Background: This study was conducted to perform deep learning-based body shape cluster analysis using 3D Body Scanner. Methods: For this study, 54 variables were measured using 3D Body Scanner on 366 adult men and women at Korea National Sport University in 2022. Transformer learning and dimensionality reduction models were used to perform cluster analysis on the measured data. Mann-Whitney test and Kruskal-Wallis test were applied to compare the principal component differences of new scale characteristics, and all statistical significance levels were set at .05. Results: First, among the two methods for classifying body types, the transformer algorithm had a higher performance in body type classification. Second, in the classification of body type clusters, two clusters, endomorphic body type and ectomorphic body type, were divided into six clusters, two for cluster 1 and four for cluster 2. Conclusion: The six clusters provide more granular information than previous body type classifications, and we believe that they can be used as basic information for predicting health and disease.

The body freedom flutter phenomenon is one of the aeroelastic instabilities that occurs due to the coupling of the aeroelastic bending mode of the wing with the short-period mode in the flight dynamics of the aircraft. By using the aeroservoelastic model and applying closed loop control, this phenomenon can be suppressed in the operating conditions of the aircraft and the velocity of this event can be increased. The simplest model aircraft capable of displaying this instability includes the flexible wing and the planar flight dynamics model. For this purpose, the wing structure is modeled using the Euler-Bernoulli beam and, the theory of minimum variable state is used to model unstable aerodynamics to make the conditions suitable for modeling the system in state space. In the control section, the elevator is used as the control surface and LQR theory with Kalman filter is used to body freedom flutter suppression. Finally, the effect of adding a closed loop control to increase the body freedom flutter velocity and the limitations of this work are studied.

Distributed Denial of Service (DDoS) attacks are among the primary concerns in internet security today. Machine learning can be exploited to detect such attacks. In this paper, a multi-layer perceptron model is proposed and implemented using deep machine learning to distinguish between malicious and normal traffic based on their behavioral patterns. The proposed model is trained and tested using the CICDDoS2019 dataset. To remove irrelevant and redundant data from the dataset and increase learning accuracy, feature selection is used to select and extract the most effective features that allow us to detect these attacks. Moreover, we use the grid search algorithm to acquire optimum values of the model’s hyperparameters among the parameters’ space. In addition, the sensitivity of accuracy of the model to variations of an input parameter is analyzed. Finally, the effectiveness of the presented model is validated in comparison with some state-of-the-art works.

Introduction. The study of gross anatomy has always been confronted with limitations including deterioration, bad odor of cadaver and fixative solutions. one of the modern preservation techniques is plastination. In this project a whole body was made by plastination technique.Methods. This study was carried out on a fresh human body after fixation of the cadaver. It was dissected carefully after 6 months. It was then dehydrated and defated in cold acetone (-25˚6) for 2 weeks and warm (35-40˚c) acetone for 4 weeks. finally it was impregnated by polymer under pressure of vaccum. Results. The plastinated specimens found to be immune from infection, deterioration and flexible without bad odor and showing natural color.Discussion. Plastination is a modern techinque that has many advantages to formaldehyde fixation especially for our country with limited source of body donation. Plastinated specimens can be unique material for teaching and can play a key role in solving our problem of cadaver shortage.  

The ray series method may be generalized using a ray centered coordinate system for general 3D-heterogeneous media. This method is useful for Amplitude Versus Offset (AVO) seismic modeling, seismic analysis, interpretational purposes, and comparison with seismic field observations. For each central ray (constant ray parameter), the kinematic (the eikonal) and dynamic ray tracing system of equations are numerically solved. Then, the ray impulse and the ray synthetic seismograms are efficiently computed. The reflected, refracted, critically diffracted multiples and converted P-waves and/or S-waves are computed and evaluated at the ray endpoints. The central Ray Method application to two-dimensional models is investigated and comparison with seismic wave field is successfully done. Two examples of the ray field and synthetic seismograms for the complex models are presented here both for surface seismic profiling (SSP) and vertical seismic profiling (VSP).

An unknown 55 years old man with loss of consciousness, bradypnea, cyanosis and miotic pupil was referred to hospital from a bus terminal. Neurological examination revealed no lateralized sign and acrocyanosis was positive. A short time after administration of hypertonic glucose and naloxone, he had seizure, hypotension and apnea, therefore, he underwent intubation and mechanical ventilation. Primary laboratory examinations revealed respiratory acidosis and hypoxemia. After brain CT scanning and other lab tests, he received naloxone again and showed more consciousness. Since the man was on suspicion of body packing, plain abdomen X-ray was done and the existence of several packs was confirmed. Treatment by polyethylene glycol 2 lit/h and naloxone and serial activated charcoal started and finally, the patient excreted the packets and was then discharged with good condition.

This article investigates the problem of simultaneous attitude and vibration control of a flexible spacecraft to perform high precision attitude maneuvers and reduce vibrations caused by the flexible panel excitations in the presence of external disturbances, system uncertainties, and actuator faults. Adaptive integral sliding mode control is used in conjunction with an attitude actuator fault iterative learning observer (based on sliding mode) to develop an active fault tolerant algorithm considering rigid-flexible body dynamic interactions. The discontinuous structure of fault-tolerant control led to discontinuous commands in the control signal, resulting in chattering. This issue was resolved by introducing an adaptive rule for the sliding surface. Furthermore, the utilization of the sign function in the iterative learning observer for estimating actuator faults has not only enhanced its robustness to external disturbances through a straightforward design, but has also led to a decrease in computing workload. The strain rate feedback control algorithm has been employed with the use of piezoelectric sensor/actuator patches to minimize residual vibrations caused by rigid-flexible body dynamic interactions and the effect of attitude actuator faults. Lyapunov's law ensures finite-time overall system stability even with fully coupled rigid-flexible nonlinear dynamics. Numerical simulations demonstrate the performance and advantages of the proposed system compared to other conventional approaches.

In traditional speech processing, feature extraction and classification were conducted as separate steps. The advent of deep neural networks has enabled methods that simultaneously model the relationship between acoustic and phonetic characteristics of speech while classifying it directly from the raw waveform. The first convolutional layer in these networks acts as a filter bank. To enhance interpretability and reduce the number of parameters, researchers have explored the use of parametric filters, with the SincNet architecture being a notable advancement. In SincNet's initial convolutional layer, rectangular bandpass filters are learned instead of fully trainable filters. This approach allows for modeling with fewer parameters, thereby improving the network's convergence speed and accuracy. Analyzing the learned filter bank also provides valuable insights into the model's performance. The reduction in parameters, along with increased accuracy and interpretability, has led to the adoption of various parametric filters and deep architectures across diverse speech processing applications. This paper introduces different types of parametric filters and discusses their integration into various deep architectures. Additionally, it examines the specific applications in speech processing where these filters have proven effective.