Anthropometric analysis of facial dimensions using 3D imaging for forensic identification and ethnicity-specific reference models

Previous artificial intelligence methods to system identification modeling for ship motion requires a mass of training data, modeling workload is vast. Aiming at these defects, an identification modeling method based on the reference model structure and Bayesian regularization network is proposed. For a start, an existed and public model is selected as the reference model. Secondly, With BR network, the reference model improves the generalization ability and reduces the training data. Finally, the method is verified with benchmark called KVLCC2. The illustrative example demonstrates the effectiveness and generalization ability of the proposed method.

BACKGROUND & OBJECTIVES: Accurate reconstruction of facial features from skeletal remains, especially the maxillofacial region, is crucial in forensic identification. Specific facial measurements from anatomical landmarks provide important correlations with the vertical dimension of occlusion for forensic experts and dental procedures. There is a gap in such a study in Sargodha, therefore the study was planned to evaluate the correlation & association of facial measurements with the vertical dimension of occlusion (VDO) amongst the students of Rai Medical College, Sargodha. METHODOLOGY: The Observational Cross-sectional study engaged a cohort of 152 third-year MBBS students at Rai Medical College, Sargodha, comprising 74 males and 78 females. Institutional ethical approval was secured prior to the commencement of the research. Facial dimensions were meticulously evaluated, including the expanse from the corner of the eye to the angle of the mouth, the angle of the mouth to the tragus, and the Glabella to sub-nasion. The vertical dimension of occlusion (VDO) traverse from the base of the nose to the chin was precisely measured using a modified digital Vernier caliper. SPSS version 27 was used to analyze statistical data. RESULTS: One-sample t-tests confirmed that mean values for all variables were significantly different from zero. Significant correlations were observed between specific facial measurements but not with gender. CONCLUSION: This study concludes the facial morphology and demographic characteristics, revealing significant differences in facial measurements across demographic groups.

A multi-class support vector machine classification model based on 14 microRNAs for forensic body fluid identification

To understand the perceptions of doctors, patients and forensic examiners on the current situation of medical disputes and medical damage identification in China, and to explore the medical damage identification model that is more conducive for the resolution of medical disputes. A questionnaire was designed, and in-service clinicians, forensic examiners and inpatients in Sichuan Province and Chongqing City were randomly selected from April to November 2019. SPSS 22.0 software was used to analyze the data of various survey results. Compared with patients (24.92%), doctors (61.72%) believed that the current doctor-patient relationship was more tense than before; both doctors and patients were more inclined to choose voluntary consultation and people's mediation to resolve medical disputes; forensic examiners have the highest level of cognition of medical and health-related laws and regulations, followed by doctors and patients; 66.72% of doctors and 78.41% of patients believed that medical damage identification was necessary, and they were more inclined to entrust forensic identification institutions; different groups all believed that forensic examiners and doctors should participate in the identification together, 80.94% of doctors believed that the appraisal institutions should be responsible for the forensic opinion, not the appraiser. It is suggested that the Medical Association identification and forensic identification should learn from each other and formulate basic unified rules for the identification of medical damage. It is suggested to standardize the behavior of medical damage forensic identification institutions and appraisers, to improve their own appraisal level, actively invite clinical medical experts for consultation in identification, and promote the standardized, scientization of forensic identification.

Forensic Speaker Identification is one of the methods used in court to identify the speaker in an incriminating recording from a set of verifiable speakers. It uses the characteristics of the speaker's voice to differentiate the speakers, such as formant frequencies and bandwidths, pitch, segmental timings etc. A methodology is proposed comprising formant frequency feature extraction, Gaussian Mixture Model (GMM) modeling, and various similarity measures, for the task of Macedonian forensic speaker identification. A novel use of the scalar product (SP) of functions as a similarity measure of two GMMs is also proposed. A test-bed database is created comprising a set of recordings from 18 suspects and one incriminating recording to test the proposed methodology. The results have shown reliable forensic speaker identification, and are better than the professional forensic software that was available for comparison.

The proposed paper focuses on the identification of system model with different investigation on real time data obtained from industrial process using neural networks along with design of PID controller using intelligent controllers. The real time process in sugar industry has been taken as reference for modeling and controller design for controlling the process parameters such as level, temperature, flow, pressure, pH etc that associate with the process. The real time process constitutes multi input and output with external disturbance which makes the conventional mathematical modeling is complex when compared to algorithm based design. The process model representation in terms of mathematical model, transfer function, state space model holds the process characteristics and order of the system reflects the input and output to the system. The initial process in sugar industry is juice extraction from sugar cane called crushing unit which requires continuous monitoring and control for maximum juice extraction. The author clearly explains the model identification for cane crushing process with the aid of neural network for data fitness, estimation and validation, model identification. The classical PID controller plays the vital role in process control industries which controls the major closed loop process. The influence of external disturbance during run time in any process or change in set point leads to non-linearity. In order to improve the control action, intelligent controller like fuzzy logic controller and genetic algorithm has been designed for tuning of PID controller. The response of proposed controllers are compared with classical controller and discussed briefly in conclusion.

In this study, a cable-driven snake-like manipulator with high load capacity and end-positioning accuracy is designed for applications in complex and narrow environments. The Hooke joint-like two degree-of-freedom joint design and the full actuation mode enhanced the rigidity of the robot. The modular link design increased the local flexibility of the robot. Because the cable tension cannot be ignored under high load on the basis of the kinematics model, a cable tension model is established based on rigid body static equilibrium to describe the relationship between posture and cable tension. This provided a foundation for follow-up studies on obstacle avoidance path planning with optimized tension. At the same time, in order to improve the response speed of the motor position controller to the tension change, this study introduces both the tension as the reference model input and the system state variable into the adaptive control method based on model identification and reinforcement learning. The kinematics model and the cable tension model were validated by experiments on the prototype. The practical results of the two adaptive control methods were compared and the result shows that the method based on model identification has a better effect.

The controller of a model reference adaptive control monitors the plant’s inputs and outputs to acknowledge its characteristics. It then adapts itself to the characteristics it encounters instead of behaving in a fixed manner. An important part of every adaptive scheme is the adaptive law for estimating the unknown parameters on line. A more precise model is required to improve performance and to stabilize a given dynamic system, such as a satellite in which performance varies over time and the coefficients change due to disturbances, etc. After model identification, the robust controller (H∞) is designed to stabilize the rigid body and flexible body of a satellite, which can be perturbed due to disturbance. The result obtained by the H∞ controller is compared with that of the proportional and integration controller which is commonly used for stabilizing a satellite.

Model Reference Adaptive Control and Identification for Nonlinear Systems: Methods and Applications

Fault Detection and Identification system (FDI) and Fault Tolerant Flight Control (FTFC) system are used to correct the faulty operation of an aircraft. Both FDIs and FTFCs have operational disadvantages due to their inherent limitation of fault source identification. This paper presents the design and implementation of a robust model reference fault detection and identification (MRFDI) system on a fixed-wing aircraft for identifying actuator fault, instrument fault and presence of any uncertainties. The proposed MRDFI fuses the real-time parameters and actuator feedback to combine the advantages of data driven and model reference FDI that makes robust fault estimation. The MRFDI system is implemented on a typical aircraft altitude hold autopilot simulation environment with a predefined fault scenario. The fault scenario includes a faulty elevator, a faulty skin-implantable sensor and wind gust as environmental uncertainty. The MRFDI performs logical analysis to detect fault using state-dependent real-time parameters and state-independent skin implantable sensor. This two-step fault detection method makes MRFDI robust to any type of fault identification. The results show that the MRFDI detects and distinguishes faults in actuator, instrument and any of the listed uncertainties thrown by the environment accurately.

In this paper, the problem of controlling and stabilizing rapidly time-varying nonlinear unknown systems is being investigated. We propose a new scheme that incorporates Multiple Takagi-Sugeno (T-S) Identification Models into an indirect adaptive fuzzy technique. By using this approach, we increase the possibilities to produce a more accurate estimation at every step for the parameters of the unkown plant than in the case that only one identification model is used. One feedback linearization controller corresponds to each identification model, designed according to a pre-specified reference model. The controller to be applied is determined at every instant by the model which best approximates the plant. This is achieved by using a switching rule with a suitable performance index. Lyapunov stability theory is used in order to obtain the adaptive law for the multiple models parameters and to ensure the asymptotic stability of the system also. A modification in this adaptive law keeps the control input away from singularities. The effectiveness and the advantages of the proposed method are demonstrated by controlling an inverted pendulum with rapidly time-varying parameters.

Achieving port automation of machinery at bulk terminals is a challenging problem due to the volatile operation environments and complexity of bulk loading compared to the situations in container terminals. In order to facilitate port automation, we present a method of hull modeling (reconstruction of hull’s structure) and operation target (cargo holds under loading) identification based on 3D point cloud collected by Laser Measurement System mounted on the ship loader. In the hull modeling algorithm, we incrementally register pairs of point clouds and reconstruct the 3D structure of bulk ship’s hull blocks in details through process of encoder data of the loader, FPFH feature matching and ICP algorithm. In the identification algorithm, we project real-time point clouds of the operation zone to spherical coordinate and transforms the 3D point clouds to 2D images for fast and reliable identification of operation target. Our method detects and complements four edges of the operation target through process of the 2D images and estimates both posture and size of operation target in the bulk terminal based on the complemented edges. Experimental trials show that our algorithm allows us to achieve the reconstruction of hull blocks and real-time identification of operation target with high accuracy and reliability.

This paper proposes an active modeling control method, as well as a simplified reference model and a modified frequency identification process, to deal with the model mismatch, which brings errors to nominal controller. The hovering model is identified through the proposed semi-decoupled reference model and the modified frequency identification process. Based on this hovering model and adaptive set-membership filter (ASMF), a scheme of compensation for nominal controller is developed due to the unknown but bounded (UBB) model errors. Flight experiments are done on ServoHeli-40 fly-robot platform to verify the effectiveness of the proposed method in full flight envelope.

This paper addresses some fundamental issues in adaptive control of aircraft with struc- tural damage. It presents a thorough study of linearized aircraft models with damage to obtain new details of system descriptions, such as coupling and partial derivatives of lateral and longitudinal dynamics. A detailed study of system invariance under damage conditions is performed for generic aircraft models to obtain key system characterizations for model reference adaptive control (MRAC), such as infinite zero structure and signs of high frequency gain matrices. A comprehensive study of multivariable MRAC systems in the presence of damage is performed to obtain critical design specifications for adaptive flight control, such as system and controller parametrizations and adaptive parameter up- date laws. Both analytical and simulation results are given to illustrate the design and performance of adaptive control systems for aircraft flight control. Adaptive control of aircraft in the presence of damage has been an important topic in the research of flight control design for aircraft safety. Damage can cause uncertain parametric and structural variations, which requires new aircraft modeling and control approaches. In Reference (1), a study of aircraft dynamics with damage is presented, and a neural network based adaptive control algorithm is introduced for control of aircraft in the presence of structure uncertainties. In (2), equations of motion are introduced in detail for aircraft with asymmetric mass loss. In (3), we introduced a nonlinear aircraft model with partial wing damage, and illustrated linearization of such a model. In (4), real time identification of a damaged aircraft model is studied. A two-step identification process is introduced, which consists of an aircraft state estimation phase and an aerodynamic model identification step. With such a two-step process, the nonlinear part of the model identification is isolated in the first phase, and the aerodynamic parameter identification procedure is simplified to a linear one. A hybrid adaptive control method is proposed in (5) for control of aircraft with damage. The control design is based on a neural network parameter estimation blended with a direct adaptive law. A stability and convergence analysis is presented for this adaptive control methodology. For accommodating unknown changes in the structure and parameters, multivariable MRAC designs offer many advantages. In (6), we introduced an MRAC design based on the LDS decomposition of the high frequency gain matrix for the control of aircraft with multiple wing damage. The key design conditions are that, both the nominal and post-damage systems should have a uniform known modified interactor matrix, and the leading principal minors of their high frequency gain matrices should be nonzero with their signs unchanged. In (7), we studied linearization of nonlinear aircraft models under damage conditions and designed a multivariable MRAC scheme which does not require the knowledge of the signs of the high frequency gain matrix. Potential extension to aircraft flight control systems with changing signs of the high frequency gain matrix remains a topic of future research.

A class of integral, hysteretic control influence operators are derived for the representation of structural systems exhibiting hysteresis due to active materials. The hysteretic control influence operator is defined in terms of a probability distribution, or more generally a measure, that describes the concentration of a particular hysteresis kernel. Two types of hysteresis kernels are studied in detail; the ideal relay kernel leading to a Preisach integral hysteresis operator, and a generalized play kernel leading to a Krasnosel'skii-Pokrovskii operator. When combined with the thermal and structural dynamics equations, the governing equations are history-dependent integropartial differential equations, coupled in a cascade structure. Existence, uniqueness and convergence of Galerkin approximation methods have been derived for the forward simulation and model identification. The cascade structure of the coupled, nonlinear partial differential equations is advantageous in that model reference control, and model reference adaptive control strategies, can be derived for the systems under consideration. Numerical and experimental results that validate the theoretical developments in the paper are presented. (Author)