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Generative Pre-trained Transformers (GPT) are powerful language models that have great potential to transform biomedical research. However, they are known to suffer from artificial hallucinations and provide false answers that are seemingly correct in some situations. We developed GeneTuring, a comprehensive QA database with 600 questions in genomics, and manually scored 10,800 answers returned by six GPT models, including GPT-3, ChatGPT, and New Bing. New Bing has the best overall performance and significantly reduces the level of AI hallucination compared to other models, thanks to its ability to recognize its incapacity in answering questions. We argue that improving incapacity awareness is equally important as improving model accuracy to address AI hallucination.

Additional file 1: Supplementary Figure 1. fold change of different signal strengths in simulation study 1. x-axis shows the signal strengths. y-axis shows the absolute value of log2 fold changes between sample groups being compared with.

Additional file 3: Supplementary Figure 3. proportion changes in library size for simulation study 1. For each sample, proportion of changes in library size before and after the simulation signals are injected.

Additional file 2: Supplementary Figure 2. . results of three scenarios for simulation study 2. The first column shows statistical power at 0.05 nominal FDR. The second column shows AUC. The third column shows the real FDR at 0.5 nominal FDR. A Results for group-true-exp scenario. B Results for true-combo-dep-exp scenario. C Results for true-exp scenario.

Additional file 1: Supplemental Figure 1. Consistency of peaks among replicates. The graph shows the number of peaks identified in one or more samples. Due to the large number of accessible regions identified in only one sample, we included only regions that were identified in at least 50% of the samples within the study group.

Background The rapid development of single-cell RNA-sequencing (scRNA-seq) technologies has led to the emergence of many methods for removing systematic technical noises, including imputation methods, which aim to address the increased sparsity observed in single-cell data. Although many imputation methods have been developed, there is no consensus on how methods compare to each other. Results Here, we perform a systematic evaluation of 18 scRNA-seq imputation methods to assess their accuracy and usability. We benchmark these methods in terms of the similarity between imputed cell profiles and bulk samples and whether these methods recover relevant biological signals or introduce spurious noise in downstream differential expression, unsupervised clustering, and pseudotemporal trajectory analyses, as well as their computational run time, memory usage, and scalability. Methods are evaluated using data from both cell lines and tissues and from both plate- and droplet-based single-cell platforms. Conclusions We found that the majority of scRNA-seq imputation methods outperformed no imputation in recovering gene expression observed in bulk RNA-seq. However, the majority of the methods did not improve performance in downstream analyses compared to no imputation, in particular for clustering and trajectory analysis, and thus should be used with caution. In addition, we found substantial variability in the performance of the methods within each evaluation aspect. Overall, MAGIC, kNN-smoothing, and SAVER were found to outperform the other methods most consistently.

This paper introduces a variable structure to regulate speed of PMSM system by a global fast terminal sliding mode control (GFTSMC) technique optimizing speed tracking performance of permanent magnet synchronous motor (PMSM) servo system. Firstly, a global fast terminal sliding mode control strategy is introduced. Compared with common sliding mode control strategy, this strategy eliminates the chattering phenomenon in that it does not contain switching terms. Also, it can make the output speed reach the reference value in finite time, along with a shorter convergence time and a better tracking precision. Due to the complexity of control equations, S-function in MATLAB is adopted to design the GFTSMC. The effectiveness and robustness of the proposed scheme are verified by MATLAB simulations.

Aiming at the inaccurate result of traditional identification algorithm and the variation of motor parameters a new algorithm based on time varying forgetting factor multi-innovation stochastic gradient is proposed. Based on the voltage equation of permanent magnet synchronous motor system a discrete identification model is constructed. The vector control method is used to control the motor the input and output data of the identification model is obtained and the rotor resistance and inductance parameters are identified. Simulation results show that the algorithm can accurately identify the parameters of permanent magnet synchronous motor based on the new random gradient algorithm with variable forgetting factor.

This paper considers the parameter estimation problem for Hammerstein multi-input multioutput finite impulse response (FIR-MA) systems. Filtered by the noise transfer function, the FIR-MA model is transformed into a controlled autoregressive model. The key-term variable separation principle is used to derive a data filtering based recursive least squares algorithm. The numerical examples confirm that the proposed algorithm can estimate parameters more accurately and has a higher computational efficiency compared with the recursive least squares algorithm.

Finite-time stability has more practical application values than the classical Lyapunov asymptotic stability over a fixed finite-time interval. The problems of finite-time stability and finite-time boundedness for a class of continuous switched descriptor systems are considered in this paper. Based on the average dwell time approach and the multiple Lyapunov functions technique, the concepts of finite-time stability and boundedness are extended to continuous switched descriptor systems. In addition, sufficient conditions for the existence of state feedback controllers in terms of linear matrix inequalities (LMIs) are obtained with arbitrary switching rules, which guarantee that the switched descriptor system is finite-time stable and finite-time bounded, respectively. Finally, two numerical examples are presented to illustrate the reasonableness and effectiveness of the proposed results.

Traditionally, the Monte Carlo criticality calculation must set a maximum inactive step by experience to ensure that a fission source distribution has converged. The tallying process can only be invoked after this maximum inactive step to avoid the system error caused by the non-converged fission source distribution. In the same way, the uniform fission site algorithm for increasing the whole efficiency of global tallying should also be invoked after the fission source distribution has converged fully. The calculation must reach a maximum iteration step, then, this process can be stopped and the tallies can be printed. This old strategy has two defects. Firstly, the appointed maximum inactive step can only be set by experience, which will be insufficient in some cases; secondly, some iteration steps can be wasted because the precision of tallies has been enough and no one knows it. So, a new strategy is proposed in this article to overcome these defects. Based on an on-the-fly diagnostic method for the convergence of Shannon entropy sequence corresponding to the fission source distribution of each iteration step, the uniform fission site algorithm will be invoked after the iteration step whose serial number is the maximum of the first active step and the first converged step diagnosed by the above-mentioned rule. This rule will be helpful in ensuring that the uniform fission site algorithm can use enough accurate data to bias the secondary fission neutron number, thus avoiding the system error to some degree. Then, a global precision index will be calculated at each fixed step to judge whether the precision standard is reached. If so, the whole calculation is stopped. This process will be repeated until the pre-set maximum step number is reached. In this way, superfluous calculations can be skipped but the calculation precision can be guaranteed. In a word, this new strategy is beneficial to increasing the efficiency of global tallying in the Monte Carlo criticality calculation when appropriate parameters are adopted. This conclusion can be proved by the numerical result from the C5G7 benchmark model.

A new type of laser fusion indirect drive octahedral spherical hohlraum has been built up by Chinese researchers in recent years. The hohlraum with 6 laser entrance holes (LEHs) has superiority over other hohlraum configurations in both robust inherent high symmetry and high coupling energy efficiency from laser to hotspot for inertial confinement fusion study. Recently, an experimental investigation on radiation emission from the spherical hohlraum with two LEHs has been performed on the SGⅢ laser facility. In this experiment, 32 laser beams (24 beams from the top, 8 beams from the bottom) are injected into the hohlraum within 3 ns, and the total laser energy is 86.4 kJ. The hohlraum radius is 1.8 mm, and the radius of laser entrance hole is 0.6 mm. The experiments are conducted under two conditions:one is that a 0.48-radius capsule is located at the center of the hohlraum, and the other is that nothing is located in the hohlraum. Some flat response X-ray detectors (FXRDs) are installed at different angles on the target wall to collect the radiation energy. We carry out three-dimensional (3D) simulations of the experiment by using our 3D radiation implicit Monte Carlo code IMC3D. This code was developed in recent years based on fleck and Cumming's ideas. The hydrodynamics is not taken into consideration in the simulations, so we deduct 30% laser energy lost to hohlraum wall movements and back scattered by laser plasma instabilities. Based on the approximation, the simulation results are reasonable in principle. As a result, the radiation temperature of the hohlraum with capsule is 230 eV, and the radiation temperature of the hohlraum without capsule is 238 eV. At the end of laser injection, the capsule reflection ratio is 0.83. Compared with the experimental data, most of the simulation data agree well with the detector observations, except the data at 0 angle. The possible reasons for the difference are analyzed. The flux at 0 angle is more sensitive to the wall plasma movements than at the other angles. So if we ignore this phenomenon, then the witch will occur both in experiment and in simulation, yielding obvious differences for those quantities which strongly relate to the hydrodynamics of wall plasma. Finally, the methods of eliminating the difference are proposed and the prospect of IMC3D is presented.

In this paper, a new modeling method of the variable speed variable pitch wind turbine is proposed. More specifically, the linear parameter varying model of wind turbines is optimized by utilizing the variable forgetting factor recursive least squares identification algorithm, which improves the accuracy of the online estimation of the aerodynamic parameters. As the power factor results in the nonlinear characteristics of the wind turbine, the nonlinear power factor is approximate to a polynomial function of the tip speed ratio and the pitch angle. Then the relationship between the aerodynamic parameters and the power factor can be deduced. Furthermore, the solving problem of aerodynamic parameters can be transformed into the identification of the power factor. It is depicted in the simulation results that the modeling method of wind turbines can be optimized from the proposed variable forgetting factor recursive least squares identification algorithm, and the controller obtained from the optimized model can improve stability.

Particle swarm optimization (PSO) is a relatively new optimization algorithm that has been applied to a variety of problems. However, it may easily get trapped into local optima when solving complex multimodal problems. To address this concerning issue, a novel approach, namely hybrid particle swarm optimization and gravitational search algorithm (GSA) method by introducing GSA into PSO (HPSO-GSA), is proposed in this paper for global numerical optimization. The proposed algorithm incorporates both the different concepts from PSO and GSA, updating particle positions offered by both PSO algorithm and GSA tool. The hybrid approach makes full use of the fast convergence capability of PSO and the exploitation ability of GSA. To efficiently decrease the computational cost in the hybrid algorithm, GSA is introduced when adaptive evolution stagnation cycle is met. HPSO-GSA is tested on a commonly used set of benchmark functions and is compared to other algorithms presented in the literature. Experimental results show that HPSO-GSA obtains better performance on the tested functions.

In this paper, we proposed a novel fault tolerant control strategy based on control-law reconfiguration for the sensor failure of the nonlinear system. The Takagi-Sugeno (T-S) fuzzy model is adopted for modeling the nonlinear system and establishing fuzzy state feedback controller. When one of the sensors is failed, control action of the failure circuit is averagely shared by the remaining state feedback circuits, thus state feedback control-law is reconfigured. As a typical nonlinear system, the wind energy conversion system (WECS) is taken as an example to illustrate the effectiveness of the proposed controller.

For the Monte Carlo simulation of the non-static transport problem, there must be many calculation steps. Because some particles cannot finish their transport in the last step, they are naturally used as the source particles of the present step. These particles are called undied particles. It is difficult to adjust the history number of each step to obtain higher efficiency because the adjusting rule is hard to find. The most direct method is to set a large enough history number for all steps. But evidently, it is unnecessary for some steps. Among all possible rules, one candidate of adjusting the history number is to check the convergence situation of Shannon entropy (corresponding to the distribution of some undied particle attributes) every some samples in each step to determine whether or not to simulate more particles. So, this method needs to calculate the Shannon entropy frequently. Because the classical method of calculating Shannon entropy in message passing parallel programming environment must reduce massive data, it is unpractical to be used in this situation for the great increasing of computation time with the high frequency of entropy calculation. In this paper, we propose an efficient method of calculating the entropy in the message passing parallel programming environment by letting each process calculate its entropy value based on the local data in each processer and calculating the final entropy by averaging all the entropy values gotten by all processes. The entropy value calculated by this method is not the same as that by the classical method when using finite history number, but the difference goes to zero when the history number goes to infinity. The most remarkable advantage of this method is the small increasing of computation time when calculating the entropy frequently. It is a suitable method of calculating Shannon entropy when adjusting the history number automatically based on the judgment of the convergence situation of Shannon entropy.

This paper investigates the stability analysis and the design of optimal guaranteed cost control law for a class of Takagi-Sugeno (T-S) fuzzy systems. Based on the structure information of the premise rule base, a novel Lyapunov function called piecewise fuzzy Lyapunov function (PFLF) approach is developed by combining the fuzzy Lyapunov function (FLF) approach with the piecewise quadratic Lyapunov function (PQLF) approach. A new parallel distributed compensation (PDC) controller is also presented. Via the PFLF approach presented, the stability analysis and the design way of optimal guaranteed cost control law are derived in the form of linear inequality matrix (LMI). The designed method in this paper inherits the advantage of FLF and PQLF and ensures the resulting system is asymptotically stable while an upper bound of the closed-loop cost function is minimized. Two numerical examples are supplied to demonstrate the effectiveness of the designed control law.

Structure of variable speed constant frequency double fed wind power generation system (WPGS) was analyzed, and its model was established. Maximum power point tracking (MPPT) control, constant power control and vector control for WPGS were discussed. System operating performances were studied by simulation under sub-synchronous mode, synchronous mode and super-synchronization mode, unsymmetrical and symmetrical voltage fault. The result supplies evidences for improving the control performances of variable speed constant frequency double fed WPGS.

Considering wind power generation system with variable-speed squirrel-cage induction generators (SGIG), a Model Reference Fuzzy Adaptive (MRFA) controller was proposed for harvesting the maximum power from wind, The designed MRFA controller was established by embedding fuzzy controller into model reference adaptive control framework, substituting complicated traditional adaptive rules with the fuzzy inverse model. The simplified control model was applied to the Wind Energy Conversion System (WECS) and the simulation was well done. The simulation results show that the proposed controller has the advantages of shortening the time for system adjustment and quickening the response speed, higher anti -interference ability and adaptability to parameter varation than conventional PID controllers.

Based on the mathematical model of the SCIG wind power generation system, a model of the system for maximum energy capture was established using MPPT in VisSim. In the double loop of control system, a PI speed controller is adopted in the speed loop, and a vector controller is used in the current loop. The validity is testified by the simulation results, and other control strategies can be applied to the system to testify their rationality, and the VisSim supplies a new method for the simulation of SCIG wind power generation system.

To improve the wind speed forecasting accuracy, a hybrid algorithm integrating time series analysis with divided difference filter (DDF) is proposed. First, by use of time series analysis theory, the non-stationary modeling for wind speed signals of wind farm is proceeded to obtain the model equation and the wind speed forecasted by the simplex time series model equation. Second, by means of the obtained model equation, the state equation and observational equation are deduced, and the wind speed is forecasted respectively by KF and DDF forecasting recurrence equation Finally, the forecasting experiments for varying wind speed measured in a certain wind farm at JiShan is conducted to validate the proposed hybrid algorithm. Experimental results show that by using this hybrid algorithm the forecasting accuracy of wind speed can be improved and DDF is a effective in wind speed forecasting.

In a three-dimensional (3-D) model-based objects tracking and recognition system, the key problem of objects location is to establish the relationship between 2-D objects image and 3-D model. Based on 3-D model projection and 2-D image feature extraction, a modified Hausdorff distance is used to establish the matching function. The relationship between matching parameters are described with a probability model, and the distribution of parameter evolves towards the direction of dominant character through probability model learning and the corresponding operation, which is proposed to solve the problem of overmany iteration and slow constringency velocity The experiments show that the optimal matching parameters between 3-D model and 2-D image feature can be found accurately and efficiently, and then the accurate object location is completed.

For the conservatism and difficulty of checking the stability of T-S fuzzy system with the common Lyapunov function approach, piecewise Lyapunov function and fuzzy Lyapunov function are proposed by using the fuzzy premise rules of the T-S system and membership functions respectively; the problem of robust control of a typical class of T-S fuzzy systems is studied by defining fuzzy Lyapunov function in each overlapping group; and The method of parametric design of output tracking controller is given in the form of LMI. And, the output tracking controller meets the disturbance attenuation gamma, the controller can guarantee that the closed-loop system is stable. Finally, The matrix shows the feasibility of the method.

This paper deals with the stability analysis and the design of Hinfin control law for a class of discrete Takagi-Sugeno (T-S) fuzzy systems with time delays. Based on the nonquadratic Lyapunov function constructed here, the stability analysis and the design way of Hinfin control law are derived in the form of linear matrix inequality (LMI) via a nonparallel distributed compensation (non-PDC) scheme. The new conclusion under a special situation is also suitable for a PDC law. Two numerical examples are supplied to demonstrate the effectiveness of the designed control law. And both theoretical analysis and numerical examples illustrate that such novel sufficient conditions are less conservative than previous results obtained within the quadratic framework.

Based on the non-linear characteristic of BUCK-BOOST converter, combining with the fine part-linear characteristic of T-S fuzzy model, this paper constructs the T-S fuzzy model of the BUCK-BOOST converter under the kind of pattern which is DCM. Adopting PDC control scheme, it designs the BUCK-BOOST converterpsilas controller via CQLF method and obtains the controller parameters by using LMI method. Simulation results show that, T-S fuzzy model which based on BUCK-BOOST converter is reliable, and the fuzzy controller via T-S fuzzy model has better effects of stability and immunity to disturbance by comparing with PI control.

Quantization index module is a normal type of information hiding scheme, but the Steganalysis methods are not accuracy enough to detect them. A novel steganalysis method is proposed on the basis of center limitation theorem. One concise statistics is constructed to detect this kind of steganagraphy algorithms. A DCT-based image steganographic method which is recently advanced as resisting statistical attack is studied and the experimental results indicate that this technology is applicable for the detection of the quantization index module embedding algorithm.

Permanent magnet synchronous motors (PMSM) are widely used in industrial drives due to their simple structure, high efficiency and high factor. Variable speed system of PMSM based on vector has owed an important position where has seriously requirement for celerity and precision. Firstly, the controlling characteristics and working principles of PMSM are analyzed, and the PMSM precision mathematical models are established based on the dq reference coordinates by strict analysis and calculation. The fuzzy model reference adaptive control method uses the fuzzy basis function expansion to represent the unknown parameters and change the identification problem from identifying the original unknown parameters to identifying the coefficients of fuzzy basis function expansion. The stability and convergence properties of the controller are ensured. The results of the simulation based on Matlab and dSPACE experiment show the fuzzy adaptive speed controller has high control and parameter track performance.

Based on existent results on control network performance and scheduling in protocol layer, the paper analyzes the scheduling algorithm of networked control systems in application layer, and structure multi-task networked control systems for scheduling simulation in virtual platform. In addition, this paper explores technologies for integration of control and scheduling design. The influence of sampling period to NCS is discussed. Based on control and network scheduling, an optimized model and sampling period selection algorithm are proposed. An example shows that the algorithm satisfies performance requirement for NCS, optimizes network scheduling, and improves network efficiency.

By combining wavelet packet with neural network in human iris recognition, an neural network ensemble was constructed to iris classification. Iris image texture features are acquired by using wavelet packet decomposition, then through the new constructive RBF neuron networks, the training for texture classification problem of neural networks is transformed into the "including" problem of a points. A combination method of wavelet packet and neural network in pattern recognition is given. The method of pattern recognition based on combining multiple classifiers not only can reduce the long training time and learning complexity of traditional neural networks, but also can improve veracity and robustness ability in pattern recognition. At the same time, the problem of harding to determine the number of hidden note is resolved in neural network, and the optimization of the neural network is also considered.

Considering a general kinematical case of mobile robot, with an offset from intersection of rear wheel axis and symmetry axis, new error dynamics are derived. Using cascaded system approach, a tracking controller is constructed. The ? - exponential convergence of tracking error dynamics, in combination of closed - loop controller, is guaranteed by means of cascaded system theory. Additionally, extending the result into dynamics model by adaptive approach. Simulation study validate the effectiveness and feasibility of the proposed controller both in kinematics and dynamics models.

To adjust the power of serial-resonant inverters for induction heating applications, asymmetrical Pulse Density Modulation (APDM) power controlled strategy is described, analysed and designed. The Pulse Density Modulation (PDM) is introduced to overcome the drawbacks of the classical control modes such as Pulse Width Modulation, Pulse Frequency Modulation and Phase Shift Modulation. One characteristic of PDM is that it operates at resonant frequency with Zero-current and zero-voltage switching for a wide range of output power variations. Normal PDM often considered as symmetry pattern for the power switches. While in some case of low quality of the resonant loads, the load current is incontinuous in stage of low density, the phase loop locked circuit can not work efficiently, and the advantage of PDM will disappear. APDM provides two times wide range of output power variations at the same operation period of PDM, and the switches also cut off at Zero Current Switching and open at Zero Voltage Switching.

The problem of uniform finite-time stability for a class of discrete-time switched descriptor systems is considered. Firstly, the concept of finite-time stability for continuous descriptor systems is extended to discrete-time switched descriptor systems. Secondly, based on the Lyapunov-like function method, and under arbitrary switching signal, sufficient conditions under which discrete-time switched descriptor systems are regular and causal, the uniform finite-time is bounded and uniform finite-time is stable, are derived. Then, the state feedback controllers are designed to guarantee the discrete-time switched descriptor system uniform finite-time stablility. Finally, some numerical examples show that the results obtained in this paper are effective.