Current Issue : April-June Volume : 2025 Issue Number : 2 Articles : 5 Articles
This paper establishes fatigue life prediction models using the soft computing method to address insufficient parameter consideration and limited computational accuracy in predicting the fatigue life of fiber-reinforced polymer (FRP) strengthened concrete beams. Five different input forms were proposed by collecting 117 sets of fatigue test data of FRPstrengthened concrete beams from the existing literature and integrating the outcomes from Pearson correlation analysis and significance testing. Using Gene Expression Programming (GEP), the effects of various input configurations on the accuracy of model predictions were examined. The model prediction results were also evaluated using five statistical indicators. The GEP model used concrete compressive strength, the steel reinforcement stress range ratio to the yield strength, and the stiffness factor as input parameters. Subsequently, using the same input parameters, the Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) method was then employed to develop a fatigue life prediction model. Sensitivity analyses of the GEP and MOGA-EPR models revealed that both could precisely capture the fundamental connections between fatigue life and multiple contributing variables. Compared to existing models, the proposed ones have higher prediction accuracy with a coefficient of determination reaching 0.8, significantly enhancing the accuracy of fatigue life predictions for FRP-strengthened concrete beams....
This publication focuses on the use of the artificial intelligence for detecting anomalies, especially in the blockchain network. The research methodology includes the selection of anomalies to be detected and the processing of blockchain data. Various artificial intelligence methods were implemented for anomaly detection as part of the tests, and one new solution—a Fuzzy Neural Network—was presented. The findings indicate the possibility of detecting selected anomalies in the blockchain using artificial intelligence, which is of significant importance for the security of this technology. The conclusions present a discussion on limitations, future research prospects, and guidelines for future work....
This paper proposes a reversible data hiding (RDH) scheme for images with an improved convolutional neural network (CNN) predictor (ICNNP) that consists of three modules for feature extraction, pixel prediction, and complexity prediction, respectively. Due to predicting the complexity of each pixel with the ICNNP during the embedding process, the proposed scheme can achieve superior performance compared to a CNNPbased scheme. Specifically, an input image is first split into two sub-images, i.e., a “Circle” sub-image and a “Square” sub-image. Meanwhile, each sub-image is applied to predict another one with the ICNNP. Then, the prediction errors of pixels are sorted based on the predicted pixel complexities. In light of this, some sorted prediction errors with less complexity are selected to be efficiently applied for low-distortion data embedding with a traditional histogram-shifting technique. Experimental results show that the proposed ICNNP can achieve better rate-distortion performance than the CNNP, demonstrating its effectiveness....
Musculoskeletal disorders are very common injuries among occupational and healthcare workers. These injuries are preventable in many scenarios using exoskeletonbased assistive technology. Soft robotics initiates an evolution in exoskeleton devices due to their safe human interactions, ergonomic design, and adaptive characteristics. Despite their enormous advantages, it is a challenging task to model and control soft robotic devices due to their inherent nonlinearity and hysteresis. Learning-based approaches are becoming more popular to overcome these limitations. This work proposes an approach to estimate the pressure input for a pneumatically actuated soft robotic elbow exoskeleton to assist occupational workers to avoid musculoskeletal disorders. An elbow exoskeleton design made up of modular pneumatic soft actuators is discussed, which helps to flex/extend an elbow joint. Machine learning (ML) approaches are used to develop a relationship between the air pressure, the bending angle of the elbow, and the percentage of the weight of the arm to be assisted by the exoskeleton. The most popular and widely used regression-based ML approaches are applied and compared in terms of accuracy and computation cost. Further, a modified KNN (K-Nearest Neighbor) approach is proposed, which outperforms the accuracy of other approaches....
Over recent decades, soft and reconfigurable robots have rapidly emerged thanks to their ability to interact safely with humans and adapt to complex environments. . However, their softness makes accurate control challenging, requiring high-fidelity sensing for posture and contact estimation.Traditional camera-based sensors and load cells have limited portability and accuracy, and they will inevitably increase the robot’s cost and weight. In this study, instead of using specialized sensors, only distributed pressure data inside a pneumatics-driven soft arm are collected and the physical reservoir computing principle is applied to simultaneously predict its kinematic posture (i.e., bending angle) and payload status (i.e., payload mass). Results show that, with careful readout training, one can obtain accurate bending angle and payload mass predictions via simple, weighted linear summations of pressure readings. In addition,analysis show that, to guarantee low prediction errors within 10%, bending angle prediction requires less training data than payload prediction. This reveals that balanced linear and nonlinear body dynamics are critical for the physical reservoir to accomplish complex proprioceptive and exteroceptive information perception tasks. Finally, the method of exploring efficient readout training methods presented here could be extended to other soft robotic systems to maximize their perception capabilities....
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