Ultrafast coherent control of a hole spin qubit in a germanium quantum dot

Operation speed and coherence time are two core measures for the viability of a qubit. Strong spin-orbit interaction (SOI) and relatively weak hyperfine interaction make holes in germanium (Ge) intriguing candidates for spin qubits with rapid, all-electrical coherent control. Here we report ultrafast single-spin manipulation in a hole-based double quantum dot in a germanium hut wire (GHW). Mediated by the strong SOI, a Rabi frequency exceeding 540 MHz is observed at a magnetic field of 100 mT, setting a record for ultrafast spin qubit control in semiconductor systems. We demonstrate that the strong SOI of heavy holes (HHs) in our GHW, characterized by a very short spin-orbit length of 1.5 nm, enables the rapid gate operations we accomplish. Our results demonstrate the potential of ultrafast coherent control of hole spin qubits to meet the requirement of DiVincenzo’s criteria for a scalable quantum information processor.

High-performance photodetector based on an interface engineering-assisted graphene/silicon Schottky junction

Graphene/silicon Schottky junctions have been proven efficient for photodetection, but the existing high dark current seriously restricts applications such as weak signal detection. In this paper, a thin layer of gadolinium iron garnet (Gd3Fe5O12, GdIG) film is introduced to engineer the interface of a graphene/silicon Schottky photodetector. The novel structure shows a significant decrease in dark current by 54 times at a −2 V bias. It also exhibits high performance in a self-powered mode in terms of an Ilight/Idark ratio up to 8.2 × 106 and a specific detectivity of 1.35 × 1013 Jones at 633 nm, showing appealing potential for weak-light detection. Practical suitability characterizations reveal a broadband absorption covering ultraviolet to near-infrared light and a large linear response with a wide range of light intensities. The device holds an operation speed of 0.15 ms, a stable response for 500 continuous working cycles, and long-term environmental stability after several months. Theoretical analysis shows that the interlayer increases the barrier height and passivates the contact surface so that the dark current is suppressed. This work demonstrates the good capacity of GdIG thin films as interlayer materials and provides a new solution for high-performance photodetectors.

Development of a Depth Control System Based on Variable-Gain Single-Neuron PID for Rotary Burying of Stubbles

Rotary burying by tractor-hitched rotary tillers is a common practice in southern China for treating rice stubbles. Currently, it is difficult to maintain stable tillage depths due to surface unevenness and the residual stubbles in the field, which leads to unstable tillage quality and nonuniform crop growth in later stages. In this study, an RTK-GNSS was used to measure the real-time height and roll angle of the tractor, and a variable-gain single-neuron PID control algorithm was designed to adjust the coefficients (KP, KI, and KD) and gain K in real-time according to the control effects. An on-board computer sent the angles of the upper swing arm u(t) to an STM32 microcontroller through a CAN bus. Compared with the current angle of the upper swing arm, the microcontroller controlled an electronic-control proportional hydraulic system, so that the height of the rotary tiller could be adjusted to follow the field undulations in real-time. Field experiments showed that when the operation speed of the tractor-rotary tiller system was about 0.61 m/s, the variable-gain single-neuron PID algorithm could effectively improve the stability of the working depth and the stubbles’ burying rate. Compared with a conventional PID controller, the stability coefficient and the stubbles’ burying rate were improved by 5.85% and 4.38%, respectively, and compared with a single-neuron PID controller, the stability coefficient and the stubbles’ burying rate were improved by 4.37% and 3.49%, respectively. This work controlled the working depth of the rotary tiller following the changes in the field surface in real-time and improved the stubbles’ burying rate, which is suitable for the unmanned operation of the rotary burying of stubbles in the future.

A Real-Time Matrix Iterative Optimization Algorithm of Booking Elevator Group and Numerical Simulation Formed by Multi-Sensor Combination

Elevators are an essential indoor transportation tool in high-rise buildings. The world is advocating the design concept of safety, energy-saving, and intelligence. We focus on improving operation speed and utilization efficiency of the elevator group. This paper proposed a real-time reservation elevator groups optimization algorithm, and a dynamic matrix iterative model has been established. The indoor navigation technology UWB is applied, which can help users to quickly find elevators. The manned equilibrium efficiency and running time equilibrium efficiency of elevator group are given. Moreover, the data filtering criterion formulas for user waiting time and elevator remaining space are defined. In this paper, three numerical examples are given. Example 1 is a single elevator in n-storey building. Example 2 is compared with different scheduling algorithms, such as FCFS, SSTF, LOOK, and SCAN algorithms, and the results show that our method has the advantages of short total running time and less round-trip frequency. At last, the matrix of numerical iteration results are visualized, and the data movement status of people on each floor can be observed. Example 3 introduced elevator group algorithms. For high-rise buildings, this paper adopts a high, medium, and low hierarchical management model; this model has high coordination, as well as fast response, batch process, and adaptive function. Finally, we also discussed and compared the complexity of single elevator and elevator group algorithms. Therefore, this method has great development potential and practical application value, which deserves further study.

OPTIMAL CONSTRUCTION OF THE PATTERN MATRIX FOR PROBABILISTIC NEURAL NETWORKS IN TECHNICAL DIAGNOSTICS BASED ON EXPERT ESTIMATIONS

In the field of technical diagnostics, many tasks are solved by using automated classification. For this, such classifiers like probabilistic neural networks fit best owing to their simplicity. To obtain a probabilistic neural network pattern matrix for technical diagnostics, expert estimations or measurements are commonly involved. The pattern matrix can be deduced straightforwardly by just averaging over those estimations. However, averages are not always the best way to process expert estimations. The goal is to suggest a method of optimally deducing the pattern matrix for technical diagnostics based on expert estimations. The main criterion of the optimality is maximization of the performance, in which the subcriterion of maximization of the operation speed is included. First of all, the maximal width of the pattern matrix is determined. The width does not exceed the number of experts. Then, for every state of an object, the expert estimations are clustered. The clustering can be done by using the k-means method or similar. The centroids of these clusters successively form the pattern matrix. The optimal number of clusters determines the probabilistic neural network optimality by its performance maximization. In general, most results of the error rate percentage of probabilistic neural networks appear to be near-exponentially decreasing as the number of clustered expert estimations is increased. Therefore, if the optimal number of clusters defines a too “wide” pattern matrix whose operation speed is intolerably slow, the performance maximization implies a tradeoff between the error rate percentage minimum and maximally tolerable slowness in the probabilistic neural network operation speed. The optimal number of clusters is found at an asymptotically minimal error rate percentage, or at an acceptable error rate percentage which corresponds to maximally tolerable slowness in operation speed. The optimality is practically referred to the simultaneous acceptability of error rate and operation speed.

On seakeeping capabilities evaluation of a large off-shore barge

Usual specially designed barges are involved in the off-shore operations that have to be evaluated by several criteria, inclusive of the seakeeping capabilities. The paper includes a comparative seakeeping study of two constructive versions for a large off-shore barge with a length of 189 m, having different breadths 40 m and 50 m. Both constructive versions are on the full cargo 23000 t condition. The seakeeping analyses are done with our own software DYN-OSC, developed by linear potential Lewis’s strip theory. The seakeeping studies are done in oblique irregular waves with a maximum height of 12 m and for the off-shore barge maximum operation speed of 7 knots. The results of this comparative study reveal the differences in the seakeeping operation capabilities for the two off-shore barge constructive versions.

Hardware in the Loop Real-time Simulation of Doubly Fed Off-grid Wind Power System

Hardware in the Loop (HIL) semi-physical real-time simulation can shorten the research period and complete the harsh working condition test, which is difficult to be carried out on the physical platform. Taking the off-grid Doubly Fed Induction Generator (DFIG) wind power system as the research object, this paper proposes the bottom modelling method of HIL real-time simulation. Using the Hardware Description Language VERILOG, the bottom real-time models of DFIG, converter and load are designed on Field Programmable Gate Array (FPGA), connected with the real controller, and the HIL real-time simulation platform is constructed. The experiments of conventional working conditions and unbalance load are carried out on the HIL platform and the physical platform. The operation speed of the HIL platform reaches 0.48μs. Compared with the physical platform, the error of HIL platform is between 1.17 ~ 3.29% under various working conditions.

Development of an NCAM motorized GRAIN GRUEL (Soy Bean) sieving machine

A survey carried out on conventional or traditional method of sieving wet grains showed that the method consumes time, energy, has low output and efficiency and is also hazardous to health due to low level of hygiene involved. A motorized grain gruel machine was developed at the National Centre for Agricultural Mechanization (NCAM), Ilorin Nigeria. The machine consists of four major units which are hopper, seiver, separator and the discharge unit. The machine’s performance was evaluated at different water volumes (16.5, 18.5 and 20.5 liters) and at three different operating speeds namely 65.3 rpm, 115.0 rpm and 126.0 rpm. The parameters evaluated are; sieving efficiency, through put and output capacity. The data were analyzed using the New Duncan multiple range test (Post Hoc Test) method and linear regression model was used to establish the relationship between the independent variables with the dependent variable, which was optimized using pulp in python version 2.9 frame work. Results show that the machine performed higher at 20.5litres and 126 rpm to a maximum sieving efficiency of 85.08% and 86.48% respectively, but was optimized to the range of 88.35-92.5% in sieving efficiency and 19.42-23.42kg/hr in output capacity. Also the analysis showed that water volume used had more significant effect on the efficiency and other variables such as the throughput and the output capacity respectively, than the operation speed. It was concluded from the results that the machine saved time and energy, reduced material wastage and hazards and therefore is more efficient than the conventional method. It is recommended that further evaluations should be carried out on the machine using a wider variety and species of grains samples.

Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response

Data telemetry is a critical element of successful unconventional well drilling operations, involving the transmission of information about the well-surrounding geology to the surface in real-time to serve as the basis for geosteering and well planning. However, the data extraction and code recovery (demodulation) process can be a complicated system due to the non-linear and time-varying characteristics of high amplitude surface noise. In this work, a novel model fuzzy wavelet neural network (FWNN) that combines the advantages of the sigmoidal logistic function, fuzzy logic, a neural network, and wavelet transform was established for the prediction of the transmitted signal code from borehole to surface with effluent quality. Moreover, the complete workflow involved the pre-processing of the dataset via an adaptive processing technique before training the network and a logistic response algorithm for acquiring the optimal parameters for the prediction of signal codes. A data reduction and subtractive scheme are employed as a pre-processing technique to better characterize the signals as eight attributes and, ultimately, reduce the computation cost. Furthermore, the frequency-time characteristics of the predicted signal are controlled by selecting an appropriate number of wavelet bases “N” and the pre-selected range for pij3 to be used prior to the training of the FWNN system. The results, leading to the prediction of the BPSK characteristics, indicate that the pre-selection of the N value and pij3 range provides a significantly accurate prediction. We validate its prediction on both synthetic and pseudo-synthetic datasets. The results indicated that the fuzzy wavelet neural network with logistic response had a high operation speed and good quality prediction, and the correspondingly trained model was more advantageous than the traditional backward propagation network in prediction accuracy. The proposed model can be used for analyzing signals with a signal-to-noise ratio lower than 1 dB effectively, which plays an important role in the electromagnetic telemetry system.

Application of SAS Algorithm in Responsive Submarine-searching Path Planning

Whether the planned path is optimal or not is an important condition for the completion of the responsive submarine-searching task, which depends on the applicability of the planning algorithm. According to the requirements of responsive submarine-searching mission of the anti-submarine aircraft, SAS algorithm used in responsive submarine-searching path planning is analysed in this paper, the relevant constraints are given, and the simulation calculation is carried out by using SAS algorithm. The results show that SAS algorithm has excellent performance in operation speed and planning effect, and it can better meet the training requirements.