Pose invariant method for emotion recognition from 3D images

Abstract. Wake steering strategies are employed to increase the overall power production of wind farms by deflecting wakes of upstream turbines away from downstream ones. The gain in net power comes at the expense of increased fatigue loads experienced by downstream turbines. In this work we investigate performance and fatigue loading characteristics of a small farm consisting of five aligned International Energy Agency Wind Technology Collaboration Programme 15 MW wind turbines. A parametric study is performed where, for every wind direction from −20 to 20°, the yaw misalignment angle varies from −25 to 25°. This setup allows us to investigate asymmetries and identify optimal conditions for a given wind direction. In general, we find that positive yaw configurations are preferred and that yaw configurations that result in attractive power differences when compared to a baseline no-yaw scenario (25 %) come with significant increase in fatigue loading (we use the standard deviation and damage-equivalent load (DEL) of the blade-root, low-speed shaft, and tower-base moments as proxies for fatigue load). We find that for any given positive wind inflow angle, yaw angles between −2.5 and 15° yield power differences of 10 %–20 % over a no-yaw baseline, and positive yaw is preferred because of lower fatigue loading. For any given negative wind inflow angles, positive yaw also results in lower magnitudes of standard deviation and DEL for the channels investigated. A small power loss of up to 2 % is observed for some positive yaw angles under negative wind directions (as compared to symmetric negative yaw and positive wind cases), but improvements in terms of loads exceed 25 % and may be enough to justify a positive yaw configuration under negative winds as well. We show that such behavior can be explained by partial waking and the direction of the rotation of the rotor.

Wind Engineering Analysis of Parabolic Trough Collectors to Optimise Wind Loads and Heat Loss

: An experimental investigation was conducted to demonstrate the capability of a newly developed flow-shaping technique to test full-scale inlet/ engine systems in the AEDC 16-ft Propulsion Wind Tunnel (transonic) at angles of yaw and combinations of angle of attack and yaw. Simulation of the flow field approaching the inlet was accomplished at Mach numbers in the range from 0.6 to 0.9 at positive yaw angles up to 6 deg over an angle-of-attack range from 0 to 8 deg and at negative yaw angles up to 6 deg over an angle of attack range from 0 to 12 deg. Inlet pressure distribution (ramp, lip, and sideplate), local flow angularity, and local Mach number in front of the inlet were used to verify the technique.

Patients with amygdala lesions were found to be impaired in recognizing the fear emotion both from face and from music. In patients with Parkinson's disease (PD), impairment in recognition of emotions from facial expressions was reported for disgust, fear, sadness and anger, but no studies had yet investigated this population for the recognition of emotions from both face and music. The ability to recognize basic universal emotions (fear, happiness and sadness) from both face and music was investigated in 24 medicated patients with PD and 24 healthy controls. The patient group was tested for language (verbal fluency tasks), memory (digit and spatial span), executive functions (Similarities and Picture Completion subtests of the WAIS III, Brixton and Stroop tests), visual attention (Bells test), and fulfilled self-assessment tests for anxiety and depression. Results showed that the PD group was significantly impaired for recognition of both fear and sadness emotions from facial expressions, whereas their performance in recognition of emotions from musical excerpts was not different from that of the control group. The scores of fear and sadness recognition from faces were neither correlated to scores in tests for executive and cognitive functions, nor to scores in self-assessment scales. We attributed the observed dissociation to the modality (visual vs. auditory) of presentation and to the ecological value of the musical stimuli that we used. We discuss the relevance of our findings for the care of patients with PD.

Enhancing Humans Trust in Robots through Explanations

The gyroscope deterministic error can be estimated through laboratory test using rate table. However, the setting misalignment, if exists, degrade the estimation accuracy severely. To solve this problem, for the simple error model which contains sensor misalignment and bias, a setting misalignment error compensation method is proposed in this paper. This method based on the fact that the setting misalignment can be represented by Euler angle: roll, pitch, and yaw angles. The identification of the three angles is performed step by step. For each pitch and yaw angle, we use the fact that the one specific element of misalignment matrix multiplied by roll matrix is exactly 1 and find pitch and yaw angle which make the estimate of the specific element closely 1. Next, we estimate roll angle with the same procedure. Finally, we estimate the sensor misalignment matrix using the estimated Euler angles. Thorough simulation, the proposed method is verified

This work presents a method to automatically locate facial feature points under large variations in pose, illumination and facial expressions. First we propose a method to calculate probabilistic-like output for each pixel of image. This probabilistic-like output describes the possibility of the pixel to be the center of specified object. A Gaussian mixture model is used to approximate the distribution of probabilistic-like output. The centers of these Gaussians are assigned with a probabilistic-like measure and they are considered as candidate feature points. There might be one or more candidate feature points in each facial region. A 3D model of facial feature points is built to enforce constraints on the localization results of feature points. Compared with active shape model (ASM) and its variant methods, our method could accommodate larger variations in pose, lighting and face expressions. Moreover, it is less sensitive to initialization errors, accurate, and fast. It takes a computer with P4 CPU about 10 ms to locate the five feature points (two eye centers, two mouth corners and nose tip). The feature localization accuracy is comparable with the accuracy of manually labeled features and it is robust to noise (glasses, beards). Experiments on FERET gallery and PIE are reported in this paper as well.

Quadrotor is one type of unmanned aerial vehicle that has the ability to vertical takeoff and landing. In this research, a system designed to stabilize quadrotor during flight condition by maintaining at angle of roll, pitch, yaw, and x, y, and z axis position using LQR full state feedback with artificial neural network (ANN).The LQR full state feedback method uses 12 states with each K constant being tuned with ANN. This research implements ANN method to change feedback constant at angle of roll, pitch, and yaw and x, y, and z axis. The artificial neural network method uses 12 input layers, 12 hidden layers, and 1 output layer.Testing with ANN improved the rise time to ± 2.18 seconds at the roll angle, ± 1.23 seconds at the pitch angle, and ± 0.31 seconds at the yaw angle. Improved settling time value up to ± 2.41 seconds at roll angle, ± 1.23 seconds at pitch angle, and ± 1.07 seconds at yaw angle. Improved steady state eror value of ± 0.61% at roll angle, ± 4.88% at pitch angle, and ± 0.82% at the yaw angle.

IntroductionRecent developments in the field of social cognition have led to a renewed interest in basic and social emotion recognition in early stages of Alzheimer's Disease (AD) and FrontoTemporal Dementia (FTD). Despite the growing attention to this issue, only few studies have attempted to investigate emotion recognition using both visual and vocal stimuli. In addition, recent studies have presented conflicting findings regarding the extent of impairment in patients in the early stages of these diseases. The present study aims to investigate emotion understanding (both basic and social emotions), using different tasks with visual and auditory stimuli, to identify supramodal deficits in AD and FTD to provide a reliable tool to better outline their behavioral and emotional profile and useful instruments for their management.MethodsEighteen patients with AD and 15 patients with FTD were included in the study. Healthy control (HCs) subjects were recruited to obtain normative data for basic emotion recognition tests and social emotion recognition tasks. To evaluate basic emotion recognition, the Facial Emotion Recognition Battery (FERB) and the Emotional Prosody Recognition Battery (EPRB) were administered. To evaluate social emotion recognition, the Faux Pas (FP), Reading the Mind in the Eyes (RME), and Reading the Mind in the Voice (RMV) tests were employed.ResultsFTD patients performed significantly worse than HCs in most of the subtests of the basic emotion recognition batteries, where, instead, AD patients were significantly impaired only when required to match emotional facial expression in different individuals (subtask of the FERB). Moreover, FTD patients scored significantly lower in RME and RMV tests compared both to AD patients and to HCs. In addition, ADs were selectively impaired in RMV as respect to HCs.DiscussionFTD patients showed deficits in emotion recognition, affecting both basic and social emotions, whether conveyed through facial expressions or prosody. This result may explain the well-known social behavioral difficulties observed in FTD patients from the early stages of the disease. The fewer and specific deficits in AD patients with comparable MMSE scores may be attributed to the mild degree of impairment, as these deficits may appear later in the progression of AD.

This paper introduces the new fast response aerodynamic probe (FRAP®), which was recently developed at the ETH Zurich. The probe provides time-resolved, three-dimensional flow measurements using the virtual four sensor technique. Two probes work in tandem, being comparable to a pair of pneumatic needle probes. The first probe, being yaw angle sensitive, is positioned in three circumferential positions. The second probe being pitch angle sensitive is brought into exactly the same position as the first probe. The resulting set of four measurements is phaselock-averaged to one specific rotor trigger position. Then the reduced data sets are combined to four calibration coefficients, which are then further processed to determine the unsteady flow vector. The results consist of yaw and pitch flow angles as well as the total and static pressure. The outer diameter of the cylindrical probe head was miniaturized to 0.84mm, hence probe blockage effects as well as dynamic lift effects are reduced. The shape of the probe head was optimized in view of the manufacturing process as well as aerodynamic considerations. The optimum geometry for pitch sensitivity was found to be a cylindrical surface with the axis perpendicular to the probe shaft. The internal design of the probes led to a sensor cavity eigen frequency of 44 kHz for the yaw sensitive and 34kHz for the pitch sensitive probe. The steady aerodynamic characteristics of the probe were measured using the free jet probe calibration facility of the laboratory. The full set of calibration surfaces is given. Data acquisition is done with a fully automated traversing system, which moves the probe within the test rig and samples the signal with a PC-based A/D-board. An error analysis implemented into the data reduction routines revealed acceptable accuracy for flow angles as well as pressures for many turbomachinery flows. Depending on the dynamic head of the application the yaw angle is accurate within ±0.35° and pitch angle within ±0.7°. Finally, a comparison of time averaged results to five hole probe measurements is discussed.

A novel hollow cylindrical cube-corner reflector (HCCCR) for the autocollimator (AC) is proposed. The angle measuring range of AC will be effectively increased by using the parallel propagation characteristics of the reflected light and the incident light in local area of this reflector. And the yaw and pitch angles of HCCCR will be measured through the morphological changes of the reflected beam. The experimental results show that the measuring range of the autocollimation angle measurement method is extended from ±30' to ±30°, and the dynamic measurement distance is 0.2∼5m, the measurement accuracy of pitch angle and yaw angle is better than 69" and 51", respectively.

In order to overcome the problem that the accuracy and efficiency of Mandarin emotion recognition is low based on singal-modal, a tri-modal Mandarin emotion recognition method based on the feature-level fusion of three important modal of speech, facial expression and body gesture is proposed. Firstly, different emotional features are extracted from speech, facial expressions and body gestures, and then the three modal emotion features are fused by IAGA method. Finally, SVM classifier is used to construct prediction model and emotion recognition is completed on it. The CHEAVD Chinease multi-modal emotion dataset is utilized to evaluate our proposed methods. Compared with the traditional mono-modal and dual-modal emotion recognition, the experimental results show that the dual-modal fusion has higher emotion recognition rate than the mono-modal, and the tri-modal fusion has higher emotion recognition rate than the dual-modal, which verifies the effectiveness of the tri-modal Mandarin emotion recognition

In several motion control applications, e.g. in precise pointing devices and navigation systems, an accurate estimate of the attitude of a device in the world coordinate system is required. Readily available sensors used to estimate the attitude suffer from drift, magnetic disturbances, or a lack of information about the direction of the geodetic north. In this paper, an algorithm which estimates the attitude of a camera using camera images and a Digital Surface Model (DSM) referenced by GPS is proposed. The algorithm uses shape features of a skyline extracted from a camera image and synthetic skylines rendered from a DSM in order to estimate the roll and pitch angles, while the yaw angle is estimated using grid search. The algorithm was evaluated using roughly 600 camera images captured with varying pitch and yaw angles. The standard deviations from the ground truth, provided by a high precision pointing device, are 0.037° ,0.015°, and 0.018° for roll, pitch, and yaw respectively. The results indicate a higher precision than current camera attitude estimation algorithms using a DSM, while also providing a robust yaw estimate.

Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. The social signal processing subfield of emotion recognition from facial expression is used in a wide range of contexts, particularly for human-computer interaction. Automatic emotion recognition has been the subject of numerous studies, most of which use a machine learning methodology. The recognition of simple emotions like anger, happiness, contempt, fear, sadness, and surprise, however, continues to be a difficult topic in computer vision. Deep learning has recently drawn increased attention as a solution to a variety of practical issues, including emotion recognition. In this study, we improved the convolutional neural network technique to identify 7 fundamental emotions and evaluated several preprocessing techniques to demonstrate how they affected the CNN performance. This research focuses on improving facial features and expressions based on emotional recognition. By identifying or recognising facial expressions that elicit human responses, it is possible for computers to make more accurate predictions about a person's mental state and to provide more tailored responses. As a result, we examine how a deep learning technique that employs a convolutional neural network might improve the detection of emotions based on facial features (CNN). Multiple facial expressions are included in our dataset, which consists of about 32,298 photos for testing and training. The preprocessing system aids in removing noise from the input image, and the pretraining phase aids in revealing face detection after noise removal, including feature extraction. As a result, the existing paper generates the classification of multiple facial reactions like the seven emotions of the facial acting coding system (FACS) without using the optimization technique, but our proposed paper reveals the same seven emotions of the facial acting coding system.

One of the challenges in speech emotion recognition is robust and speaker-independent emotion recognition. In this paper, we take a cascaded normalization approach, combining linear speaker level, nonlinear value level and feature vector level normalization to minimize speaker-related effects and to maximize class separability with linear kernel classifiers. We use extreme learning machine classifiers on a four class (i.e. joy, anger, sadness, neutral) problem. We show the efficacy of our proposed method on the recently collected Turkish Emotional Speech Database.