Feasible Vectors Research Articles

On value efficiency analysis and cone-ratio data envelopment analysis models

AbstractIn this paper we relate the Value Efficiency Analysis (VEA) efficiency scores with those of three different Cone-Ratio Data Envelopment Analysis (CR-DEA) models, when the chosen model Decision Making Units (DMUs) in CR-DEA jointly comprise the Most Preferred Solution (MPS) in VEA. In particular, we consider the cases where the set of feasible weight vectors is given respectively by (i) the weight vectors that are jointly optimal in DEA for all the model DMUs, (ii) every weight vector that is optimal in DEA for at least one of the model DMUs, and (iii) only those weight vectors with strictly positive input and output weights, each of which is jointly optimal in DEA for all the model DMUs. In each of these cases we show that the CR-DEA efficiency scores can be obtained or approximated by estimating either a VEA model or a series of VEA models, without the need of identifying a priori the set of feasible weight vectors. We illustrate the usefulness of our results by means of an empirical application using data on Japanese regional banks.

Robust fault-tolerant control for dynamic positioning of ships with prescribed performance

This study presents a new fault-tolerant control scheme for ship dynamic positioning. The scheme aims to handle unknown disturbances, imprecise fault estimation, and control input constraints. First, an adaptive sliding mode disturbance observer is introduced. This observer effectively reconstructs disturbances in finite time, even without knowledge of their derivative upper bounds. Next, we design a novel prescribed performance function that sets bounds on tracking errors. This function is combined with an auxiliary intermediate control technique to create a high-level controller. Lastly, a robust fault-tolerant control allocation scheme is proposed to redistribute the generalized forces among faulty actuators. It autonomously finds a feasible force vector in the attainable force set. Simulation results are provided to demonstrate the effectiveness of the proposed control scheme.

Design of a real prototype of a hybrid fuel cell suv: Vehicle "Hércules"

Hydrogen is becoming one of the most feasible energy vectors to succeed gasoline as fuel. It has the potential to be obtained from renewable energy sources, having almost a null impact on contamination. Oil wells are drying up and oil does not fulfil the ZEV (zero emission vehicle) program of contamination. These reasons lead to think that oil will pass the baton to hydrogen as the next energetic vector for automotive applications. In addition, hydrogen has the advantage of being used directly in fuel cells which have higher efficiency than internal combustion engines (ICE). Project Hércules consists of the construction of a real sport utility vehicle (SUV) prototype of a fuel cell vehicle (FCV) based in the model Santana 350. It will be fuelled by hydrogen obtained totally renewably by using an electrolyser fed by solar energy. This paper shows, in this framework, all the steps that are needed to be covered to specify the power of the fuel cell, battery and electric motor using the simulation tool ADVISORTM to match the desired specifications.

Adeno-Associated Virus Engineering and Load Strategy for Tropism Modification, Immune Evasion and Enhanced Transgene Expression.

Gene therapy aims to add, replace or turn off genes to help treat disease. To date, the US Food and Drug Administration (FDA) has approved 14 gene therapy products. With the increasing interest in gene therapy, feasible gene delivery vectors are necessary for inserting new genes into cells. There are different kinds of gene delivery vectors including viral vectors like lentivirus, adenovirus, retrovirus, adeno-associated virus et al, and non-viral vectors like naked DNA, lipid vectors, polymer nanoparticles, exosomes et al, with viruses being the most commonly used. Among them, the most concerned vector is adeno-associated virus (AAV) because of its safety, natural ability to efficiently deliver gene into cells and sustained transgene expression in multiple tissues. In addition, the AAV genome can be engineered to generate recombinant AAV (rAAV) containing transgene sequences of interest and has been proven to be a safe gene vector. Recently, rAAV vectors have been approved for the treatment of various rare diseases. Despite these approvals, some major limitations of rAAV remain, namely nonspecific tissue targeting and host immune response. Additional problems include neutralizing antibodies that block transgene delivery, a finite transgene packaging capacity, high viral titer used for per dose and high cost. To deal with these challenges, several techniques have been developed. Based on differences in engineering methods, this review proposes three strategies: gene engineering-based capsid modification (capsid modification), capsid surface tethering through chemical conjugation (surface tethering), and other formulations loaded with AAV (virus load). In addition, the major advantages and limitations encountered in rAAV engineering strategies are summarized.

A preference elicitation approach for the ordered weighted averaging criterion using solution choice observations

Decisions under uncertainty or with multiple objectives usually require the decision maker to formulate a preference regarding risks or trade-offs. If this preference is known, the ordered weighted averaging (OWA) criterion can be applied to aggregate scenarios or objectives into a single function. Formulating this preference, however, can be challenging, as we need to make explicit what is usually only implicit knowledge. We explore an optimization-based method of preference elicitation to identify appropriate OWA weights. We follow a data-driven approach, assuming the existence of observations, where the decision maker has chosen the preferred solution, but otherwise remains passive during the elicitation process. We then use these observations to determine the underlying preference by finding the preference vector that is at minimum distance to the polyhedra of feasible vectors for each of the observations. Using our optimization-based model, weights are determined by solving an alternating sequence of linear programs and standard OWA problems. Numerical experiments on risk-averse preference vectors for selection, assignment and knapsack problems show that our passive elicitation method compares well against having to conduct pairwise comparisons and performs particularly well when there are inconsistencies in the decision maker’s choices.

The folk theorem for the prisoner's dilemma with endogenous private monitoring

We study the repeated prisoner's dilemma with private monitoring under the assumption that the monitoring structure is endogenously chosen by the players in each period. We allow the players to choose from all possible monitoring structures. If the players disagree on the monitoring structure they would like, the realized monitoring structure is determined by a function that aggregates their choices. When one player can dictate the monitoring structure, then the repetition of the stage Nash is the only sequential equilibrium outcome. In contrast, when no player can dictate the monitoring structure, we provide conditions on the aggregation function under which any strictly individually rational and feasible payoff vector can be supported in sequential equilibrium.

Deep Reinforcement Learning Empowers Wireless Powered Mobile Edge Computing: Towards Energy-Aware Online Offloading

Deep integration of wireless power transmission and mobile edge computing (MEC) promotes wireless powered MEC to become a new research hotspot in the field of Internet of Things. In this paper, we focus on the joint optimization problem of online offloading decision and charging resource allocation for minimizing task accomplishing time in dynamic time-varying wireless channel scenarios. The optimal solution involves addressing a mixed integer programming problem in real time, which is proved to be NP-hard, and imposes nontrivial challenges to design with conventional optimization methods. To efficiently address this problem, we leverage the deep reinforcement learning (DRL) technology to propose an energy-aware online offloading algorithm called EAOO. EAOO algorithm learns empirically the online offloading decision policies via a well-designed DRL framework, and adopts the feasible solution region analysis method to implement the charging resource allocation. We further propose a novel feasible decision vector generation method, and incorporate the crossover and mutation technology to expand the offloading vector search space with the provable feasibility guarantee. Extensive experimental results show that, our EAOO algorithm outperforms existing baseline algorithms, and achieves near-optimal performance with low CPU execution latency, which well satisfies the practical requirements of real-time and efficiency.

Opt-RNN-DBFSVM: Optimal recurrent neural network density based fuzzy support vector machine

Two major problems are encountered when using fuzzy SVM: (a) the number of local minima increases exponentially with the number of samples and (b) the quantity of required computer storage, required for a regular quadratic programming solver, increases by an exponential magnitude as the problem size expands. The Kernel-Adatron family of algorithms gaining attention lately which has allowed to handle very large classification and regression problems. However, these methods treat different types of samples (Noise, border, and core) with the same manner, which causes searches in unpromising areas and increases the number of iterations. In this work, we introduce a hybrid method to overcome these shortcoming, namely Optimal Recurrent Neural Network Density Based fuzzy Support Vector Machine (Opt-RNN-DBFSVM). This method consists of four steps: (a) characterization of different samples, (b) elimination of samples with a low probability of being a support vector, (c) construction of an appropriate recurrent neural network based on an original energy function, and (d) solution of the system of differential equations, managing the dynamics of the RNN, using the Euler–Cauchy method involving an optimal time step. Thanks to its recurrent architecture, the RNN remembers the regions explored during the search process. We demonstrated that RNN-FSVM converges to feasible support vectors and Opt-RNN-DBFSVM has a very low time complexity compared to RNN-FSVM with constant time step, and KAs-FSVM. Several experiments were performed on academic data sets. We used several classification performance measures to compare Opt-RNN-DBFSVM to different classification methods and the results obtained show the good performance of the proposed method.

OPT-RNN-DBSVM: OPTimal Recurrent Neural Network and Density-Based Support Vector Machine

When implementing SVMs, two major problems are encountered: (a) the number of local minima of dual-SVM increases exponentially with the number of samples and (b) the computer storage memory required for a regular quadratic programming solver increases exponentially as the problem size expands. The Kernel-Adatron family of algorithms, gaining attention recently, has allowed us to handle very large classification and regression problems. However, these methods treat different types of samples (i.e., noise, border, and core) in the same manner, which makes these algorithms search in unpromising areas and increases the number of iterations as well. This paper introduces a hybrid method to overcome such shortcomings, called the Optimal Recurrent Neural Network and Density-Based Support Vector Machine (Opt-RNN-DBSVM). This method consists of four steps: (a) the characterization of different samples, (b) the elimination of samples with a low probability of being a support vector, (c) the construction of an appropriate recurrent neural network to solve the dual-DBSVM based on an original energy function, and (d) finding the solution to the system of differential equations that govern the dynamics of the RNN, using the Euler–Cauchy method involving an optimal time step. Density-based preprocessing reduces the number of local minima in the dual-SVM. The RNN’s recurring architecture avoids the need to explore recently visited areas. With the optimal time step, the search moves from the current vectors to the best neighboring support vectors. It is demonstrated that RNN-SVM converges to feasible support vectors and Opt-RNN-DBSVM has very low time complexity compared to the RNN-SVM with a constant time step and the Kernel-Adatron algorithm–SVM. Several classification performance measures are used to compare Opt-RNN-DBSVM with different classification methods and the results obtained show the good performance of the proposed method.

Graph-Based Interdependent Cyber-Physical Risk Analysis of Power Distribution Networks

This paper proposes a graph-based model for interdependent cyber-physical risk analysis of power distribution networks. The proposed methodology evaluates device criticality in the interdependent cyber and physical networks that comprise modern power distribution networks. A network traversal algorithm is developed to identify latent interdependent support and dependency structures as network subgraphs. Identifying these subgraphs provides a context-aware approach to model feasible cyber-attack vectors, define metrics for device criticality, and measure overall network resilience to cyber-attacks. The proposed methodology is demonstrated in a cyber-attack detection study. The results indicate that the proposed context-aware criticality metrics, based on the dependency subgraph framework, better identify the risks of cascading dependencies in power distribution networks. Further, several characteristics of the power distribution network such as interdependencies, device vulnerabilities, and attack target criticalities detailed in the proposed methodology have significant impacts on the performance of attack detection strategies.

Robust Additive Value-Based Efficiency Analysis with a Hierarchical Structure of Inputs and Outputs

We introduce a novel methodological framework based on additive value-based efficiency analysis. It considers inputs and outputs organized in a hierarchical structure. Such an approach allows us to decompose the problem into manageable pieces and determine the analyzed units’ strengths and weaknesses. We provide robust outcomes by analyzing all feasible weight vectors at different hierarchy levels. The analysis concerns three complementary points of view: distances to the efficient unit, ranks, and pairwise preference relations. For each of them, we determine the exact extreme results and the distribution of probabilistic results. We apply the proposed method to a case study concerning the performance of healthcare systems in sixteen Polish voivodeships (provinces). We discuss the results based on the entire set of factors (the root of the hierarchy) and three subcategories. They concern health improvement of inhabitants, efficient financial management, and consumer satisfaction. Finally, we show the practical conclusions that can be derived from the hierarchical decomposition of the problem and robustness analysis.

Significantly Enhanced Electrostrain in Oriented Epitaxial Self-Assembled Aurivillius-Type Piezoelectric Films via Regulating Polarization Vectors.

High-temperature piezoelectric films with excellent piezoelectric and ferroelectric properties lay the foundation for the development of high-temperature piezo-MEMS devices. However, due to the poor piezoelectricity and strong anisotropy, it remains a challenge to obtain high quality Aurivillius-type high-temperature piezoelectric films with high performance, which impedes their practical implements. Here, a feasible polarization vector regulation strategy associated with oriented epitaxial self-assembled nanostructures for enhancing electrostrain is proposed. Guided by lattice matching relation, non-c-axis oriented epitaxial self-assembled Aurivillius-type calcium bismuth niobate (CaBi2Nb2O9, CBN) high-temperature piezoelectric films were successfully prepared on different oriented Nb-STO substrates. By the lattice matching relationship, hysteresis measurement, and piezoresponse force microscopy analysis, it is confirmed that the polarization vectors transform from a two-dimensional plane to a three-dimensional space, and the out-of-plane polarization switching is enhanced. A platform for more possible polarization vectors is provided in the self-assembled (013)CBN film. More importantly, enhanced ferroelectric (Pr ∼ 13.4 μC/cm2) and large strain (∼0.24%) were obtained in the (013)CBN film, which promotes the great application prospect of CBN piezoelectric films in high-temperature MEMS devices.

Double-Objective Global Optimal Model-Free Predictive Control for SMPMSM Drive System Based on DSVM

To achieve the global optimization of stator current and inverter switching frequency, a double-objective global optimal model-free predictive control (MFPC) is proposed based on discrete space vector modulation (DSVM) for surface-mounted permanent magnet synchronous motor (SMPMSM) drive system. First, the double-objective invalid optimization area of the conventional DSVM-based finite control set (FCS) model predictive control (MPC) is revealed, and the reason is analyzed that the global optimal voltage vector cannot be achieved. Then, according to the distribution of the double-objective invalid optimization area, the voltage hexagon is divided into three subareas. In each subarea, the candidate voltage vector with the best current control performance (BC-CVV) is obtained. Moreover, the inverter switching number of the three BC-CVVs is used as a criterion, and the number of voltage vectors evaluated online is significantly reduced. As a result, the double-objective global optimal voltage vector is achieved without enumerating all the feasible voltage vectors. Finally, experiments under different operating conditions are implemented and the effectiveness of the proposed method is confirmed.

Predictive discrete‐time sliding mode based direct power control for grid‐connected energy conversion systems

AbstractA method for digital direct power control design for grid‐connected energy conversion systems was proposed in this paper. The active and reactive powers are directly controlled with constant switching frequency by selecting an appropriate switching control sequence of voltage source converter as a key part of energy conversion systems. The control design combines a discrete‐time sliding mode control and predictive control. The selection of feasible switching control vectors is a direct result of the discrete‐time sliding mode control approach. These vectors satisfy the existence conditions of the sliding mode, but each of them induces system motion with different properties. The task of predictive control is to select an appropriate switching control sequence to reduce the chattering phenomenon, steady‐state error, and overshoot. The cost function of the prediction process is defined as the average value of both power errors on the prediction horizon of three sampling periods. Simulation results, experimental results, and results of a comparative analysis are presented in order to show the performance and effectiveness of the proposed control design.

Inverse Linear Goal Programming Problem

This paper considers the inverse linear goal programming problem of multi-objective function in case the change in coefficient of the objective function. 
 Let denote the set of feasible solutions points of linear goal programming problem of a multi-objective function, and let be the positive and negative deviation variables of the maximum and minimum goals respectively, be a specified cost vector, be given feasible solution vector, and be given tow vectors denoted the feasible positive deviation and the feasible negative deviation points of the max or min goals, respectively.
 The inverse linear goal programming problem of multi-objective function is as follows:
 Consider the change of the cost vectors as less as possible such that the vectors feasible solution becomes an optimal solution of LGP of multi-objective function under the new cost vectors and is minimal, where is some selected -norm. 
 In this paper, we consider the inverse version ILGP of LGMP. under the -norm where the objective is to minimize , with denoting the index set of variables . We show that the inverse version of the considered under -norm reduces to solving a problem for the same kind; that is, an inverse multi-objective assignment problem reduces to an assignment problem.
 

DC Power Grids With Constant-Power Loads—Part II: Nonnegative Power Demands, Conditions for Feasibility, and High-Voltage Solutions

In this two-part article, we develop a unifying framework for the analysis of the feasibility of the power flow equations for dc power grids with constant-power loads. Part II of this article, explores further implications of the results in Part I. We present a necessary and sufficient linear matrix inequality (LMI) condition for the feasibility of a vector of power demands (under small perturbation), which extends a necessary condition in the literature. The alternatives of these LMI conditions are also included. In addition, we refine these LMI conditions to obtain a necessary and sufficient condition for the feasibility of nonnegative power demands, which allows for an alternative approach to determine power flow feasibility. Moreover, we prove two novel sufficient conditions, which generalize known sufficient conditions for power flow feasibility in the literature. Finally, we prove that the unique long-term voltage semistable operating point associated to a feasible vector of power demands is a strict high-voltage solution. A parametrization of such operating points, which is dual to the parametrization in Part I, is obtained, as well as a parametrization of the boundary of the set of feasible power demands.

On the Design of a Matrix Adaptation Evolution Strategy for Optimization on General Quadratic Manifolds

An evolution strategy design is presented that allows for an evolution on general quadratic manifolds. That is, it covers elliptic, parabolic, and hyperbolic equality constraints. The peculiarity of the presented algorithm design is that it is an interior point method. It evaluates the objective function only for feasible search parameter vectors and it evolves itself on the nonlinear constraint manifold. Such a characteristic is particularly important in situations where it is not possible to evaluate infeasible parameter vectors, e.g., in simulation-based optimization. This is achieved by a closed form transformation of an individual’s parameter vector, which is in contrast to iterative repair mechanisms. This constraint handling approach is incorporated into a matrix adaptation evolution strategy making such algorithms capable of handling problems containing the constraints considered. Results of different experiments are presented. A test problem consisting of a spherical objective function and a single hyperbolic/parabolic equality constraint is used. It is designed to be scalable in the dimension. As a further benchmark, the Thomson problem is used. Both problems are used to compare the performance of the developed algorithm with other optimization methods supporting constraints. The experiments show the effectiveness of the proposed algorithm on the considered problems. Additionally, an idea for handling multiple constraints is discussed. And for a better understanding of the dynamical behavior of the proposed algorithm, single run dynamics are presented.

Investigate exact reliability under limited time and space of a multistate online food delivery network

In recent years, especially during the outbreak of COVID-19, there have witnessed the emergence of online food delivery (OFD) services that allow customers to place orders via Internet-connected devices to obtain door-to-door delivered meals. In this service, on-time delivery to the right customer with high-quality fresh meals is deemed the primary factor. This paper investigates the reliability of OFD through its ability to meet customer needs in a specific context. During the delivery process, the courier receives the food at the store, packs it into a delivery box, and then delivers it to the customer. Note that, the delivery boxes are limited in space. Customers prefer having their orders delivered within a given time threshold. Thus, reliability is defined as the probability to complete non-integer and multiple orders under a limited time and space. Besides, the OFD system belongs to the gig economy, which allows couriers to select their shifts and working regions. Hence, the capacity of each region, which indicates the number of available couriers, is regarded as multistate. We, thus, construct the OFD system as a multistate online food delivery network (MOFN). Under the limitation of time and space, all feasible minimal capacity vectors are determined to compute the reliability. With the proposed investigation, managers can understand their MOFN’s ability to meet specific customer demands. Accordingly, they can make appropriate adjustments for better performance.

An efficient multi-objective optimization approach for sensor management via multi-Bernoulli filtering

Intelligent sensor management is generally required for efficient and accurate data processing when the multi-sensor system is used for multi-target tracking (MTT). However, this is theoretically and computationally challenging. To deal with this problem, we propose a novel sensor management approach based on efficient multi-objective optimization for MTT under the framework of partially observed Markov decision process. The multi-Bernoulli filter is used in conjunction with two objective functions. To simplify the multi-objective optimization problem, we use the Euclidean distance (ED) between the feasible and utopian solution vectors as a measure of the objectives and then sequentially select sensors from the candidates. For the selected sensors, we rank them according to the obtained ED measure and implement the iterated-corrector fusion scheme after the ranking. Numerical studies demonstrate the effectiveness and efficiency of our approach in multi-sensor MTT scenarios.

Glutathione targeted tragacanthic acid-chitosan as a non-viral vector for brain delivery of miRNA-219a-5P: An in vitro/in vivo study

Multiple sclerosis (MS) is a progressive chronic demyelinating and neurodegenerative disease. The symptoms could only be diminished through stimulated remyelination. Although administration of microRNA-219a-5P (miR-219) seems to recover the damages, it is hampered by the challenging delivery of genes to the central nervous system across the blood-brain barrier. To enhance the CNS delivery of miR-219, a novel non-viral targeted vector was appraised by conjugating chitosan (Ch) to tragacanthic acid (TA) and glutathione (Glu). The nanoparticles were characterized and injected into the cuprizone model of MS mice to investigate the in vivo features of the resulting polyplex. Transmission electron microscopy, luxol fast blue staining, and proteolipid protein 1 (Plp1) overexpression confirmed more compact myelin sheaths following the administration of the targeted miR-219 nanoparticles and positron emission tomography (PET) scan also demonstrated the reduced inflammation and higher cell regeneration in the brain. Fluorescence microscopy and in vivo imaging were employed to identify miR-219 accumulation patterns in mice. The polyplex led to miR-219 overexpression, crystallin alpha B upregulation, and apolipoprotein E downregulation. It was concluded that glutathione targeted Ch/TA nanoparticles could be exploited as a feasible non-viral vector for miR-219 specific targeting to the brain, miR-219 overexpression and inflammation abatement in MS.