Unmanned wave glider heading model identification and control by artificial fish swarm algorithm

We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of“thruster+float body”,based on which a planar motion model of the UWG was established.Second,we obtained the model parameters using an empirical method combined with parameter identification,which means that some parameters were estimated by the empirical method.In view of the specificity and importance of the heading control,heading model parameters were identified through the artificial fish swarm algorithm based on tank test data,so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system.Based on the established heading motion model,parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm.Heading motion comparison and maritime control experiments of the“Ocean Rambler”UWG were completed.Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity.The UWG shows good control performance in tank tests and sea trials.The efficiency of the proposed method is verified.

Accuracy Analysis and Design of A3 Parallel Spindle Head

As functional components of machine tools, parallel mechanisms are widely used in high efficiency machining of aviation components, and accuracy is one of the critical technical indexes. Lots of researchers have focused on the accuracy problem of parallel mechanisms, but in terms of controlling the errors and improving the accuracy in the stage of design and manufacturing, further efforts are required. Aiming at the accuracy design of a 3-DOF parallel spindle head（A3 head）, its error model, sensitivity analysis and tolerance allocation are investigated. Based on the inverse kinematic analysis, the error model of A3 head is established by using the first-order perturbation theory and vector chain method. According to the mapping property of motion and constraint Jacobian matrix, the compensatable and uncompensatable error sources which affect the accuracy in the end-effector are separated. Furthermore, sensitivity analysis is performed on the uncompensatable error sources. The sensitivity probabilistic model is established and the global sensitivity index is proposed to analyze the influence of the uncompensatable error sources on the accuracy in the end-effector of the mechanism. The results show that orientation error sources have bigger effect on the accuracy in the end-effector. Based upon the sensitivity analysis results, the tolerance design is converted into the issue of nonlinearly constrained optimization with the manufacturing cost minimum being the optimization objective. By utilizing the genetic algorithm, the allocation of the tolerances on each component is finally determined. According to the tolerance allocation results, the tolerance ranges of ten kinds of geometric error sources are obtained. These research achievements can provide fundamental guidelines for component manufacturing and assembly of this kind of parallel mechanisms.

BRAIN INJURY BIOMECHANICS IN REAL WORLD VEHICLE ACCIDENT USING MATHEMATICAL MODELS

This paper aims at investigating brain injury mechanisms and predicting head injuries in real world accidents. For this purpose, a 3D human head finite element model （HBM-head） was developed based on head-brain anatomy. The HBM head model was validated with two experimental tests. Then the head finite element（FE） model and a multi-body system （MBS） model were used to carry out reconstructions of real world vehicle-pedestrian accidents and brain injuries. The MBS models were used for calculating the head impact conditions in vehicle impacts. The HBM-head model was used for calculating the injury related physical parameters, such as intracranial pressure, stress, and strain. The calculated intracranial pressure and strain distribution were correlated with the injury outcomes observed from accidents. It is shown that this model can predict the intracranial biomechanical response and calculate the injury related physical parameters. The head FE model has good biofidelity and will be a valuable tool for the study of injury mechanisms and the tolerance level of the brain.

Stability of rat models of fluid percussion-induced traumatic brain injury: comparison of three different impact forces

Fluid percussion-induced traumatic brain injury models have been widely used in experimental research for years. In an experiment, the stability of impaction is inevitably affected by factors such as the appearance of liquid spikes. Management of impact pressure is a crucial factor that determines the stability of these models, and direction of impact control is another basic element. To improve experimental stability, we calculated a pressure curve by generating repeated impacts using a fluid percussion device at different pendulum angles. A stereotactic frame was used to control the direction of impact. We produced stable and reproducible models, including mild, moderate, and severe traumatic brain injury, using the MODEL01-B device at pendulum angles of 6°, 11° and 13°, with corresponding impact force values of 1.0 ± 0.11 atm（101.32 ± 11.16 k Pa）, 2.6 ± 0.16 atm（263.44 ± 16.21 k Pa）, and 3.6 ± 0.16 atm（364.77 ± 16.21 k Pa）, respectively. Behavioral tests, hematoxylin-eosin staining, and magnetic resonance imaging revealed that models for different degrees of injury were consistent with the clinical properties of mild, moderate, and severe craniocerebral injuries. Using this method, we established fluid percussion models for different degrees of injury and stabilized pathological features based on precise power and direction control.

Effect of head model on Monte Carlo modeling of spatial sensitivity distribution for functional near-infrared spectroscopy

Modeling Light propagation within human head to deduce spatial sensitivity distribution(SSD)is important for Near-infrared spectroscopy(NIRS)/imaging(NIRI)and diffuse correlation tomography.Lots of head models have been used on this issue,including layered head model,artificial simplified head model,MRI slices described head model,and visible human head model.Hereinto,visible Chinese human(VCH)head model is considered to be a most faithful presentation of anatomical structure,and has been highlighted to be employed in modeling light propagation.However,it is not practical for all researchers to use VCH head models and actually increasing number of people are using magnet resonance imaging(MRI)head models.Here,all the above head models were simulated and compared,and we focused on the effect of using di®erent head models on predictions of SSD.Our results were in line with the previous reports on the effect of cerebral cortex folding geometry.Moreover,the in fluence on SSD increases with thefidelity of head models.And surprisingly,the SSD percentages in scalp and gray matter(region of interest)in MRI head model were found to be 80%and 125%higher than in VCH head model.MRI head models induced nonignorable discrepancy in SSD estimation when compared with VCH head model.This study,as we believe,is the first to focus on comparison among full serials of head model on estimating SSD,and provided quantitative evidence for MRI head model users to calibrate their SSD estimation.

Multi-layered tissue head phantoms for noninvasive optical diagnostics

Extensive research in the area of optical sensing for medical diagnostics requires development of tissue phantoms with opt ical properties similar to those of living human tissues.Development and improvement of in vivo optical measurement systems requires the 1use of stable tissue phantoms with known characteristics,which are mainly used for calibration of such systems and testing their performance over time.Optical and mechanical properties of phantoms depend on their purpose.Nevertheless,they must accurately simulate specific tssues they are supposed to mimic.Many tsues and organs including head possess a multi-layered structure,with specifie optical properties of each layer.However,such a structure is not always addressed in the present-day phantoms.In this paper,we focus on the development of a plain-parallel multi-layered phantom with optical properties(reduced scattering oofficientμ'and absorption cofficientμa)corresponding to the human head layers,such as skin,skul,and gray and white matter of the brain tissue.The phantom is intended for use in noninvasive diffuse near-infrared spectroscopy(NIRS)of humnan brain.Optical parameters of the fabricated phantoms are reconstructed using spectrophotometry and inverse adding-doubling calculation method.The results show that polyinyl chloride plastisol(PVCP)and zinc oxide(ZnO)nanoparticles are suitable materials for fabrication of tissue mimicking phantoms with controlled scattering properties.Good matching was found between optical properties of phantoms and the corresponding values found in the literature.

Comparison between Equivalent Charge Model and Equivalent Dipole Model on Realistic Head Model

Equivalent source layer （ESL） imaging is an important kind of high-resolution electro- encephalogram （EEG） imaging. It consists of two categories： equivalent dipole layer （EDL） and equivalent charge layer （ECL）. Both of them are assumed to be located on or near the cortical surface and have been proposed as high-resolution imaging modalities or as intermediate steps to estimate the epicortical potential. Here, EDL and ECL based on a realistic head model are presented, both simulations and real data experiment are done to compare these two models. The results show that ECL can provide higher spatial resolution about source location than EDL does.

Interictal Electrophysiological Source Imaging Based on Realistic Epilepsy Head Model in Presurgical Evaluation:A Prospective Study

Invasive techniques are becoming increasingly important in the presurgical evaluation of epilepsy.Adopting the electrophysiological source imaging(ESI)of interictal scalp electroencephalography(EEG)to localize the epileptogenic zone remains a challenge.The accuracy of the preoperative localization of the epileptogenic zone is key to curing epilepsy.The T1 MRI and the boundary element method were used to build the realistic head model.To solve the inverse problem,the distributed inverse solution and equivalent current dipole(ECD)methods were employed to locate the epileptogenic zone.Furthermore,a combination of inverse solution algorithms and Granger causality connectivity measures was evaluated.The ECD method exhibited excellent focalization in lateralization and localization,achieving a coincidence rate of 99.02%(p<0.05)with the stereo electroencephalogram.The combination of ECD and the directed transfer function led to excellent matching between the information flow obtained from intracranial and scalp EEG recordings.The ECD inverse solution method showed the highest performance and could extract the discharge information at the cortex level from noninvasive low-density EEG data.Thus,the accurate preoperative localization of the epileptogenic zone could reduce the number of intracranial electrode implantations required.