Application of Various Machine Learning Models for Process Stability of Bio-Electrochemical Anaerobic Digestion

The application of a machine learning (ML) model to bio-electrochemical anaerobic digestion (BEAD) is a future-oriented approach for improving process stability by predicting performances that have nonlinear relationships with various operational parameters. Five ML models, which included tree-, regression-, and neural network-based algorithms, were applied to predict the methane yield in BEAD reactor. The results showed that various 1-step ahead ML models, which utilized prior data of BEAD performances, could enhance prediction accuracy. In addition, 1-step ahead with retraining algorithm could improve prediction accuracy by 37.3% compared with the conventional multi-step ahead algorithm. The improvement was particularly noteworthy in tree- and regression-based ML models. Moreover, 1-step ahead with retraining algorithm showed high potential of achieving efficient prediction using pH as a single input data, which is plausibly an easier monitoring parameter compared with the other parameters required in bioprocess models.

Dynamic logarithmic state and control quantization for continuous-time linear systems

This paper addresses logarithmic quantizers with dynamic sensitivity design for continuous-time linear systems with a quantized feedback control law. The dynamics of state quantization and control quantization sensitivities during “zoom-in”/“zoom-out” stages are proposed. Dwell times of the dynamic sensitivities are co-designed. It is shown that with the proposed algorithm, a single-input continuous-time linear system can be stabilized by quantized feedback control via adopting sensitivity varying algorithm under certain assumptions. Also, the advantage of logarithmic quantization is sustained while achieving stability. Simulation results are provided to verify the theoretical analysis.

Stabilization of the Cart–Inverted-Pendulum System Using State-Feedback Pole-Independent MPC Controllers

In this paper, a pole-independent, single-input, multi-output explicit linear MPC controller is proposed to stabilize the fourth-order cart–inverted-pendulum system around the desired equilibrium points. To circumvent an obvious stability problem, a generalized prediction model is proposed that yields an MPC controller with four tuning parameters. The first two parameters, namely the horizon time and the relative cart–pendulum weight factor, are automatically adjusted to ensure a priori prescribed system gain margin and fast pendulum response while the remaining two parameters, namely the pendulum and cart velocity weight factors, are maintained as free tuning parameters. The comparison of the proposed method with some optimal control methods in the absence of disturbance input shows an obvious advantage in the average peak efficiency in favor of the proposed SIMO MPC controller at the price of slightly reduced speed efficiency. Additionally, none of the compared controllers can achieve a system gain margin greater than 1.63, while the proposed one can go beyond that limit at the price of additional degradation in the speed efficiency.

Recovery of Global Symmetries in a 't Hooftian Universe

All fundamental Planck scale symmetries are restored on a global level when a new charge is postulated in a finite, closed, Euclidean discrete space. Gravity emerges as a residual effect of the electromagnetic force in this scenario, resulting in a deterministic toy universe driven by a single input parameter. The model is developed using a constructive approach. Randomness is identified using a Chaintin argument. Aleph0 definite value is tied to the size of the universe. This is not an interpretation of Quantum Mechanics, but a deeper attempt to describe nature.

Design of protection and control scheme for hybrid nanogrid

Nanogrid is “The new ray of hope” for people living in remote isolated locations as well as where power supply reliability is poor. A nanogrid is a small power capacity distribution system with the ability to operate standalone or with a utility grid. It consists of local power production supplying local loads and energy storage systems. In this paper, an innovative inverter design is presented, which converts the power in a single stage. It is superior to the traditional two-stage inverter system and can supply hybrid loads (AC and DC loads) with a single input. System AC and DC bus voltages are regulated under both steady-state and dynamic load variation conditions in the nanogrid. Simulation results are presented which confirm the suitability of the inverter and its control strategy for a hybrid nanogrid system.

Study of Weak Fuzzy Automaton Homomorphisms on Single Input Fuzzy Automata

In this paper we have defined input-connected fuzzy automaton and established that in an input connected fuzzy automaton, element of an orbit maps to an element of the orbit under the weak fuzzy automaton endomorphism. Further, if an input x is unicore in the fuzzy automaton, then the number of weak fuzzy automaton endomorphisms of A(x) is equal to the number of states.