Motivated by the challenge that manual glaucoma detection is costly and time consuming, and that existing automated glaucoma detection processes lack either good performance or any statistical robustness testing procedures, we proposed an effective, robust, and automated framework for glaucoma detection based on fundus images. The proposed framework using 1450 color fundus images provided by Kaohsiung Chang Gung (KCG) Memorial Hospital in Taiwan. The proposed framework combines the use of convolutional neural networks (CNN) with the proposed generalized loss function, robust design of experiment (DOE), and Retinex theory to improve the results of fundus photography flash by restoring the original colors via removing the light effect. The proposed framework outperformed most archival automatic glaucoma detection approaches in its effectiveness and simplicity. The effectiveness was demonstrated via the estimated sensitivity 0.95, specificity 0.98, and accuracy 0.97. The simplicity was shown via the adopted basic CNN model compared to deep CNNs such as GoogleLeNet and ResNet152. Further, the proposed framework outperformed all relevant archival work in terms of its robustness, illustrated in the associated standard errors (all less than 0.03). This paper demonstrated the proposed framework via intuitive graphs and clear mathematical notations to make it easy for others to reproduce our results. The proposed framework and demonstration have the potential to become the standard automated glaucoma detection approaches in practice.

This study proposes the flow and temperature controllers of a cockpit environment control system (ECS) by implementing an optimal simplified swarm optimization (SSO) fuzzy proportional-integral-derivative (PID) control. The ECS model is considered as a multiple-input multiple-output (MIMO) and second-order dynamic system, which is interactive. In this work, we use five methods to design and compare the PID controllers in MATLAB and Simulink, including Ziegler–Nicolas PID tuning, particle swarm optimization (PSO) PID, SSO PID, and the combination of the fuzzy theory with PSO PID and SSO PID, respectively. The main contribution of this study is the pioneering implementation of SSO in a fuzzy PI/PID controller. Moreover, by adding the original gain parameters K_p, K_i, and K_d in the PID controller with delta values, which are calculated by fuzzy logic designer, we can tune the parameters of PID controllers in real time. This makes our control system more accurate, adaptive, and robust.

Applications in real life are composed of different kinds of network systems; these networks may be interfered by uncontrollable or unpredictable disruptive events involving natural disasters, human errors, evil-intentioned attacks, or other disturbances. Any of these disruptive events will cause networks to malfunction and possibly result in large economic losses. As a result, it is important to assess network resilience which is a measure to describe how a network system recovers its performance and functionality to a satisfactory level from a disruptive event. Inspired by the measures of reliability evaluation used in binary-state networks, this paper proposes a binary-addition tree algorithm-based resilience assessment for binary-state networks and applies it on a wildfire network with wireless sensors. Considering the stochastic nature of disruptive events, the proposed binary-addition tree algorithm-based resilience assessment comprehensively enumerates all the possible disruptive events and all the corresponding recovery strategies, and then calculate the network resilience. Furthermore, recovery cost limit is concerned in this paper for decision makers who choose the recovery strategies with their recovery cost limit and resilience requirement.

Modern life is becoming more and more convenient, all because of the perfect operation of the infrastructure network. However, if these infrastructure networks encounter interference, the operation of the network system will be delayed or even shut down, often causing huge losses in livelihood, economy, and society. Therefore, how to evaluate the resilience of the network system and provide protection and recovery strategies to deal with attacks that interfere with the system are important issues. This study considers a situation with protection, attack, and recovery strategies, proposes the time-related Binary-Addition Tree-based Resilience Assessment to consider more decision variables and parameters, and further includes the costs in the formulation of the protection and recovery strategies. Moreover, a new performance measure oriented to the degree of network reliability recovery to quantify the resilience of the network system is developed.

Acyclic (binary-state) networks are commonly implemented in many diverse disciplines and applications, including information systems, processes management, project management. These networks owe their popularity to the fact that they do not use directed cycles. Network reliability is the most commonly used tool for evaluating and managing systems modeled on acyclic networks, and minimal paths (MPs) play a significant role in evaluating this reliability. This study therefore proposes a new algorithm based on a novel convolution concept for evaluating acyclic network reliability. The proposed algorithm is able to find all convolution-based MP sets within polynomial time, and then obtain the symbolic function of the acyclic network reliability in terms of those convolution-based MP sets based on the pivotal decomposition. Its total time complexity is O(2n), which is the best among all existing MP algorithms which are at least O(2|P|), where O(|P|) = O(2n), and n and |P| are the number of nodes and MPs, respectively. The correctness and time complexity of the proposed algorithm is proven and examined. An example is used to display the novel convolution-based algorithm is implemented to solve the acyclic network reliability problem.