H∞ Controller with Graphical LMI Region Profile for Gantry Crane System

This paper presents investigations into the development of H∞ controller with pole clustering based on LMI techniques to control the payload positioning of INTECO 3D crane system with very minimal swing. The linear model of INTECO 3D crane system is obtained using the system identification process. Using LMI approach, the regional pole placement known as LMI region combined with design objective in H∞ controller guarantee a fast input tracking capability, precise payload positioning and very minimal sway motion. A graphical profile of the transient response of crane system with respect to pole placement is very useful in giving more flexibility to the researcher in choosing a specific LMI region. The results of the response with the controllers are presented in time domains. The performances of control schemes are examined in terms of level of input tracking capability, sway angle reduction and time response specification. Finally, the control techniques is discussed and presented

Estimation of volume and weight of apple by using 2D contactless computer vision measuring method

Volume and weight are key parameters that have been used as a benchmark to identify the quality of apples. These two parameters can be easily measured individually by using a weighing balance to measure weight and the water displacement method (WDM) to measure volume. However, these two methods are not suitable to apply in industries since both methods require a lot of time to obtain the final output. Therefore, a new approach is needed. The main objective of this work is to develop a contactless system based on computer vision system that can estimate the volume and weight of apples by using the width and height via 2D image captured. The camera needs to calibrate in order to get the ratio of pixel/cm by using the checkerboard point detection technique. Mask regional convolution neural network (R-CNN) was used to detect and segment apple images while providing the height and width of apples. The system was tested with four different settings, with 20cm and 30cm distance, and two different camera models. The best estimation of the volume and weight of apples obtained were with errors of 11.97 % and 11.49 % respectively. Overall, the findings showed that height and width from a 2D calibrated perspective can be used as an alternative method for the contactless assessment of apple volume and weight

Classical angular tracking and intelligent anti-sway control for rotary crane system

This paper presents investigations into the development of hybrid control schemes for sway suppression and rotational angle tracking of a rotary crane system. A lab-scaled rotary crane is considered and the dynamic model of the system is derived using the Euler-lagrange formulation. To study the effectiveness of the controllers, initially a classical controller which is collocated proportional-derivative (PD) controller is developed for control of rotary motion. This is then extended to incorporate a non-collocated fuzzy logic controller for control of sway angle of the pendulum. Implementation results of the response of the rotary crane system with the controllers are presented in time and frequency domains. The performances of the control schemes are assessed in terms of level of sway reduction, rotational angle tracking capability and time response specifications. Finally, a comparative assessment of the control techniques is presented and discussed

Simple Pole Placement Controller for Elastic Joint Manipulator

This paper presents investigations into the development of simple pole placement controller for tip angular position tracking and deflection reduction of an elastic joint manipulator system. A Quanser elastic joint manipulator is considered and the dynamic model of the system is derived using the Euler-Lagrange formulation. The pole placement controller is designed based on integral state feedback structure and the feedback gain is computed based on the desired time response specifications of tip angular position. The proposed control scheme is also compared with a hybrid Linear Quadratic Regulator (LQR) with input shaper control scheme. The performances of the control schemes are assessed in terms of tip angular tracking capability, level of deflection angle reduction and time response specifications. Finally, a comparative assessment of the control techniques is presented and discussed

In-The-Wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy

Deepfake technology has become increasingly sophisticated in recent years, making detecting fake images and videos challenging. This paper investigates the performance of adaptable convolutional neural network (CNN) models for detecting Deepfakes. In-the-wild OpenForensics dataset was used to evaluate four different CNN models (DenseNet121, ResNet18, SqueezeNet, and VGG11) at different batch sizes and with various performance metrics. Results show that the adapted VGG11 model with a batch size of 32 achieved the highest accuracy of 94.46% in detecting Deepfakes, outperforming the other models, with DenseNet121 as the second-best performer achieving an accuracy of 93.89% with the same batch size. Grad-CAM techniques are utilized to visualize the decision-making process within the models, aiding in understanding the Deepfake classification process. These findings provide valuable insights into the performance of different deep learning models and can guide the selection of an appropriate model for a specific application

Three-Dimensional Convolutional Approaches for the Verification of Deepfake Videos: The Effect of Image Depth Size on Authentication Performance

Deep learning has proven to be particularly effective in tasks such as data analysis, computer vision, and human control. However, as this method has become more advanced, it has also led to the creation of DeepFake video sequences and images in which alterations can be made without immediately appealing to the viewer. These technological advancements have introduced new security threats, including in the field of education. For example, in online exams and tests conducted through video conferencing, individuals may use Deepfake technology to impersonate another person, potentially allowing them to cheat by having someone else take the exam in their place. Several detection approaches have been proposed to address these issues, including systems that use both spatial and temporal features. However, existing approaches have limitations regarding detection accuracy and overall effectiveness. The paper proposes a technique for detecting Deepfakes that combines temporal analysis with convolutional neural networks. The study explores various 3-D Convolutional Neural Networks-based (CNN-based) model approaches and different sequence lengths of facial photos. The results indicate that using a 3-D CNN model with 16 sequential face images as input can detect Deepfakes with up to 97.3 percent accuracy on the FaceForensic dataset. Detecting Deepfakes is crucial as they pose a threat to the authenticity of visual media. The proposed technique offers a promising solution to this issue

The Vehicle Steer by Wire Control System by Implementing PID Controller

The latest technology of vehicle steer-by-wire (VSBW) system has promised significant improvement in vehicle safety, dynamics, stability, comfort and maneuverability. Due to complete separation between steering wheel and the front wheels gives the practical problems for steering control especially on directional control and wheel synchronization of vehicle. This paper presents investigations into the development of PID control scheme for directional control and wheel synchronization of a VSBW system. Two PID controllers are used to control the steering wheel angle and front wheel angle. The PID controllers use the front wheel tracking error to generate controlled steering angle. The Ziegler Nichols method is used for tuning the PID parameters. The implementation environment is developed within Matlab/Simulink software for evaluation of performance of the control scheme. Implementation results of the response of the VSBW system with the PID controller are presented in time domains. The performances of control schemes are examined in terms of input tracking capability, wheel synchronization and time response specifications with the absence of disturbances

Using cascade CNN-LSTM-FCNs to identify AI-altered video based on eye state sequence

Deep learning is notably successful in data analysis, computer vision, and human control. Nevertheless, this approach has inevitably allowed the development of DeepFake video sequences and images that could be altered so that the changes are not easily or explicitly detectable. Such alterations have been recently used to spread false news or disinformation. This study aims to identify Deepfaked videos and images and alert viewers to the possible falsity of the information. The current work presented a novel means of revealing fake face videos by cascading the convolution network with recurrent neural networks and fully connected network (FCN) models. The system detection approach utilizes the eye-blinking state in temporal video frames. Notwithstanding, it is deemed challenging to precisely depict (i) artificiality in fake videos and (ii) spatial information within the individual frame through this physiological signal. Spatial features were extracted using the VGG16 network and trained with the ImageNet dataset. The temporal features were then extracted in every 20 sequences through the LSTM network. On another note, the pre-processed eye-blinking state served as a probability to generate a novel BPD dataset. This newly-acquired dataset was fed to three models for training purposes with each entailing four, three, and six hidden layers, respectively. Every model constitutes a unique architecture and specific dropout value. Resultantly, the model optimally and accurately identified tampered videos within the dataset. The study model was assessed using the current BPD dataset based on one of the most complex datasets (FaceForensic++) with 90.8% accuracy. Such precision was successfully maintained in datasets that were not used in the training process. The training process was also accelerated by lowering the computation prerequisites

Application Of Defect Detection In Gluing Line Using Shape-Based Matching Approach

This paper investigates various approaches for automated inspection of gluing process using shape-based matching application. A new supervised defect detection approach to detect gap defect, bumper defect and bubble defect in gluing application is proposed. The creation of region of interest for important region of the object is further explained. The Correlation algorithm to determine better image processing result using template matching techniques is also proposed. This technique does not only reduce execution time, but also produce high accuracy in defect detection rate. The recognition efficiency will achieve more than 95% with defect’s data for further process

Liquid slosh control by implementing model-free PID controller with derivative filter based on PSO

Conventionally, the control of liquid slosh system is done based on model-based techniques that challenging to implement practically because of the chaotic motion of fluid in the container. The aim of this article is to develop the tuning technique for model-free PID with derivative filter (PIDF) parameters for liquid slosh suppression system based on particle swarm optimization (PSO). PSO algorithm is responsible to find the optimal values for PIDF parameters based on fitness functions which are Sum Squared Error (SSE) and Sum Absolute Error (SAE) of the cart position and liquid slosh angle response. The modelling of liquid slosh in lateral movement is considered to justify the design of control scheme. The PSO tuning method is compared by heuristic tuning method in order to show the effectiveness of the proposed tuning approach. The performance evaluations of the proposed tuning method are based on the ability of the tank to follow the input in horizontal motion and liquid slosh level reduction in time domain. Based on the simulation results, the suggested tuning method is capable to reduce the liquid slosh level in the same time produces fast input tracking of the tank without precisely model the chaotic motion of the fluid