In current decades, significant advancements in robotics engineering and autonomous vehicles have improved the requirement for precise depth measurements. Depth estimation (DE) is a traditional task in computer vision that can be appropriately predicted by applying numerous procedures. This task is vital in disparate applications such as augmented reality and target tracking. Conventional monocular DE (MDE) procedures are based on depth cues for depth prediction. Various deep learning techniques have demonstrated their potential applications in managing and supporting the traditional ill-posed problem. The principal purpose of this paper is to represent a state-of-the-art review of the current developments in MDE based on deep learning techniques. For this goal, this paper tries to highlight the critical points of the state-of-the-art works on MDE from disparate aspects. These aspects include input data shapes and training manners such as supervised, semi-supervised, and unsupervised learning approaches in combination with applying different datasets and evaluation indicators. At last, limitations regarding the accuracy of the DL-based MDE models, computational time requirements, real-time inference, transferability, input images shape and domain adaptation, and generalization are discussed to open new directions for future research.

Because of the static nature of conventional principal component analysis (PCA), natural process variations may be interpreted as faults when it is applied to processes with time-varying behavior. In this paper, therefore, we propose a complete adaptive process monitoring framework based on incremental principal component analysis (IPCA). This framework updates the eigenspace by incrementing new data to the PCA at a low computational cost. Moreover, the contribution of variables is recursively provided using complete decomposition contribution (CDC). To impute missing values, the empirical best linear unbiased prediction (EBLUP) method is incorporated into this framework. The effectiveness of this framework is evaluated using benchmark simulation model No. 2 (BSM2). Our simulation results show the ability of the proposed approach to distinguish between time-varying behavior and faulty events while correctly isolating the sensor faults even when these faults are relatively small.

In this paper, fatigue life of solder joints of the power module and the printed circuit board in a power inverter under directional random vibration using the finite element method (FEM) was evaluated. Five angles of 0°, 30°, 45°, 60°, and 90° were selected for direction of vibration loading. The simulation results showed that the outermost corner of the solder layer undergoes the maximum stress during the vibration frequencies in all the situations. In general, with the increase of input frequency, the solder joints got more susceptible to the catastrophic failure. Moreover, it was found that in lower angles of loading direction, the peeling stress is dramatically increased in the solder layer. This event can be attributed to the considerable portion of shear stress in loading directions with lower angles. The experimental results also proved the FEM simulation and showed that the void growth and coalescence, resulted by fatigue, are intensified in the lower angles of vibration loading. Overall, this paper indicates that the reliability of a power module under a normal loading (90°) is the highest among all other conditions.

This paper introduces a new adaptive framework for fault detection and diagnosis using neural-based PCA. This framework addresses the limitations of traditional PCA in handling time-varying processes. The adaptive framework updates the correlation matrix recursively, allowing it to adapt to the natural time-varying behavior of processes. It also recursively determines the number of principal components and the confidence limits for three process monitoring statistics (T², Q, and the combined index φ). To diagnose faults, four different types of contribution plots are used as follows: complete decomposition contributions (CDC), partial decomposition contributions (PDC), diagonal-based contributions (DBCs), and reconstruction-based contributions (RBCs). The evaluation through three simulation studies—including a numerical example, the continuous stirred tank reactor (CSTR) process, and water resource recovery facilities (WRRFs)—demonstrates that the combined statistics provided superior fault detection and diagnosis performance compared with individual statistics. Additionally, the study of the isolation method shows that no single method can definitively be claimed as superior. Overall, our study highlights the strength and versatility of neural-based PCA for detecting and diagnosing faults in dynamic processes.

The inverted pendulum is a non-linear unbalanced system that needs to be controlled using motors to achieve stability and equilibrium. The inverted pendulum is constructed with Lego and using the Lego Mindstorm NXT, which is a programmable robot capable of completing many different functions. In this paper, an initial design of the inverted pendulum is proposed and the performance of different sensors, which are compatible with the Lego Mindstorm NXT was studied. Furthermore, the ability of computer vision to achieve the stability required to maintain the system is also investigated. The inverted pendulum is a conventional cart which can be controlled using a Fuzzy Logic controller that produces a self-tuning PID control for the cart to move on. The fuzzy logic and PID are simulated in MATLAB and Simulink, and the program for the robot developed in the LabVIEW software.

The objective of this paper is to describe an approach to detect the slip and contact force in real-time feedback. In this novel approach DAVIS camera used as a vision tactile sensor due to its fast process speed and high resolution. Two hundred experiments were performed on four objects with different shape, size, weight and material to compare the accuracy and respond of the Baxter robot grippers to avoid slipping. The advanced approach is validated by using a force-sensitive resistor (FSR402). The events captured with DAVIS camera are processed with specific algorithms to provide feedback to the Baxter robot aiding it to detect the slip.