Hydrocarbon adsorption mechanism of modern automobile engines and methods of reducing hydrocarbon emissions during cold start process: A review.

With the global emphasis on environmental protection and increasingly stringent emission regulations for internal combustion engines, there is an urgent need to overcome the problem of large hydrocarbon (HC) emissions caused by unstable engine cold starts. Synergistic engine pre-treatment (reducing hydrocarbon production) as well as after-treatment devices (adsorbing and oxidizing hydrocarbons) is the fundamental solution to emissions. In this paper, the improvement of hydrocarbon emissions is summarized from two aspects: pre-treatment and after-treatment. The pre-treatment for engine cold start mainly focuses on summarizing the intake control, fuel, and engine timing parameters. The after-treatment mainly focuses on summarizing different types of adsorbents and modifications (mainly including different molecular sieve structures and sizes, preparation conditions, silicon aluminum ratio, ion exchange modification, and heterogeneity, etc.), adsorptive catalysts (mainly including optimization of catalytic performance and structure), and catalytic devices (mainly including coupling with thermal management equipment and HC trap devices). In this paper, a SWOT (strength, weakness, opportunity, and threat) analysis of pre-treatment and after-treatment measures is conducted. Researchers can obtain relevant research results and seek new research directions and approaches for controlling cold start HC emissions.

An Automated Skill Assessment Framework Based on Visual Motion Signals and a Deep Neural Network in Robot-Assisted Minimally Invasive Surgery.

Surgical skill assessment can quantify the quality of the surgical operation via the motion state of the surgical instrument tip (SIT), which is considered one of the effective primary means by which to improve the accuracy of surgical operation. Traditional methods have displayed promising results in skill assessment. However, this success is predicated on the SIT sensors, making these approaches impractical when employing the minimally invasive surgical robot with such a tiny end size. To address the assessment issue regarding the operation quality of robot-assisted minimally invasive surgery (RAMIS), this paper proposes a new automatic framework for assessing surgical skills based on visual motion tracking and deep learning. The new method innovatively combines vision and kinematics. The kernel correlation filter (KCF) is introduced in order to obtain the key motion signals of the SIT and classify them by using the residual neural network (ResNet), realizing automated skill assessment in RAMIS. To verify its effectiveness and accuracy, the proposed method is applied to the public minimally invasive surgical robot dataset, the JIGSAWS. The results show that the method based on visual motion tracking technology and a deep neural network model can effectively and accurately assess the skill of robot-assisted surgery in near real-time. In a fairly short computational processing time of 3 to 5 s, the average accuracy of the assessment method is 92.04% and 84.80% in distinguishing two and three skill levels. This study makes an important contribution to the safe and high-quality development of RAMIS.

Research on force and position control performance of the tendon sheath system with time-varying parameters and flexible robotic arms.

BACKGROUND: The tendon-sheath-system (TSS) is an excellent medium for remote power transmission, which is widely used in laparoscopic surgery robots. Since the operation process requires the robot to move continuously, this time-varying characteristic further aggravates the force and position transmission loss caused by the nonlinear friction of TSS, which affects the control accuracy of the surgical robot. METHOD: A time-varying tendon-sheath transmission model (RT model) is proposed. A feedforward control system is designed to improve tendon-sheath transmission accuracy. Furthermore, a tendon-sheath transmission model with velocity characteristics (RV model) is established. RESULT: Force, position, and velocity experiments were carried out on the platform of TSS with a robotic arm. The results show that the R-square values of force and position compensation are at least 96.57% and 99.16%. CONCLUSION: The proposed RT and RV models are effective in compensating for the TSS transmission loss during the operation of the surgical robot.

Time-varying modeling and intelligent compensation control of singletendon-sheath structure of surgical robot.

The inaccurate force and position control of tendon sheath system (TSS) due to nonlinear friction during surgery seriously hinders its development in the field of precision surgical robots. To this end, this paper proposes a time-varying bending angle estimation method under the state of sensorless offline identification combined with robot kinematics by analyzing the friction of the TSS and the deformation of the robot during the movement, and establishes a force and position transfer model with time-varying path trajectory (SJM model). The model uses B-spline curve to fit tendon-sheath trajectory. In order to further improve the control accuracy of force and position, a new intelligent feedforward control strategy that integrates the SJM model and a neural network algorithm is proposed. In order to gain an in-depth understanding of the transmission process of force and position and to demonstrate the validity of the SJM model, an experimental platform for the TSS was built. A feedforward control system under the MATLAB environment was built with the aim of verifying the accuracy of the intelligent feedforward control strategy. The system innovatively combines the SJM model with BP and RBF neural networks, respectively. The experimental results showed that the correlation coefficients (R2) of force and position transfer are above 99.10% and 99.48%, respectively. Ultimately, we compared the intelligent feedforward and intelligent control strategy under a single neural network, and observed that the intelligent feedforward control strategy has a better effect.

Semi-Supervised Medical Image Segmentation Guided by Bi-Directional Constrained Dual-Task Consistency.

BACKGROUND: Medical image processing tasks represented by multi-object segmentation are of great significance for surgical planning, robot-assisted surgery, and surgical safety. However, the exceptionally low contrast among tissues and limited available annotated data makes developing an automatic segmentation algorithm for pelvic CT challenging. METHODS: A bi-direction constrained dual-task consistency model named PICT is proposed to improve segmentation quality by leveraging free unlabeled data. First, to learn more unmarked data features, it encourages the model prediction of the interpolated image to be consistent with the interpolation of the model prediction at the pixel, model, and data levels. Moreover, to constrain the error prediction of interpolation interference, PICT designs an auxiliary pseudo-supervision task that focuses on the underlying information of non-interpolation data. Finally, an effective loss algorithm for both consistency tasks is designed to ensure the complementary manner and produce more reliable predictions. RESULTS: Quantitative experiments show that the proposed PICT achieves 87.18%, 96.42%, and 79.41% mean DSC score on ACDC, CTPelvic1k, and the individual Multi-tissue Pelvis dataset with gains of around 0.8%, 0.5%, and 1% compared to the state-of-the-art semi-supervised method. Compared to the baseline supervised method, the PICT brings over 3-9% improvements. CONCLUSIONS: The developed PICT model can effectively leverage unlabeled data to improve segmentation quality of low contrast medical images. The segmentation result could improve the precision of surgical path planning and provide input for robot-assisted surgery.

Autonomous path planning for robot-assisted pelvic fracture closed reduction with collision avoidance.

BACKGROUND: Robot-assisted pelvic fracture closed reduction (RPFCR) positively contributes to patient treatment. However, the current path planning suffers from incomplete obstacle avoidance and long paths. METHOD: A collision detection method is proposed for applications in the pelvic environment to improve the safety of RPFCR surgery. Meanwhile, a defined orientation planning strategy (OPS) and linear sampling search (LSS) are coupled into the A* algorithm to optimise the reduction path. Subsequently, pelvic in vitro experimental platform is built to verify the augmented A*algorithm's feasibility. RESULTS: The augmented A* algorithm planned the shortest path for the same fracture model, and the paths planned by the A* algorithm and experience-based increased by 56.12% and 89.02%, respectively. CONCLUSIONS: The augmented A* algorithm effectively improves surgical safety and shortens the path length, which can be adopted as an effective model for developing RPFCR path planning.

Impact of carbon chain length of alcohols on the physicochemical properties and reactivity of exhaust soot.

Particle is the main pollutant in diesel engine exhaust, which seriously endangers human health and the atmospheric environment. The use of alcohol fuels in diesel engines can effectively reduce particle emissions, but alcohol fuels with different carbon chain lengths will affect the generation process of particles, which in turn changes the physicochemical properties and oxidation characteristics of the particles. Therefore, it is particularly important to study the properties of particle emitted by diesel engines fueling alcohol fuels with different carbon chain lengths. The physicochemical properties of soot emitted from commercial diesel engines were studied by thermogravimetric analyzer, HRTEM (high-resolution transmission electron microscopy), and XPS (X-ray photoelectron spectroscopy) in this paper, respectively. The diesel engine used alcohol-diesel blends of different carbon chain lengths with the same oxygen content as fuels, such as methanol/diesel blend (M10), n-butanol/diesel blend (NB25), and n-octanol/diesel blend (NO45), and pure diesel fuel was used as a reference. The results showed that the use of alcohols reduced the fractal dimension (Df) of particles, and the NB25 particles had the smallest Df. Moreover, the particles of blended fuels had smaller primary particle diameter (dp) compared to pure diesel. However, with the use of short-chain to long-chain alcohols, an increasing tendency of dp was observed. In terms of the nanostructure, as the use of short-chain to long-chain alcohols, the La (fringe length) increased, both the d (fringe separation distance) and Tf (fringe tortuosity) reduced, which was not favorable for the oxidation of the particles. In addition, in terms of oxygenated surface functional groups (SFGs), the CO group occupied a higher proportion in most working conditions relative to the groups of CO and COO. Further analysis showed that the dp and nanostructure had more influence on the oxidation behavior of soot than Df and oxygenated SFGs.

Analysis of morphology, nanostructure, and oxidation reaction of soot particulates from CI engines with dimethoxymethane-diesel blends under different loads and speeds.

Dimethoxymethane (DMM)-diesel blended fuels can simultaneously reduce exhaust emissions of soot and nitrogen oxide (NOX); several studies have been conducted in this regard. However, the influence of additive DMM on the production of inception and precursors of particulates, especially the relation between oxidation, morphology, and the nanostructure of soot particles has not been extensively investigated. In this study, a transmission electron microscope (TEM) and a thermogravimetric analyzer are introduced to acquire TEM images and conduct temperature-programmed-oxidation experiments. Aiming to study the influence of DMM addition on soot oxidation, morphology, and nanostructure, tests are conducted at different rotational speeds (1400 rpm and 2200 rpm), two engine loads (0.6 MPa and 1.2 MPa), and three fuels (D100, DMM6.4, and DMM13). The results show that the diameter distributions of all samples display a similar distribution, with the range of sample diameters being from 10 to 45 nm, and the addition of DMM reduces the dp (primary particle diameters) and the Df (fractal dimension), indicating a decreased structural compactness of aggregates, compared with diesel. Moreover, with increasing load and speed, La (the length of the fringe) increases and d (the distance between adjacent layer planes) decreases. Furthermore, with the addition of DMM, a more regular and higher degree of graphitization within soot particles can be observed in comparison to D100. The nanostructure influences the oxidation reaction of graphene segments with a line relation, leading to a difference in soot oxidation property.