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A modified NMOS inverter employing output feedback theme has been proposed in this paper. The feedback structure can guarantee high voltage output at a wider range of low voltage input. The proposed inverter and the conventional pseudo-CMOS inverter are both fabricated by top gate coplanar In-Zn-O thin film transistors (IZO TFTs) for comparing. The experimental results show that the proposed inverter possesses better static performance exhibiting noise margin NML/NMH, output swing VOL-VOH, switching threshold VM of 3.95/5.82 V, 0.05-9.97 V, 4.05 V, respectively. In addition, the proposed inverter is also applied in the gate driver integrated by IZO TFTs to further verify its function. Benefited from good static characteristic of the proposed inverter, the output module is well controlled to produce a good output waveform. It is measured that the output waveform of the 120th-stage has almost no distortion compared with that of the 1st-stage at 25 kHz.

This paper presents a common-drain-based operational amplifier (OPAMP) fabricated by mono n-type indium-zinc-oxide (IZO) thin-film transistors (TFTs). Positive feedback technology is employed to the load TFTs by cross-coupled connection in order to boost the voltage gain of the common-drain differential pair. The OPAMP exhibits an open-loop voltage gain (Av) of 27 dB over a −3 dB bandwidth (BW) of 8.4 kHz at a DC supply voltage of 10 V. The measured unity-gain frequency (UGF), phase margin (PM) and DC power consumption are 119.4 kHz, 36° and 0.96 mW, respectively. Moreover, the chip area of the proposed OPAMP is as small as 0.37 mm $\times0.3$ mm since this concise topology needs only 10 TFTs.

In this paper, the effect of ultraviolet (UV) irradiation treatment of active layer IGZO on the bias stability of amorphous IGZO thin film transistor with etch stop (ES) structure is studied. Along with the increase in UV irradiation time, the surface of the IGZO film becomes smoother. An appropriate UV irradiation treatment cannot only improve the bias stress stability but also suppress the lump behavior generated by light illumination. The devices with irradiation time of 1 min exhibits an excellent properties with the field effect mobility of 15.07 cm2/V·s, subthreshold swing of 0.2 V/dec, and all bias stress less than 0.2 V including NIBS and PIBS. However, a relative long UV treatment would result in deteriorating the bias stability of devices. The fact implies that the reasonable UV irradiation treatment on the active layer island before depositing the ES layer is advantageous for improving the stability of the a-IGZO TFT device.

A recently modern stochastic optimization algorithm has been developed by observing the life of slime mold physarum polycephalum in nature. The algorithm is called the slime mold algorithm (SMA) with an excellent exploratory capacity and exploitation inclination. Still, slipping into optimal local is easy to happen and slowly converges speed while dealing with complicated problems. This article proposes a new process of improving SMA (namely ISMA) by adapting the weight coefficient and cooperating the reverse learning strategy in the expression of agents updating locations to enhance the algorithm's optimization performance. Many selected benchmark functions and the optimal operation of cascade reservoirs are applied to evaluate the performance of the proposed algorithm. Comparisons of the proposed approach's results with the various algorithms under the case situations show that the recommended solution produces better performance than the different competing algorithms.

This paper presents an integrated operational amplifier (OPAMP) consisting of only n-type metal oxide thin-film transistors. In addition to using positive feedback in the input differential pair, a transconductance-enhancement topology is applied to improve the gain of the OPAMP. The OPAMP has a voltage gain (Av) of 29.54 dB over a 3-dB bandwidth of 9.33 kHz at a supply voltage of 15 V. The unity-gain frequency, phase margin (PM), and dc power consumption (PDC) are 180.2 kHz, 21.5° PM and 5.07 mW, respectively.

In order to reduce operation and maintenance costs, reliability, and quick response capability of multi-fault intelligent diagnosis for the wind turbine system are becoming more important. This paper proposes a rapid data-driven fault diagnostic method, which integrates data pre-processing and machine learning techniques. In terms of data pre-processing, fault features are extracted by using the proposed modified Hilbert–Huang transforms (HHT) and correlation techniques. Then, time domain analysis is conducted to make the feature more concise. A dimension vector will then be constructed by including the intrinsic mode function energy, time domain statistical features, and the maximum value of the HHT marginal spectrum. On the other hand, as the architecture and the learning algorithm of pairwise-coupled sparse Bayesian extreme learning machine (PC-SBELM) are more concise and effective, it could identify the single- and simultaneous-fault more quickly and precisely when compared with traditional identification techniques such as pairwise-coupled probabilistic neural networks (PC-PNN) and pairwise-coupled relevance vector machine (PC-RVM). In this case study, PC-SBELM is applied to build a real-time multi-fault diagnostic system. To verify the effectiveness of the proposed fault diagnostic framework, it is carried out on a real wind turbine gearbox system. The evaluation results show that the proposed framework can detect multi-fault in wind turbine gearbox much faster and more accurately than traditional identification techniques.

With the trend of manufacturing globalization, distributed production has attracted wide attention from the industry and academia. Nevertheless, there has been little research on the distributed hybrid flowshop scheduling (DHFS) problem. To make up for the gap, this study aims to solve the DHFS problem, in which multiple factories with hybrid flowshop scheduling (HFS) problems are considered. This problem consists of two subproblems: 1) how to choose a factory for each job and 2) how to schedule all jobs within the assigned factories. To solve the DHFS problem, a mathematical model is formulated. Then, inspired by successful applications of brain storm optimization (BSO) algorithm in different fields, we try to solve the DHFS with a hybrid BSO (HBSO). In the proposed algorithm, firstly, a new approach to calculate the distance in the procedure of clustering is embedded. Then, a novel constructive heuristic based on the Nawaz-Enscore-Ham (NEH) method, called distributed NEH, is proposed. Moreover, an improved crossover operator based on the partial-mapped crossover (PMX) is designed for the distributed scheduling problem. Finally, the 20 large-scale instances based on the realistic production data are randomly generated to test the performance of the proposed algorithm. The experimental results verify that the proposed algorithm is efficient and effective for solving the considered DHFS problems in comparison with the other recently published efficient algorithms.

The conveying distance during the backfill operation has become extensively large with the advancement of mining work. It requires the immediate attention of academic and industrial communities. Adding air entraining agent (AEA) can improve the rheological properties of the filling slurry and facilitat the filling slurry transfer to the long-distance underground stope. The AEA can generate a large number of tiny bubbles, which acted as floating balls and reduced the friction between the particles. In addition, AEA acted as a dispersing and wetting agent. Herein, we have obtained filling material from the Sanning mine (Hubei, China) and utilized sodium abietate (SA) and triterpenoid saponins (SJ), as air entraining agents, to improve the fluidity of filling slurry. The influence of AEA content on mechanical properties, structure and morphology has been analyzed by uniaxial compressive strength (UCS) testing, nuclear magnetic resonance (NMR) and scanning electron microscopy (SEM). The results reveal that the addition of AEA increased the fluidity of filling slurry. Moreover, UCS of the AEA-free cemented paste backfill (CPB) sample, cured for 28 days, was found to be 2.23 MPa. However, the addition of 0.2% AEA (SA and SJ) increased the UCS to 2.31 MPa and 2.35 MPa, respectively. However, when SA and SJ amount was increased to 0.6%, UCS decreased to 2.19 MPa and 2.13 MPa, respectively. Hence, UCS of the CPB initially increased with the addition of AEA, followed by a gradual decrease, which can be ascribed to the pore structure inside CPB. One should note that the presence of AEA increased the number of small pores inside the CPB. The SEM results shows that pore structure of CPB can be optimized by adding an optimal proper amount of AEA.

The blastability grading of a rock mass is an important parameter for the blasting design in metal mines. Appropriate blastability grading results guide the production to increase the efficiency and reduce the costs. In this paper, the multi-factors index system of the rock mass blastability consisting of the density, the p-wave velocity, the wave impedance, the uniaxial compressive strength, the rock elasticity, and the uniaxial tensile strength is determined for the Tongkeng ore in Guangxi, China. The determination of such indicator system is based on the multi-factors interaction, ambiguity, and randomness of the rock engineering system (RES). Based on the system engineering theory, a new improved RES-multidimensional cloud rock mass blastability classification model is established. The results show that the cloud-inference expert semi-quantitative (CESQ) can effectively obtain the expert experience and decrease the subjectivity of the traditional RES methods, and based on the improved fuzzy RES, the interaction factors among the factors and the systems affecting the blasting morphology of the rock mass are analyzed. Moreover, the importance of in situ testing of mechanical parameters of different rock masses is determined; finally, the five 6-D cloud models for the rock mass blastability grade are generated via the multidimensional cloud theory. The program is designed to support the engineering case analysis and is consistent with the results of the sample blasting experiment. A biased evaluation of the edge results is carried out, and this improved the actual usability of the evaluation model.

An improved path planning for mobile robots is proposed based on the hybrid multi-objective bare-bones particle swarm optimization with differential evolution. The mathematical model for robot path planning is firstly devised as a tri-objective optimization with three indices, i.e., the path length, the smoothness degree of a path, and the safety degree of a path. Then, a hybrid multi-objective bare bones particle swarm optimization is developed to generate feasible paths by combining infeasible paths blocked by obstacles with feasible paths via improved mutation strategies of differential evolution. In addition, a new Pareto domination with collision constraints is developed to select the personal best position of a particle according to the definition of the collision degree of a path. Simulation results confirm the effectiveness of our algorithm.

With the rapid growth of the automotive technology, electronically controlled air suspension has been widely used to improve ride comfort and handling stability of the vehicle by actively modulating the suspension stiffness, vehicle height, and posture. Ride height control (RHC) is the main function of the semi-active air suspension, and it is achieved by conducting air charging and discharging of the air spring, which plays a critical role in improving the vehicle dynamic performance. In addition, the unevenness distribution with payloads at the four wheels, the different dynamic characteristics of the front and rear air suspensions, and the undesired roll and pitch angles make adjustment effect of the vehicle ride height hard to be well addressed. Therefore, an effective control strategy is very essential, which is not only for RHC but also for keeping the vehicle posture, as well as the vehicle dynamic performance. Considering that the full car dynamics is a highly nonlinear model, this paper proposes a novel nonlinear model predictive controller to handle the multi-objective control requirement of a full car system. In order to evaluate the performance of the proposed controller, comparisons among the proposed control algorithm, the existing proportional- integral-derivative method, and the sliding-mode controller are carried out by a numerical analysis. The numerical results show that the proposed method excels the other methods and is effective in the adjustment of the vehicle ride height.

A hybrid wavelength-division-multiplexing (WDM) lightwave transport system for millimeter-wave (MMW)/microwave (MW)/baseband (BB) signals that are transmission based on fiber-wireless and fiber-visible laser light communication (VLLC) convergences is proposed and demonstrated. A broadband light source with an optical signal-to-noise ratio enhancement scheme in a hybrid lightwave transport system is employed. Light is optically promoted from a 5-Gbps/15-GHz RF data signal to 5-Gbps/60-GHz MMW and 5-Gbps/30-GHz MW data signals in fiber-wireless convergence. Light is also optically demoted from a 5-Gbps/15-GHz RF data signal to a 5-Gbps data stream in fiber-VLLC convergence. Over a 40-km single-mode fiber and a 4-m RF wireless/a 10-m free-space VLLC transmission, bit error rate performs impressively for 60-GHz MMW, 30-GHz MW, and 5-Gbps BB signal transmission. Such a hybrid WDM lightwave transport system could have practical applications for fiber-wireless and fiber-VLLC convergences to provide broadband integrated services.

Synaptic electronics based on three-terminal transistors can become a promising technology in the neuromorphic field. However, most synaptic transistors are composed of layer-by-layer structures and thin films. Due to the large power consumption and operating voltage, it is difficult to meet the current pursuit of low energy consumption devices. Herein, the artificial synapse behavior is demonstrated through a lithium-based electrolyte-gated transistor with a three-dimensional (3D) interface conduction channel for low-power synapse simulation. The device fabricated by the electrospinning process successfully simulates typical artificial synaptic characteristics under different stimulation intensities. The energy consumption of a single spike of a synaptic device can be as low as $\sim 1.25$ pJ. In addition, the device simulates behavior similar to the Ebbinghaus memory. This work provides a simple and effective reference for the fabrication of low-voltage artificial synaptic devices. [ABSTRACT FROM AUTHOR]

By adopting atomic layer deposition (ALD) sidewall passivation, the electroluminescence (EL) and communication performances of mini-light emitting diodes (mini-LEDs) of different sizes were improved. In particular, the most significant improvement in the electroluminescence properties of the external quantum efficiency (EQE) (a 7.2% increase), leakage current, and the communication properties of the modulation bandwidth (a 20% increase) transmission rate and bit error rate (BER) was found in the smallest mini-LEDs (80 $\mu \text{m}\,\,\times $ 120 $\mu \text{m}$). According to the results of time-resolved photoluminescence (TRPL) measurements, the carrier lifetime of the samples can be affected by both the size and ALD sidewall passivation. In addition, the effects of ALD sidewall passivation for visible-light communication (VLC) were demonstrated. [ABSTRACT FROM AUTHOR]