

The relationship between postpartum mothers’ dyadic coping and adjustment and psychological well-being

This study determines the relationship between postpartum mothers’ dyadic coping and adjustment strategies and their psychological well-being. The study design was descriptive and correlational; it was conducted in the pediatric outpatient clinic of a maternity hospital in Northern Türkiye. A total of 327 1–12 month postpartum mothers participated in the study. Data were collected using a personal information form, the Dyadic Coping Inventory (DCI), the Revised Dyadic Adjustment Scale (RDAS), and the Psychological Well-Being Scale. Mothers’ behavior as an individual and as a couple in dyadic coping, their perception of their partner’s behavior, and weak dyadic harmony between partners was significantly associated with risk factors affecting mothers’ psychological well-being (p < 0.05). A weak relationship was found between the dimensions of mothers’ psychological well-being and their perception of their own behavior in dyadic coping; a positive moderate relationship was found between mothers’ perception of their partner’s behavior in dyadic coping and behaviors exhibited as a couple in dyadic coping, and the former had a weak positive relationship with the dimensions of dyadic harmony and its subdimensions (p < 0.05). Self-perceived coping behavior, perception of the partner’s behavior, and joint coping behavior in dyadic coping, and weak dyadic harmony between partners were significant risk factors affecting mothers’ psychological well-being. Awareness of these factors by health professionals and individuals will increase the effectiveness of postpartum care and help couples adapt to the transition in the postpartum period, improve mother-father-infant interaction and strengthen dyadic harmony.

A choice for Europe, a choice for growth? The Bank of Italy, the Pandolfi Plan and the European Monetary System, 1977-1979

The creation of the European Monetary System (EMS) between 1977 and 1979 introduced an element that would become central to Italy's strategy of adjustment to the macroeconomic shocks of the 1970s: the ‘external constraint', a political device — albeit with a technocratic matrix — designed to reduce public debt and enhance the country's economic competitiveness.Conceived by an economist at the Bank of Italy, Tommaso Padoa-Schioppa, in 1978, the reforms of the ambitious programme known as the Pandolfi Plan were designed to restore the competitiveness of the Italian economy. The plan described the main features of the lira's entry into the EMS as an external constraint, based on indications from the Bank of Italy's Director General Carlo Azeglio Ciampi and despite objections from Governor Paolo Baffi.At that juncture, the Bank of Italy consciously assumed the role of substitute that would characterise its action in the following decade, motivating and orienting the country's political choices in favour of an ever more urgent economic and monetary integration of Europe, towards what would finally be formalised with the Maastricht Treaty. Consistently with the structure of the EMS, the external constraint outlined in the Pandolfi Plan moved towards exchange rate constraints related to public finances and to the phenomena of fiscal dominance typical of monetary policy in Italy; it was essentially transformed into a fiscal policy constraint formally recognised in July 1981, with the so-called divorce between the Bank of Italy and the Treasury. The classic external constraint related to the balance of payments accounts and foreign exchange rates remained unchanged towards the rest of the world, meaning that it had an economic and not a "legal" nature, as defined by Guido Carli in the early 1990s.

Anomaly Detection of Tire Tiny Text: Mechanism and Method

Due to the variety of tire specifications and poor contrast, it is difficult for ordinary 2D vision-based methods to accurately and autonomously detect anomalies in the tire text embossed on the tire sidewalls. In this paper, a 3D vision-based autonomous scanning mechanism and method is proposed, including three core components: a three-degree-of-freedom (3-DoF) robotic arm, a high-precision turntable and a composite vision probe. Based on the designed vision probe, the mechanism can autonomously scan the tire sidewall through a series of optimal viewpoints to obtain high-quality tire images even in the absence of tire CAD models. To obtain the optimal viewpoints, an information-driven viewpoint planning method is proposed. Combined with the real-time viewpoint adjustment strategy, the viewpoint of the vision probe is dynamically fine-tuned, which can effectively balance the scanning precision and motion cost. In the text anomaly detection phase, a three-stage tire text anomaly detection method based on deep learning and statistics strategy is proposed, which can accurately detect and recognize tire text, identify sub-mold categories, and judge text anomalies. Experimental results show that the proposed mechanism and method perform well in terms of accuracy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 96%) and efficiency (time per lap for a tire is less than 9 s); it shows that the proposed mechanism and method have certain industrial application value and can be applied to tire quality inspection of on-line production. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Tire text may be incorrectly embossed during tire manufacturing. Due to the wide variety of tire specifications, poor contrast, and thin text strokes, it is difficult to detect abnormal tire text. In this paper, a 3D vision-based mechanism and method is developed to address this issue. Using a specially designed composite vision probe and scanning method, the mechanism can observe tiny text information on tires of different specifications from multiple angles and directions. By using our tire text anomaly detection method based deep learning and statistics strategy, the mechanism can accurately and robustly detect the region and content of abnormal text. The mechanism is efficient (with a detection time of less than 9 s per lap for one tire) and accurate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 96%), indicating that it has great potential in the first inspection or quality inspection step of tire manufacturing in industrial scenarios.

Adaptive adjustment of brightness and blur of the camera for high precision internal parameter calibration

Camera calibration is a prerequisite for many computer vision tasks, such as visual measurement and visual localization. This paper proposes a new method for high-precision calibration of camera intrinsic parameters. By dynamically adjusting image brightness and blur to cope with changes in external lighting conditions and camera working distance, high-precision internal parameter measurement is achieved. This method solves the problems of the traditional camera intrinsic parameter calibration process being cumbersome and susceptible to changes in the placement of calibration boards and fluctuations in external lighting conditions. Firstly, an adaptive exposure adjustment algorithm is proposed to adjust the camera exposure value by statistically analyzing the average gray value of randomly selected rectangular regions on the calibration board, thereby solving the problem of the camera calibration accuracy being affected by overly bright or dark environments. Subsequently, an evaluation criterion and adaptive adjustment strategy for adjusting image blur are proposed. By extracting edge points of feature circles and obtaining the slope of the fitted curve, the relationship between edge sharpness and slope is utilized to adjust the image blur. Finally, the proposed method achieves high-precision semi-automatic calibration. Experimental results demonstrate that the proposed method has significant advantages in terms of correctness, robustness, and flexibility, with an average reprojection error of 0.0167.

Constraint tightening-based control for small-satellite formations using differential aerodynamic forces

Aerodynamic forces hold significant promise for controlling small-satellite formations in low Earth orbit. However, significant challenges still exist regarding complex input constraints and their application to multi-satellite formations. This paper proposes a constraint tightening-based control scheme for small-satellite formations, utilizing differential drag and lift as actuating forces. Two attitude adjustment strategies, namely the reorientation strategy and the single-radial rotation strategy, are compared in terms of their control effectiveness and applicability. Relative aerodynamic coefficients are employed as control inputs in view of their solid reachable region, followed by the development of a decoupling algorithm for determining pointing angles. Through a method that tightens the reachable region using liner models, the input constraints are linearized and can be explicitly expressed. Subsequently, an observer-based Model Predictive Control (MPC) algorithm is formulated to meet these linearized input constraints and provide feedforward compensation for system disturbances. This discrete algorithm enables practical onboard implementations owing to its low computational complexity. Additionally, a concept of overall computation for all satellites’ attitudes is proposed to adapt the control scheme for multi-satellite formations. Hardware-in-the-loop simulations are conducted to evaluate the control scheme's performance in along-track and circular formations, accounting for uncertainties in aerodynamic forces and attitude dynamics.

Design of high-entropy P2/O3 hybrid layered oxide cathode material for high-capacity and high-rate sodium-ion batteries

High-entropy layered cathode materials have garnered significant attention due to their exceptional structural stability and capacity retention. However, the complex composition of these materials has posed challenges for performance optimization. Herein, an innovative high-entropy P2/O3 hybrid layered oxide cathode material with impressive reversible capacity and high-rate capabilities is designed according to the configurational entropy adjustment strategy. Specifically, the unique structure of Na0.85Li0.05Ni0.3Fe0.1Mn0.5Ti0.05O2 (LNFMT) exhibits not only enhanced anionic activity but also mitigated migration of transition metals at high voltage. The high-entropy effect facilitates the migration of Li+ to Na layer, forming a pseudo-tetrahedral structure that hinders the movement of Fe3+. Additionally, the robust Ti-O bond ensures the internal cohesion of transition metal layers, enhancing the structural stability during de-/intercalation processes. As a result, the undesirable P2-O2 phase transformation is effectively suppressed in 1.5–4.5 V, leading to a remarkable capacity retention of over 70% after 100 cycles at 0.2 C. Moreover, LNFMT shows a high discharge capacity of 116 mAh/g at 10 C with excellent capacity retention of 94.23% after 150 cycles in 2.0–4.2 V, demonstrating its superior rate capability. This research showcases the significant impact of the high-entropy effect and provides a novel perspective for the design of sodium-ion cathode materials.

Prediction of digital transformation of manufacturing industry based on interpretable machine learning.

The enhancement of digital transformation is of paramount importance for business development. This study employs machine learning to establish a predictive model for digital transformation, investigates crucial factors that influence digital transformation, and proposes corresponding improvement strategies. Initially, four commonly used machine learning algorithms are compared, revealing that the Extreme tree classification (ETC) algorithm exhibits the most accurate prediction. Subsequently, through correlation analysis and recursive elimination, key features that impact digital transformation are selected resulting in the corresponding feature subset. Shapley Additive Explanation (SHAP) values are then employed to perform an interpretable analysis on the predictive model, elucidating the effects of each key feature on digital transformation and obtaining critical feature values. Lastly, informed by practical considerations, we propose a quantitative adjustment strategy to enhance the degree of digital transformation in enterprises, which provides guidance for digital development.

Western tourists’ adjustment to cultural expectations in Africa

ABSTRACT Despite the theoretical and practical value of understanding how tourists adjust to the cultural expectations of destinations, there is little empirical focus on the subject. Based on cultural adaptation theory and data obtained from interviews with 39 Western tourists, this study explores the strategies used by Western tourists to adjust to cultural expectations in Africa. The findings indicate that Western tourists’ cultural awareness of Africa prior to their trip included awareness of dress code, mannerisms/gestures, and social interactions. Regarding their cultural expectations, it was found that the Western tourists expected to encounter hospitality, culture shock, and outdated culture. The findings further revealed that four cultural adjustment strategies including demonstration of cultural sensitivity, imitation of culture, use of local guides, and avoidance of culturally sensitive sites were used by the Western tourists interviewed. Theoretically, this study extends cultural adaptation theory by highlighting the various shades of cultural adjustment strategies amongst tourists. Practically, African destinations should design marketing messages that highlight positive cultural expectations and some cultural adjustment strategies to enable tourists to have smooth cultural transitions.

Robotic welding system for adaptive process control in gas metal arc welding

Changing process conditions such as distortion, varying seam preparation or gap width during welding is a major challenge in automated gas metal arc welding (GMAW). While human welders can adjust the process during welding (e.g. welding speed, torch orientation), an automated welding system needs sensors to detect and actuators to adjust the process. Adjusting the process in response to changing process conditions is usually referred to as adaptive welding. The aim of this work is to build a robotic welding system capable of automatically adapting the welding process using some of the approaches of a human welder. To enable adaptive process control, a robotic welding system is built. It consists of four main components: a six-axis industrial robot for mechanical guidance of the welding torch, a welding power source, a monochrome visual camera as an image sensor and a process controller that combines the three components. The camera captures images of the weld pool during welding and processes the images to provide geometrical information such as the width of the weld pool and the position of the weld pool front. Changes in the weld pool geometry are quantified, and an adjustment strategy is generated in the process control unit in real time. Process adjustments can be mechanical (e.g. welding speed, torch orientation) and electrical by adjusting synergic process settings (wire feed speed, arc length, process dynamics). Validation tests demonstrate the functionality of the welding system. Two use cases were investigated. Firstly, a deposited weld bead was examined, and variations in the width of the weld pool were induced by varying the welding speed. The second application was a seam tracking application. The path is pre-programmed, and the specimen is positioned with an offset to the path. Compensation for the offset is implemented.

Two-stage consensus reaching process in social network large group decision-making with application to battery supplier selection

Trust relationships play an important role in social network large group decision making (SN-LGDM). Extant studies overlook the self-adjustment stage of decision makers (DMs) and the binding force of subgroup on preference adjustment in consensus reaching process (CRP). This article proposes a new two-stage CRP for SN-LGDM considering self-adjustment and binding force of subgroup. A new trust propagation approach is presented based on individual trust level. An improved grey clustering algorithm is devised based on the fusion of preference similarity and trust relationship. The preference stability and the awareness of big picture are defined to determine the weights of DMs. The subgroup size, cohesion and influence are integrated to determine the weights of subgroups comprehensively. For the new two-stage CRP, the first stage is self-adjustment stage. DMs may proactively choose to adjust the preference or update the trust relationship according to the degree of matching between the trust relationship and preference difference. In the second stage, four scenarios are identified and the corresponding adjustment strategies are given considering the intention of DM to modify and the binding force of subgroup, which makes the consensus more consistent with reality. A battery supplier selection case is offered to illustrate the proposed method.

Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation

Adversarial Robustness Distillation (ARD) is a promising task to solve the issue of limited adversarial robustness of small capacity models while optimizing the expensive computational costs of Adversarial Training (AT). Despite the good robust performance, the existing ARD methods are still impractical to deploy in natural high-security scenes due to these methods rely entirely on original or publicly available data with a similar distribution. In fact, these data are almost always private, specific, and distinctive for scenes that require high robustness. To tackle these issues, we propose a challenging but significant task called Data-Free Adversarial Robustness Distillation (DFARD), which aims to train small, easily deployable, robust models without relying on data. We demonstrate that the challenge lies in the lower upper bound of knowledge transfer information, making it crucial to mining and transferring knowledge more efficiently. Inspired by human education, we design a plug-and-play Interactive Temperature Adjustment (ITA) strategy to improve the efficiency of knowledge transfer and propose an Adaptive Generator Balance (AGB) module to retain more data information. Our method uses adaptive hyperparameters to avoid a large number of parameter tuning, which significantly outperforms the combination of existing techniques. Meanwhile, our method achieves stable and reliable performance on multiple benchmarks.

A model-free toolface control strategy for cross-well intelligent directional drilling

Toolface adjustment methods for directional drilling in oil and gas drilling currently rely heavily on manual real-time interventions for continuous adjustment. However, due to the influence of manual experience, these existing methods suffer from unstable effects and high labor costs. As energy consumption continues to rise, the demand for intelligent directional drilling is becoming increasingly pressing. To address the challenges posed by automatic adjustment issues encountered in actual drilling operations under various complex downhole environments, this study presents a model-free online learning adaptive decision strategy for cross-well intelligent adjustment and toolface stabilization, which is applicable for both slide and rotatory steerable systems. A reward function embedded with expert operating experience is developed to learn the orientation strategy from the driller's corrective actions. Additionally, a priority-based experience replay mechanism is introduced to enhance online learning efficiency. To accurately simulate the directional drilling process and pre-train the orientation strategy, a data-driven directional drilling simulation environment is proposed. With the aim of facilitating implementation and widespread adoption in practical engineering, this study also involves the migration of a strategic model, integration of a real-time interaction module, and encapsulation of algorithms for field applications. Simulations and field experiments are conducted to validate the effectiveness of the proposed strategy. The experimental results demonstrate that the strategy can achieve decision-making goals in a short period of time.

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

To optimize the evacuation process of rail transit passenger flows, the influence of the feeder bus network on bus demand is pivotal. This study first examines the transportation mode preferences of rail transit station passengers and addresses the feeder bus network’s optimization challenge within a three-dimensional framework, incorporating an elastic mechanism. Consequently, a strategic planning model is developed. Subsequently, a multi-objective optimization model is constructed to simultaneously increase passenger numbers and decrease both travel time costs and bus operational expenses. Due to the NP-hard nature of this optimization problem, we introduce an enhanced non-dominated sorting genetic algorithm, INSGA-II. This algorithm integrates innovative encoding and decoding rules, adaptive parameter adjustment strategies, and a combination of crowding distance and distribution entropy mechanisms alongside an external elite archive strategy to enhance population convergence and local search capabilities. The efficacy of the proposed model and algorithm is corroborated through simulations employing standard test functions and instances. The results demonstrate that the INSGA-II algorithm closely approximates the true Pareto front, attaining Pareto optimal solutions that are uniformly distributed. Additionally, an increase in the fleet size correlates with greater passenger volumes and higher operational costs, yet it substantially lowers the average travel cost per customer. An optimal fleet size of 11 vehicles is identified. Moreover, expanding feeder bus routes enhances passenger counts by 18.03%, raises operational costs by 32.33%, and cuts passenger travel time expenses by 21.23%. These findings necessitate revisions to the bus timetable. Therefore, for a bus network with elastic demand, it is essential to holistically optimize the actual passenger flow demand, fleet size, bus schedules, and departure frequencies.

Cracks-suppression perceptual geometry coding for dynamic point clouds

Dynamic point clouds can effectively describe 3D objects and natural scenes, providing users with an immersive visual experience, but their huge amount of data requires efficient compression tools. To this end, the video-based point cloud compression (V-PCC) standard was developed, however, it may lead to cracks in the compressed point cloud at low bitrates. To solve the problem, this paper proposes a cracks-suppression perceptual geometry coding method for dynamic point cloud. Firstly, considering that the edge regions of 2D geometry patches in V-PCC are prone to cracks, a prior knowledge based crack region detection and preprocessing scheme is designed to reduce cracks. Secondly, considering the unique perceptual geometry characteristics of point cloud, a projected geometry curvature based structural similarity (PGC-SSIM) is proposed to evaluate geometry quality of point clouds, which contains the geometric perception information of point cloud, and is more consistent with the human visual perception. Finally, based on the PGC-SSIM, an adaptive quantization parameter adjustment strategy is designed for rate-distortion optimization in geometry coding of dynamic point clouds. The experimental results show that the proposed method can effectively reduce cracks in the compressed point cloud without reducing the coding performance compared to the V-PCC anchor. Moreover, the presented PGC-SSIM can be used to improve the visual quality of the compressed point cloud.

Dynamic analysis on an asymmetric spatial dumbbell-type model

The structural symmetric breaking of the spatial structure will enhance the coupling dynamic behaviors and bring new challenges for the dynamic analysis on the spatial structure inevitably. The main contribution of this paper is proposing a structure-preserving iteration method to investigate the effects of the dynamic symmetry-breaking factors on the dynamic behaviors of the rigid-flexible coupling systems. Firstly, the spatial flexible damping beam assembled with two particles on both ends is simplified as an asymmetric spatial dumbbell-type model. For this model, the coupling dynamic equations are presented based on the Hamiltonian variational principle. Then, connecting the symplectic precise integration method for the ordinary differential equations mainly controlling the plane motion of the model and the generalized multi-symplectic scheme for the partial differential equation mainly controlling the transverse vibration of the beam, a structure-preserving numerical iteration method is proposed, which provides a new way to analysis the dynamic behaviors of the coupling problems with the symmetric breaking. Finally, the effects of the absolute mass and the mass ratio of the additional particles on the orbit radius, the orbit true anomaly velocity, the attitude angle velocity of the model and the transverse vibration of the flexible beam are reproduced by using the structure-preserving numerical iteration method in the numerical simulations. Particularly, the effects of the additional particles on the attitude stability of the model and the vibration dissipation of the flexible damping beam are investigated, which gives some guidance for the attitude adjustment strategy and the vibration control method for the spatial flexible structure with the structural symmetric breaking.

A Real-time Path Planning Algorithm for Mobile Robots Based on Safety Distance Matrix and Adaptive Weight Adjustment Strategy

A Real-time Path Planning Algorithm for Mobile Robots Based on Safety Distance Matrix and Adaptive Weight Adjustment Strategy

Optimal subsidy policy for new energy vehicles based on the influence of multiple factors and consumers' green preference

AbstractPolicies concerning subsidies for new energy vehicles and consumers' environmental preferences significantly impact the sales of such vehicles. To investigate the correlation between sales and subsidies or consumers' environmental preferences and to devise optimal subsidy strategies, two Stackelberg game models are constructed based on both monopoly and duopoly markets, with solutions derived for Nash equilibrium points. In order to refine the strategy for subsidy adjustments, two dynamic adjustment models considering bounded rationality are established. Additionally, the process of adjusting subsidies towards Nash equilibrium points is analyzed. Simulations, utilizing actual research data, are performed to assess China's current adjustment strategies. The findings reveal a substitution relationship between consumers' environmental preferences and subsidies. Furthermore, optimal subsidy levels and bounded rational subsidy adjustment strategies are identified. Governments should thoroughly consider consumers' environmental preferences when refining subsidy policies, as it is a crucial factor influencing the development of the new energy vehicle industry.

GaitSCM: Causal representation learning for gait recognition

Gait recognition is a promising biometric technology that aims to identify the target subject via walking pattern. Most existing appearance-based methods focus on learning discriminative spatio-temporal representations from gait silhouettes. However, these methods pay less attention to probing the causality between identity factors and identity labels, which often mislead the model to learn gait representations that are susceptible to identity-irrelevant factors. In this paper, we attribute the cause that leads to the decline of model generalization under different external conditions to identity-irrelevant factors. We formulate the causalities among the identity factors, identity-irrelevant factors, and identity labels as a structural causal model (SCM). We accordingly propose a novel gait recognition framework named GaitSCM to learn covariate invariant gait representations, which is mainly composed of three components, including feature extraction module, feature disentanglement module, and backdoor adjustment. Specifically, we design a feature extractor with regard to the movement patterns of different body parts to learn fine-grained gait motion features, and then present a two-branch feature decoupling module to disentangle identity features and identity-irrelevant features with the aid of the classification confusion loss. To relieve the negative effect of identity-irrelevant factors, we develop a backdoor adjustment strategy to eliminate spurious associations between identity and identity-irrelevant features, which further facilitates the proposed framework to generate more powerful identity representations. Extensive experiments conducted on two public datasets validate the effectiveness of our method. The average Rank-1 can reach 93.2% and 90.4% on CASIA-B and OU-MVLP datasets, respectively, which verifies the superiority of GaitSCM. Source code is released at: https://github.com/HuoweiCode/GaitSCM.