Standard Dual Quaternion Optimization and Its Applications in Hand-Eye Calibration and SLAM

Several common dual quaternion functions,such as the power function,the magnitude function,the 2-norm function,and the kth largest eigenvalue of a dual quaternion Hermitian matrix,are standard dual quaternion functions,i.e.,the standard parts of their function values depend upon only the standard parts of their dual quaternion variables.Furthermore,the sum,product,minimum,maximum,and composite functions of two standard dual functions,the logarithm and the exponential of standard unit dual quaternion functions,are still standard dual quaternion functions.On the other hand,the dual quaternion optimization problem,where objective and constraint function values are dual numbers but variables are dual quaternions,naturally arises from applications.We show that to solve an equality constrained dual quaternion optimization(EQDQO)problem,we only need to solve two quaternion optimization problems.If the involved dual quaternion functions are all standard,the optimization problem is called a standard dual quaternion optimization problem,and some better results hold.Then,we show that the dual quaternion optimization problems arising from the hand-eye calibration problem and the simultaneous localization and mapping(SLAM)problem are equality constrained standard dual quaternion optimization problems.

State Accurate Representation and Performance Prediction Algorithm Optimization for Industrial Equipment Based on Digital Twin

The combination of the Industrial Internet of Things(IIoT)and digital twin(DT)technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance.However,conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information.The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model.In this paper,a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state.Firstly,a global real-time feedback and the dynamic adjustment mechanism is established by combining DT-IIoT with algorithm optimization.Secondly,a strong screening dual-model optimization(SSDO)prediction method based on Stacking integration and fusion is proposed in the dynamic regulation mechanism.Lightweight screening and multi-round optimization are used to improve the prediction accuracy of the evolution model.Finally,tak-ing the boiler performance of a power plant in Shanxi as an example,the accurate representation and evolution prediction of boiler steam quantity is realized.The results show that the real-time state representation and life cycle performance prediction of large key equipment is optimized through these methods.The self-lifting ability of the Stacking integration and fusion-based SSDO prediction method is 15.85%on average,and the optimal self-lifting ability is 18.16%.The optimization model reduces the MSE loss from the initial 0.318 to the optimal 0.1074,and increases R2 from the initial 0.731 to the optimal 0.9092.The adaptability and reliability of the model are comprehensively improved,and better prediction and analysis results are achieved.This ensures the stable operation of core equipment,and is of great significance to comprehensively understanding the equipment status and performance.

Double Side Interfacial Optimization for Low-Temperature Stable CsPbI_(2)Br Perovskite Solar Cells with High Efficiency Beyond 16%

CsPbI_(2)Br perovskite solar cells have achieved rapid development owing to their exceptional optoelectronic properties and relatively outstanding stability.However,open-circuit voltage(Voc)loss caused by band mismatch and charge recombination between perovskite and charge transporting layer is one of the crucial obstacles to further improve the device performance.Here,we proposed a bilayer electron transport layer ZnO(bottom)/SnO_(2)(top)to reduce the Voc loss(Eloss)and promote device Voc by ZnO insert layer thickness modulation,which could improve the efficiency of charge carrier extraction/transfer and suppress the charge carrier recombination.In addition,guanidinium iodide top surface treatment is used to further reduce the trap density,stabilize the perovskite film and align the energy levels,which promotes the fill factor,short-circuit current density(Jsc),and stability of the device.As a result,the champion cell of double-side optimized CsPbI_(2)Br perovskite solar cells exhibits an extraordinary efficiency of 16.25%with the best Voc as high as 1.27 V and excellent thermal and storage stability.

ODFF: Optimized Dual Fuzzy Flow Controller Based Voltage Sag Compensation for SMES-Based DVR in Power Quality Applications

The booming electronics itself carries an impact on power quality. Superconducting Magnetic Energy Storage (SMES) is proposed to enhance power quality in three-phase systems under various loads. This paper aimed to compensate the voltage sags under various faults in the grid systems. The SMES is selected as an energy storage unit to improve the capability of voltage sag compensation. Optimized Dual Fuzzy Flow (ODFF) logic controller is designed to prevent the voltage sag time during excessive phase voltage variation. Hence the proposed controller strategy reduces the total harmonic distortion during various fault conditions. To regulate the contribution of active power, the least possible value is improved using ODFF. The depth of voltage sags compensation is achieved by the over modulation and an iterative loop is designed in the control block. While protecting sensitive loads from voltage disturbances, and sags initiated by the power system, the proposed configuration is advantageous for an industrial implementation. It is found that the proposed method can result in more than 50% additional sag support time when compared with the previous methods such as PI and PSO. Utilizing MATLAB Simulink, compensation of sag and minimization of THD is established, and the simulation tests are performed to evaluate the performance of the proposed control method.

A Novel Technique for Detecting Various Thyroid Diseases Using Deep Learning

Thyroid disease is a medical condition caused due to the excess release of thyroid hormone.It is released by the thyroid gland which is in front of the neck just below the larynx.Medical pictures such as X-rays and CT scans can,however,be used to diagnose it.In this proposed model,Deep Learning technology is used to detect thyroid diseases.A Convolution Neural Network(CNN)based modified ResNet architecture is employed to detectfive different types of thyroid diseases namely 1.Hypothyroid 2.Hyperthyroid 3.Thyroid cancer 4.Thyroiditis 5.Thyroid nodules.In the proposed work,the training method is enhanced using dual optimizers for better accuracy and results.Keras,a Python library that is high level runs as the main part of the Tensor Flow framework.It is used in the proposed work to implement deep learning techniques.The comparative analysis of the proposed model and the existing work helps to show that there is a great improvement in the performance metrics in classifying the type of thyroid disease.By applying Adam and SGD(Stochastic Gradient Descent)optimizers in the training phase of the proposed model it was identified that these increase the operational efficiency of the modified ResNet model.After retraining the model with SGD,the modified ResNet provides more accuracy of about 97%whereas the basic ResNet architecture attains 94%accuracy.A web-based frame-work is also developed which yields the type of thyroid disease as the output for a given input scanned image of the system.

Dual Interval Optimization Based Trading Strategy for ESCO in Day-ahead Market with Bilateral Contracts

Being capable of aggregating multiple energy resources, the energy service company(ESCO) has been regarded as a promising alternative for improving power system flexibility and facilitating the consumption of renewable resources in the electricity market. Considering the uncertain variables in day-ahead(DA) market trading, an ESCO can hardly determine their accurate probability distribution functions. Traditional interval optimization methods are used to process these uncertain variables without specific probability distribution functions.However, the lower and upper bounds of the intervals may change due to extreme weather conditions and other emergent events. Hence, a dual interval optimization based trading strategy(DIOTS) for ESCO in a DA market with bilateral contracts(BCs) is proposed. First, we transfer the dual interval optimization model into a simple model consisting of several interval optimization models. Then, a pessimistic preference ordering method is applied to solve the derived model. Case studies illustrating an actual test system corroborate the validity and the robustness of the proposed model, and also reveal that ECSO is critical in improving power system flexibility and facilitating the ability of absorbing renewable resources.

A Variant of the Dual Face Algorithm Using Gauss-Jordan Elimination for Linear Programming

Using Cholesky factorization, the dual face algorithm was described forsolving standard Linear programming (LP) problems, as it would not be very suitablefor sparse computations. To dodge this drawback, this paper presents a variant usingGauss-Jordan elimination for solving bounded-variable LP problems.