Compensation Operation Research Articles

Reducing Product Expiration by Aligning Salesforce Incentives: A Data‐driven Approach

Product expirations at retail stores erode profits and burden the environment. We investigate whether manufacturers can leverage salesforce compensation design as a waste‐reduction tool. We propose a data‐driven framework that uses structural econometric modeling and agency theory. Our analysis starts with a game theoretic model in which a salesperson interacts with a manufacturer that reimburses retailers and penalizes its own salesforce for expired products. We then use our model’s equilibrium outcome to estimate factors that determine the effort exerted by the sales representatives of a consumer‐packaged‐goods manufacturer. We first show theoretically that a manufacturer can simultaneously increase profits and reduce waste by increasing its sales representatives’ penalty for product expiration that occurs at the retailer. Based on this, we formulate a manufacturer’s profit maximization problem to compute optimum expiration penalties. Solving this problem for the manufacturer, we find the potential to simultaneously reduce waste and improve profits for 14% of the product–market combinations in our dataset. For these cases, we find that charging the salesperson 2.1 times the commission (on average) for each expired unit can increase the manufacturer’s profits by 0.58% and reduce waste by 23.3%. This study determines that a profit‐seeking manufacturer can reduce waste at retailers by optimizing its salesforce incentive structure. Our work bridges the salesforce compensation and sustainable operations literatures. Furthermore, our framework allows consumer‐packaged‐goods manufacturers to identify win‐win opportunities for the environment and their bottom line.

Q-Compensated Denoising of Seismic Data

It is widely known that strong noise can decrease the quality of seismic data. However, the anelastic attenuation could be more important to account for the weak amplitude and low quality of seismic data. Here, we develop an inversion framework to simultaneously compensate for the attenuation of seismic data and remove noise, thereby enhancing the quality of seismic data. Instead of directly applying a compensation operator to the input seismic data, we formulate an inverse problem that connects the sparse reflectivity model and the raw seismic data via the convolution and attenuation functions. The random noise is assumed to be the unpredicted part of the forward modeling process. We use the L2-norm regularization for the data misfit and impose a sparsity constraint onto the reflectivity series, e.g., using the L1-norm constraint. We use an iterative preconditioned conjugate gradient method to solve the L1-norm constrained least-squares optimization problem and obtain the reflectivity series. The denoised and compensated data are obtained by applying the convolution operator to the reflectivity. We use several synthetic and field seismic data to illustrate the effectiveness of the presented method.

A Fabry–Perot Interferometer-Based Fiber Optic Dynamic Displacement Sensor With an Analog in-Phase/Quadrature Generator

In this paper, the design details of a low-cost and high resolution Fiber Optic Dynamic Displacement sensor are discussed. The sensor is designed based on the principle of extrinsic Fiber Optic Fabry-Perot Interferometer (FPI). Higher sensitivity is achieved by using a double pass sensor head configuration. To resolve direction ambiguity, quadrature signals are generated by employing Phase Generated Carrier (PGC) demodulation technique. An Analog In-phase/Quadrature phase (I/Q) generator is designed to perform light intensity compensation, carrier mixing, and filtering operations to obtain the quadrature signals. Arctangent algorithm and phase unwrapping algorithms are used to obtain dynamic displacement from the quadrature signals. The sensor is demonstrated to measure non-contact dynamic displacement at a working distance of 5 cm with an amplitude resolution of 12 nm, a precision of 1.4 nm, and a standard deviation of 2%. The sensor is also able to measure multi-frequency dynamic displacements generated by piezo actuators. Potential applications of the sensor include condition monitoring and predictive maintenance in industries.

A Direct Current Control Scheme With Compensation Operation and Circuit-Parameter Estimation for Full-Bridge DC–DC Converter

To boost the dynamic performance of the full-bridge (FB) dc–dc converter, some model-based controls have been recently proposed in the literature. However, in terms of dynamic performance, these methods have failed to reveal the potential of this converter since the inherent relationship among the duty ratio, the input voltage, the output voltage, and the load resistor is not adopted in the controller, and the dynamic performances are still relayed on the fuzzy adjustment based on the disturbances of output voltage as the single-voltage-loop control method. In this article, based on this inherent characteristic of the FB dc–dc converter, an accurate transferred current modulation method is presented first. Based on this modulation method, a simple direct current control (DCC) scheme is proposed for a fast dynamic response with the feedback values of input voltage and load current. Moreover, the model uncertainties such as the power losses and control delay may influence the control performance. Therefore, a compensation operation is also presented to reduce the impact caused by these uncertainties to ensure the dynamic performance of the proposed strategy. In addition, since the circuit parameters including the leakage inductance and the output capacitor should be employed to implement the proposed DCC strategy with compensation operation, inaccurate circuit parameters may influence the performance of these proposed strategies. Therefore, the circuit-parameter estimation methods for these two circuit parameters are also proposed to ensure the dynamic performance of the FB dc–dc converter. Finally, the simulation and experiment results have verified the correctness and effectiveness of the proposed DCC strategy.

High-order numerical methods with mass and energy conservation for spin–orbit-coupled Bose–Einstein condensates

ABSTRACT In this work, a high-order numerical method with mass and energy conservation obtained by the operator-compensation (OC) technique is proposed for the coupled Gross–Pitaevskii equations (CGPEs) modelling the spin–orbit-coupled Bose–Einstein condensates. The OC technique is utilized to seek high-order approximations to the first- and the second-order spatial derivatives in the CGPEs, after which a time-dependent semi-discrete system is obtained. Then we obtain a fully discrete scheme by applying the Crank–Nicolson method to discretize this system in time. We theoretically show that the semi-discrete system and the fully discrete scheme conserve both mass and energy on the discrete level. In addition, the stability analysis is discussed for the proposed method. Numerical experiments are given to test accuracy and verify conservative properties. The dynamics of the two-dimensional CGPEs and the effects of the parameters in the CGPEs are simulated.

On Inherent Redundancy of MMC-Based STATCOMs in the Overmodulation Region

Modular multilevel converters (MMCs) are frequently featured to their modular structure, which results in fault-tolerant operation and easy redundancy realization. Nevertheless, in order to reach a given redundancy factor, more cells must be included in the converter, which directly affects the costs. This paper discusses the inherent redundancy of a modular multilevel converter (MMC) based static synchronous compensator (STATCOM) operating in the overmodulation region. Analytical expressions for the limits of the converter linear region were developed in order to define the minimum required dc-link voltage. Moreover, sensitivity analyses were implemented in order to show the effects of grid voltage variations, different output impedances, power factor and injected currents. The results indicated that the operation in overmodulation region has significant inherent redundancy. For a MMC based STATCOM with 26 cells, the converter can ride through 4 failures per arm without significantly increasing the output THD or reducing the injected current into the grid.

Constructing a method of multi­coordinate control over the static thyristor compensators with forced commutation

The configuration and the principle of operation of the static thyristor compensator of reactive power with the forced commutation and voltage addition for networks with a compensated neutral have been considered. The integrated indicators of the compensator energy process have been defined for the case when it is powered by a rectangular-shaped voltage in the case of independent control over the switching thyristors. At certain values of thyristor control angles, the specific losses of active power become less than the similar specific losses when the compensator is powered by a sinusoid voltage. This ensures its competitiveness relative to other static compensators.A method of multicoordinate control over the static thyristor compensators with forced commutation has been proposed. It implies independent control over all switching thyristors in a compensator in accordance with the objective function of the system, which is determined under condition that the specific losses of active power do not exceed their economically justified level.A circuit to control the static thyristor compensator with forced commutation and voltage addition has been suggested. The application of the circuit makes it possible to reduce active power losses in the compensator when controlling the reactive power and to execute independent control over the phase reactors. Real-time microprocessor control over all elements of the system makes it possible to enable the required algorithm for switching commutation thyristors and to implement multi-coordinate control over the compensator energy processes. An algorithm for operating the microprocessor system of the static compensator when controlling the reactive power has been constructed. The algorithm, due to an increase in the voltage addition coefficient during the action of a negative half-wave of supply voltage, makes it possible to reduce the specific losses of active power in the electrical network and compensator

Shadow Detection and Removal for Illumination Consistency on the Road

Shadow detection and removal is an important task for on-road visual perception. However, effectively detecting and removing the shadows on the road to maintain illumination consistency remain challenging. Road shadows blur and change road features, greatly damaging the image quality and making further detection and tracking more difficult to implement. For example, detection could be mistaken due to incorrect identification of shadow boundaries. The adaptability and accuracy can be affected as well. To tackle the problems, this paper employs Support Vector Machine (SVM) based on color saliency space and gradient field to detect shadow. Nonlinear SVM classifier analyzes its color saliency space and gradient information, then reconstructs road shadow descriptor to distinguish shadowed regions. To remove the shadow, adaptive variable scale regional compensation operator is adopted. Following experiments verify the detection and removal method are feasible in the real world, and robust to many types of road conditions.

A novel hybrid deterministic-statistical approach for the mid-frequency vibro-acoustic problems

It is difficult to predict precisely the frequency response of a complex vibro-acoustic system in mid-frequency region. To overcome this deficiency, a novel hybrid stable node-based smoothed finite element method/statistical energy analysis model is proposed in this work. The whole vibro-acoustic system can be divided into a combination of a structural subsystem with statistical behavior and an acoustic subsystem with deterministic feature. The recently developed stable node-based smoothed finite element method is employed here to simulate the deterministic subsystem, and the statistical energy analysis is utilized to deal with the statistical subsystem. Based on the so-called diffuse field reciprocity relation, these two subsystems can be easily connected and coupled. Due to the introducing of gradient smoothing and compensation operation, our algorithm can significantly reduce the dispersion error compared with the traditional finite element method. Thus, it is expected that the present coupled model can provide ultra-accurate results. Numerical examples, including both benchmark and practical engineering cases, demonstrate that our algorithm works very well in mid-frequency vibro-acoustic analysis.

A Machine Learning Approach for Coordinated Voltage and Reactive Power Control

Increasing penetration of renewable energy sources in form of distributed generators has brought many technical challenges to distribution networks. Among those, voltage and reactive power control should be revised and improved. Existing and new control resources should be coordinated based on real-time information and in closed loop. To achieve this, machine learning (ML) can be used to map the relationship between the selected network information and the desired control output. In this paper, setting of the shunt compensator operating in capacitive or inductive modes is coordinated with the tap position of substation transformer by the developed ML. Dataset emulating network behaviour during a year operation is constructed for training ML. A multi-class classification problem is formulated. Simulation results show satisfactory accuracy for some classes.

Enhancement of Dynamic Modeling for LVRT Capability in DFIG-Based Wind Turbines

Low-voltage ride through (LVRT) is important for system compensation and reliable operation of the system during balance and unbalanced voltage dips. In this study, a new LVRT capability method was developed using active and passive compensator in doubly fed induction generator (DFIG)-based wind turbines. While the active compensator provides the control of the rotor-side converter and grid-side converters of DFIG, the passive compensator decreases the stator and rotor over currents and injects reactive power into the network to support the grid voltage DFIG. Besides, rotor electromotive force is developed to improve LVRT capability against not only symmetrical but also asymmetrical faults of DFIG. It was found that the system became stable in a short time and oscillations damped using active and passive compensator modeling.

Robust fault tolerant tracking control for the multi-joint manipulator based on operator theory

Uncertainties and fault signals usually exist in the real systems and it is difficult to maintain stability and tracking performance for the real applications. Therefore, it is important to study the robust fault tolerant tracking control (RFTTC) for uncertain nonlinear feedback systems (NFS). Generally, the RFTTC schemes are designed for the uncertain NFS by using operator theory based robust right coprime factorization (ORRCF) method. In details, firstly, one novel RFTTC scheme is proposed for the uncertain NFS, wherein the fault tolerant controllers as well as the tracking one are designed based on the internal model. Secondly, the other new RFTTC scheme is proposed where the robust fault tolerant controllers are combined with the compensation operator and the tracking controller. By the proposed two design schemes, the impacts induced by the faults on the system is eliminated, and the entire nonlinear feedback system holds strong robustness and perfect tracking performance. Finally, simulation results of the multi-joint manipulator are used to indicate the validity of proposed two design schemes.

FINANCIAL DISTRESS DI BUMN INDONESIA DAN FAKTOR-FAKTOR YANG MEMPENGARUHI INVESTASI, LEVERAGE DAN CASH FLOW OPERATION TERHADAP FINANCIAL DISTRESS PADA PERUSAHAAN BUMN

This study aims to analyze the effect of managerial compensation, working capital, investment growth, and cash flow operations on financial distress in state-owned companies. This study uses several independent variables that empirically affect operating cash flow that has an impact on SOE financial distress. This study also uses financial indicators as control variables to limit the influence of factors or other variables outside the independent variable. The control variable used in this study is leverage. The researcher selected secondary data from 2014-2017 from a sample of 19 State-owned enterprises that received subsidies or equity participation from the government. The analytical method used is a statistical approach through the classical assumption test and linear regression model. The results of this study indicate that Working Capital and leverage have an influence on Financial Distress. Management Compensation, Investment Growth, and Cash Flow Operations have no influence on Financial Distress.

Operator-based nonlinear fault detection and fault tolerant control for microreactor using one-class SVM

This paper proposes a method of nonlinear fault detection scheme using one-class support vector machine (SVM) and operator-based fault tolerant control system for a microreactor with Peltier device. This method requires only one-shot training data for the normal condition of the plant. The data is used to learn for the SVM, it realises nonlinear fault detection without the known fault condition of the plant. Also, the control system that consists of the nonlinear model of the microreactor system and operator-based controller designed by the model is introduced as the fault tolerant control system using compensation operator to remove the sensor fault. The usefulness of the method is shown by an experiment of the temperature sensor fault case for the temperature control system of the microreactor system.

Algorithm for automatic compensation of voltage dips in power supply of industrial facilities

The factors of failures and their impact on the equipment of large enterprises is discussined in this article. The optimal method for compensating voltage dips was selected and the principle of operation of the developed compensator operation algorithm is described. According to the algorithm, a model was developed at the MatLab Simulink environment. The graphs of the model are presented.

An approach for attenuation-compensating multidimensional constant-Q viscoacoustic reverse time migration

Simulation of wave propagation in a constant-[Formula: see text] viscoacoustic medium is an important problem, for instance, within [Formula: see text]-compensated reverse time migration (RTM). Processes of attenuation and dispersion influence all aspects of seismic wave propagation, degrading the resolution of migrated images. To improve the image resolution, we have developed a new approach for the numerical solution of the viscoacoustic wave equation in the time domain and we developed an associated viscoacoustic RTM ([Formula: see text]-RTM) method. The main feature of the [Formula: see text]-RTM approach is compensation of attenuation effects in seismic images during migration by separation of amplitude attenuation and phase dispersion terms. Because of this separation, we are able to compensate the amplitude loss effect in isolation, the phase dispersion effect in isolation, or both effects concurrently. In the [Formula: see text]-RTM implementation, an attenuation-compensated operator is constructed by reversing the sign of the amplitude attenuation and a regularized viscoacoustic wave equation is invoked to eliminate high-frequency instabilities. The scheme is tested on a layered model and a modified acoustic Marmousi velocity model. We validate and examine the response of this approach by using it within an RTM scheme adjusted to compensate for attenuation. The amplitude loss in the wavefield at the source and receivers due to attenuation can be recovered by applying compensation operators on the measured receiver wavefield. Our 2D and 3D numerical tests focus on the amplitude recovery and resolution of the [Formula: see text]-RTM images as well as the interface locations. Improvements in all three of these features beneath highly attenuative layers are evident.

Operator-based nonlinear fault detection and fault tolerant control for microreactor using one-class SVM

This paper proposes a method of nonlinear fault detection scheme using one-class support vector machine (SVM) and operator-based fault tolerant control system for a microreactor with Peltier device. This method requires only one-shot training data for the normal condition of the plant. The data is used to learn for the SVM, it realises nonlinear fault detection without the known fault condition of the plant. Also, the control system that consists of the nonlinear model of the microreactor system and operator-based controller designed by the model is introduced as the fault tolerant control system using compensation operator to remove the sensor fault. The usefulness of the method is shown by an experiment of the temperature sensor fault case for the temperature control system of the microreactor system.

Parameter-Adaptive Compensation (PAC) for Processing Underwater Selective Absorption

The quality of visual information of underwater images is mainly limited by selective absorption and scattering in water. Usually, most researches of underwater image recovery focus on addressing the scattering via inverting an optical model, but less on compensating for the unbalance attenuation caused by the selective absorption. For this reason, the recovery results are still subject to artifacts and color shifting. In this letter, a parameter-adaptive compensation method is proposed to mitigate the selective absorption. Different from the fixed parameter utilized compensation operations, the proposed method utilizes an adaptation parameter to compensate the attenuation. Further, a rigorous gradient relation between the dominant color channel and the compensated channel is established to avoid over-compensation or under-compensation. The final recovery results show the parameter-adaptive compensation method is beneficial to improve the visual quality and the Underwater Image Quality Measurement (UIQM) scores of recovered underwater images.

Channel-Covariance and Angle-of-Departure Aided Hybrid Precoding for Wideband Multiuser Millimeter Wave MIMO Systems

Hybrid precoding is essential for millimeter wave (mmWave) multiple-input multiple output (MIMO) systems due to its inherent advantage of a high gain, whilst alleviating the high cost of hardware. However, most of the existing literature considered either the narrowband or wideband single-user mmWave MIMO scenarios. Hence in this paper we focus our attention on the more challenging design of hybrid Transmit Precoding (TPC) for wideband multiuser mmWave MIMO systems by exploiting the long-term channel’s covariance matrix and the angle of departure (AoD) information. Specifically, in the analog TPC designed, firstly, the analog TPC matrix having an infinite angular resolution is constructed based on the channel’s covariance matrix. Then, we also propose a non-uniformly spaced quantization codebook based analog TPC having finite angular resolution. Furthermore, a phase compensation operation is carried out to alleviate the effect of beam squint. As for the design of the digital TPC, a two-stage scheme is proposed to cancel the inter-user interference and to attain multiplexing gains. We study the effects of various parameters on the achievable sum rate and demonstrate with the aid of our simulation results that the proposed hybrid TPC is capable of achieving a similar performance to the excessive-complexity fully digital TPC.

Inversion-Based Time-Domain Inverse $Q$ -Filtering for Seismic Resolution Enhancement

Seismic wave will suffer from resolution loss during its propagation in subsurface media, which is caused by the earth filtering effect. Inverse Q-filtering (IQF) is an essential scheme for eliminating earth filtering effect and enhancing temporal resolution. In this letter, IQF is achieved by solving a time-domain inverse problem whose corresponding forward model is established based on the idea of seismic migration, i.e., extrapolating the unfiltered record (the detected signal if there is no earth filtering effect) from surface downward to each time–depth point and then adopting imaging condition to obtain the attenuated record. We express this migration-based forward model as a matrix-vector equation and construct the objective function of inversion-based time-domain IQF (ITIQF). Instead of adopting frequency-domain incomplete amplitude compensation operator for the purpose of antinoise, we propose three transform-domain sparse regularization strategies, including wavelet-based constraint, curvelet-based constraint, and double constraint (a combination of wavelet- and curvelet-based constraints). Solvers of these constrained ITIQF methods are provided according to convex optimization theory. We demonstrate the superior performance of ITIQF scheme via both synthetic and field data examples.