Parameters Of Objective Function Research Articles

Automatic Determination of the Number of Clusters for Semi-Supervised Relational Fuzzy Clustering

Semi-supervised clustering relies on both labeled and unlabeled data to steer the clustering process towards optimal categorization and escape from local minima. In this paper, we propose a novel fuzzy relational semi-supervised clustering algorithm based on an adaptive local distance measure (SSRF-CA). The proposed clustering algorithm utilizes side-information and formulates it as a set of constraints to supervise the learning task. These constraints are expressed using reward and penalty terms, which are integrated into a novel objective function. In particular, we formulate the clustering task as an optimization problem through the minimization of the proposed objective function. Solving this optimization problem provides the optimal values of different objective function parameters and yields the proposed semi-supervised clustering algorithm. Along with its ability to perform data clustering and learn the underlying dissimilarity measure between the data instances, our algorithm determines the optimal number of clusters in an unsupervised manner. Moreover, the proposed SSRF-CA is designed to handle relational data. This makes it appropriate for applications where only pairwise similarity (or dissimilarity) information between data instances is available. In this paper, we proved the ability of the proposed algorithm to learn the appropriate local distance measures and the optimal number of clusters while partitioning the data using various synthetic and real-world benchmark datasets that contain varying numbers of clusters with diverse shapes. The experimental results revealed that the proposed SSRF-CA accomplished the best performance among other state-of-the-art algorithms and confirmed the outperformance of our clustering approach.

Hypergames and Cyber-Physical Security for Control Systems

The identification of the Stuxnet worm in 2010 provided a highly publicized example of a cyber attack that physically damaged an industrial control system. This raised public awareness about the possibility of similar attacks against other industrial targets—including critical infrastructure. In this article, we use hypergames to analyze how strategic perturbations of sensor readings and calibrated parameters can be used to manipulate a system that employs optimal control. Hypergames form an extension of game theory that enables us to model strategic interactions where the players may have significantly different perceptions of the game(s) they are playing. Past work with hypergames has focused on relatively simple interactions consisting of a small set of discrete choices for each player. Here, we apply single-stage hypergames to larger systems with continuous variables. We find that manipulating constraints can be a more effective attacker strategy than manipulating objective function parameters. Moreover, the attacker need not change the underlying system to carry out a successful attack—it may be sufficient to deceive the defender controlling the system. It is possible to scale our approach up to even larger systems, but this will depend on the characteristics of the system in question, and we identify several characteristics that will make those systems amenable to hypergame analysis.

Distillation of neural network models for detection and description of image key points

Subject of Research. Image matching and classification methods, as well as synchronous location and mapping, are widely used on embedded and mobile devices. Their most resource-intensive part is the detection and description of the image key points. In case of classical methods for detection and description of key points they can be executed in real time on mobile devices but for modern neural network methods with better quality, such approach is difficult due to trading off performance. Thus, the issue of speeding for neural network models applied for the detection and description of key points is currently topical. The subject of research is distillation as one of the methods for reducing neural network models. The aim of the study is to obtain more compact model for detection and description of key points and a description of the procedure for this model design. Method. We proposed a method for pairing the original and more compact new model for its subsequent training on the output values of the original model. In this regard, the new model is learned to reconstruct the output of the original model without using image labels. Both networks accept identical images as input. Main Results. Neural network distillation method for detection and description of key points is tested. The objective function and training parameters that provide the best results in the framework of the study are proposed. A new data set is created for testing key point detection methods, and a new quality indicator of the allocated key points and their corresponding local features is added. New model training in the way suggested with the same number of parameters, shows greater accuracy in key points compared to the original model. A new model with a significantly smaller number of parameters shows the accuracy of point matching close to the accuracy of the original model. Practical Relevance. More compact model for detection and description of image key points is created applying the proposed method. The model is applicable on embedded and mobile devices for synchronous location and mapping. Such model application can also increase the service efficiency of the image search on the server side.

Estimating Market Expectations for Portfolio Selection Using Penalized Statistical Models

The portfolio selection problem can be viewed as an optimization problem that maximizes the risk–return relationship. It consists of a number of elements, such as an objective function, decision variables and input parameters, which are used to predict expected returns and the covariance between the said returns. However, the real values of these parameters cannot be directly observed; thus, estimations based on historical data are required. Historical data, however, can often result in modelling errors when the parameters are replaced by their estimations. We propose to address this by using some regularization mechanisms in the optimization. In addition, we explore the use of implicit information to improve the portfolio performance, such as options market prices, which are a rich source of investor expectations. Accordingly, we propose a new estimator for risk and return that combines historical and implicit information in the portfolio selection problem. We implement the new estimators for the mean-VAR and mean-VaR2 problems using an elastic-net model that reduces the risk of all estimations performed. The results suggest that the model has a good out-of-sample performance that is superior to models with pure historical estimations.

Power quality improvement of wind energy conversion system with unified power quality controller: A hybrid control model

The real problems in diminution of power quality (PQ) occur due to the rapid growth of nonlinear load are leading to a sudden decrease of source voltage for a few seconds. All these problems can be compensated by unified power quality controller (UPQC). The proposed research is based on designing a wind energy conversion system (WECS) fed to the dc-link capacitor of UPQC so as to maintain proper voltage across it and operate the UPQC for PQ improvement. The proposed research utilizes two techniques for enhancing the performance of UPQC known as integrated ant lion optimizer (IALO)-adaptive neuro fuzzy inference system (ANFIS), called IALO-ANFIS. Here, induction motor is considered as non-linear load. ALO searching behavior is enhanced by crossover and mutation. Initially, the objective function parameters are defined, that is, voltage, real, grid parameters, load parameters, real and reactive power and current. Based on these parameters, the control pulse is produced for series and shunt active power filter (APF). IALO is used to identify the optimal solutions and creates the training dataset. In light of the accomplished dataset, ANFIS predicts the best control signals of UPQC. During load variation conditions, the proposed strategy minimized the power loss and voltage instability issue individually. Subsequently, the power quality of the system is enhanced. In order to evaluate the effectiveness of the proposed method, three different cases are considered. The performance of the proposed technique is validated through MATLAB/Simulink and compared with existing techniques such as genetic algorithm and ALO.

Evaluation of the Results of Rehabilitation of Chronic Low Back Pain Syndrome in Physically Active and Inactive Patients During Leisure Time

Introduction: Movement is one of the main, modifiable factors (behaviour-dependent) that affects the health and quality of life of individuals and the entire population. Physical activity, including evidence-based active rehabilitation, is the subject of many studies on the prevention and treatment of spinal disorders, which are some of the most serious health problems. The largest group of people with spinal disorders are patients suffering from Nonspecific Low Back Pain (ang. Nonspecific Low Back Pain – NLBP), with kinesitherapy having the widest application in the chronic pain phase. A significant part of the research is devoted to the problem of movement in patients with chronic non-specific low back pain (CNLBP). One physiotherapy treatment method used in chronic low back pain syndrome is the DBC method. It is based on a detailed functional diagnosis, answers given to a standardized questionnaire, individualized treatment plans and active exercises with spinal resistance using specialized DBC equipment. Aim: The aim of this study was to evaluate the influence of leisure-time physical activity on the results of the DBC functional restoration program in patients with chronic, non-specific, low back pain after outpatient rehabilitation. The researchers tested the hypothesis that patients with regular physical activity attained better results of functional restoration compared to physically inactive participants. M aterials and Methods: The research group was comprised of one-hundred forty-seven participants. All subjects suffered from low back pain syndrome lasting for a period of at least three months without neurological symptoms. All symptoms were non-specific. The subjects were divided into two groups. Group 1 was comprised of participants who undertake regular physical activity for more than 45-minutes per exercise session, at least twice a week. Participants in Group 2 did not undertake any physical activity other than that resulting from everyday activities. The study commenced with an interview and standardized DBC questionnaire to get participants’ subjective assessment of the following parameters: current level of pain, functional limitations and scale of depression. A clinical trial was conducted prior to the measurement of objective parameters in order to exclude radicular pain (Lasegue test). Next, the objective parameters were measured: bioelectric activity of the lumbar erector spinae muscles (ang. surfasce electromyography – sEMG) on the right and left side in two positions: standing upright and leaning forward with straight legs. Also, DBC devices were used to measure the following range of motion of the lumbo-thoracic spine: flexion, extension, rotation and lateral flexion/bending. Upon completing the tests (Test 1) both groups were subject to active DBC therapeutic treatment. The therapy consisted of 12 meetings carried out within a 6 week period. Each session lasted for 1.5 hours. When the treatments ended testing was repeated for each group (Test 2). Results: Mean values of functional parameters of the lumbar spine were calculated for both groups. Statistical significance was determined between the preliminary (Test 1) and final (Test 2) tests. All the objective parameters in Group 1 showed statistically significant improvement post-therapy. Different results were observed in Group 2 for participants who did not perform regular physical activity. Although all parameters improved, only some achieved statistical significance. Significance between the two groups was also studied at the beginning of the experiment (Test 1) and after its completion (Test 2). There results showed no statistically significant difference in values of some objective parameters prior to the treatment, while some parameters differed. The treatment achieved a statistically significant improvement in the “active” group compared to the “inactive” one in almost every tested parameter excluding the extension. Significant differences between the groups were observed in the subjective parameters such as: functional limitations (ADL) (ang. Activity of Daily Living – ADL) and level of depression both in the first and last tests. Conclusions: Patients with chronic non-specific low back pain syndrome who remain physically active in leisure time and participated in the 6-week DBC rehabilitation program achieved a statistically significant improvement in the functional parameters of the spine. Patients with chronic non-specific low back pain syndrome who remain physically active in leisure time and participated in the 6-week DBC rehabilitation program improved more in the objective and subjective functional parameters of the spine in comparison with the inactive participants. Active leisure time contributes to the efficacy of physiotherapy for people with low back disorders.

Global optimization of batch and steady-state recycling chromatography based on the equilibrium model

A bi-level approach is presented for the optimal design of batch and steady-state recycling (SSR) chromatography. On the first level, an efficient formulation as mixed-integer problem (MIP) is applied to obtain optimal fractionation times for chromatograms simulated by the equilibrium model. On the second level, the optima are evaluated against a parametrized objective function. The combination of model and optimization method provides a computationally very efficient tool. A comprehensive example study demonstrates that batch chromatography achieves the highest productivities at the cost of limited yields, which is optimal only for vanishing feed costs. In other instances, the more flexible SSR concept provides larger profits. The results reveal also new optimal operating policies for SSR processes with segmented product fractions and waste streams.

Real2Sim

This paper presents a method for optimizing visco-elastic material parameters of a finite element simulation to best approximate the dynamic motion of real-world soft objects. We compute the gradient with respect to the material parameters of a least-squares error objective function using either direct sensitivity analysis or an adjoint state method. We then optimize the material parameters such that the simulated motion matches real-world observations as closely as possible. In this way, we can directly build a useful simulation model that captures the visco-elastic behaviour of the specimen of interest. We demonstrate the effectiveness of our method on various examples such as numerical coarsening, custom-designed objective functions, and of course real-world flexible elastic objects made of foam or 3D printed lattice structures, including a demo application in soft robotics.

Effect of Geometrical Parameters on the Thermal Performance of Ammonia-Based Trapezoidal-Shaped Axial Grooved Heat Pipe

Abstract Liquid–vapor interfacial shear stresses, contact angle, and thin-film resistance are incorporated in the present numerical model of the axial grooved heat pipe (AGHP). Experiments are performed to validate the numerical model, which predicts maximum heat transportation capacity (Qmax) within 2.5% error. Further, a parametric study is performed using maximum heat transportation capacity (Qmax) and total thermal resistance (Rtotal) as an objective function and geometrical parameters of groove (i.e., height of grooves (hg), number of grooves (N), and groove inclination angle (2υ)) as variables. From the numerical results, it is observed that number of grooves (N) and groove inclination angle (2υ) are inversely proportional to Rtotal as desired. Therefore, an increase in N and 2υ results into reduction in Rtotal. However, an increment in hg increases Rtotal due to liquid layer resistance into the grooves. Study is aimed to determine such a combination of variable which can maximize Qmax and minimize Rtotal. For ammonia based AGHP of 10.5 mm ID, 12.7 mm OD, and 1 m length, the best combination is determined as hg = 1.3 mm, N = 28 and 2υ = 76 deg, which gives Qmax and Rtotal as 109 W and 0.093 K/W, respectively.

Multi-Microgrid-Based Operation of Active Distribution Networks Considering Demand Response Programs

In a novel approach, this paper investigates the effect of demand response (DR) programs simultaneous with the multi-microgrid-based operation of active distribution networks. In the proposed model, the possibility of microgrid construction is evaluated through technical indices such as supply-adequacy, efficiency, voltage profile, and reliability as well as DR transaction costs. The multi-objective optimization problem is solved by a powerful method named non-dominated genetic algorithm-II (NSGA-II). Due to the probabilistic nature of renewable energy sources, loads, and energy prices, these are modeled using their probability density functions by Monte Carlo simulation algorithm. Also, the backward–forward load flow method is employed for obtaining the objective function parameters. The results of the proposed model are compared with the conventional operation method in various scenarios by implementing it on IEEE 69-bus test distribution system.

Multiobjective Quadratic Fractional Programming using Iterative Parametric Function

The paper proposed the Model of multiobjective quadratic fractional optimisation problem with a set of quadratic constraints and a methodology for obtaining a set of solutions based on the approach of using iterative parametric functions. Firstly, each fractional objective function is transformed into non-fractional parametric objective function by assigning a vector of parameters to each objective function. In this approach, the Decision Maker(DM) predecides the desired tolerance levels of the objective functions in the form of termination constants. Then, by using ε-constraint method, a set of efficient solutions is obtained and termination conditions are checked for each parametric objective function. Also, a comparative study of the proposed method and fuzzy approach is given to reveal the validity of the method. A numerical for Multiobjective quadratic fractional programming Model (MOQFPM) is given in the end to check the applicability of the approach.

Damaged-aircraft trailer dynamics simulation and vibration optimization

As a rescue vehicle, damaged-aircraft trailer is used to move damaged aircraft quickly to restore the normal order of the airport. Several damaged-aircraft trailer parameters such as tire stiffness and damping of the suspension hydraulic system influence the dynamic performance significantly. In this article, a simplified 9 degrees of freedom model of damaged-aircraft trailer is established considering the physical parameters of suspension and tires. The relationships among the parameters of the suspension hydraulic components, the elastic force and damping force are established, and then the optimization model of the whole vehicle is obtained. In order to reduce the secondary damage to the aircraft, the multi-island genetic algorithm is used to optimize the suspension system and tire. During the calculation, the maximum vertical acceleration of damaged-aircraft trailer is taken as objective function for variable parameters of the suspension hydraulic system and the tire. As a result, the performance of the vehicle is greatly improved with the maximum acceleration of 0.2 m/s2 after optimization.

A chaotic particle swarm optimization algorithm for solving optimal power system problem of electric vehicle

Transportation of electrification has become a hot issue in recent decades and the large-scale deployment of electric vehicles has yet to be actualized. This article proposes a powertrain parameter optimization design approach based on chaotic particle swarm optimization algorithm. To improve the driving and economy performance of pure electric vehicles, chaotic particle swarm optimization algorithm is adopted in this study to optimize principal parameters of vehicle power system. Vehicle dynamic performance simulations were carried out in the Cruise software, and the simulation results before and after optimization were compared. Simulation results show that optimized vehicles by chaotic particle swarm optimization can meet the expected dynamic performance and the driving range has been greatly improved. Meanwhile, it is also viable that the parameters of the optimal objective function can achieve the purpose of balancing the driving performance and economic performance, which provides a reference for the development of vehicle dynamic performance.

The Optimal Location of Ground-Based GNSS Augmentation Transceivers

Modern Global Navigation Satellite Systems (GNSS) allow for positioning with accuracies ranging from tens of meters to single millimeters depending on user requirements and available equipment. A major disadvantage of these systems is their unavailability or limited availability when the sky is obstructed. One solution is to use additional range measurements from ground-based nodes located in the vicinity of the receiver. The highest accuracy of distance measurement can be achieved using ultra wide band (UWB) or ZigBee phase shift measurement. The position of the additional transmitter must be carefully selected in order to obtain the optimal improvement in the dilution of precision (DOP), which reflects the improvement in the geometry of solution. The presented case study depicts a method for selecting the optimal location of a ground-based ranging source. It is based on a search of a minimum DOP value as a transmitter location function. The parameters of objective function are the elevation and azimuth of the transceiver. The solution was based on a limited-memory Broyden–Fletcher–Goldfarb–Shanno with Box constraints (L-BFGS-B) method and a numerical optimization algorithm for parameter value estimation. The presented approach allows for the selection of the optimal location of a ground-based source of ranging signals in GNSS processing from a geometry of solution point of view. This can be useful at the design stage of an augmentation network of ground-based transceivers. This article presents a theoretical basis and a case study presenting the selection of the optimal location of a ground-based ranging source.

The method of conditional minimization based on the selection of the optimal value of the objective function

One of the convex programming methods that allows to reduce the solution of the original problem to the successive minimization of auxiliary functions on the whole space or on simple sets is the method of parametrization of the objective function. A method close to the one mentioned above is proposed in this paper. As in the known method, in order to find an iterative point, we solve the minimization problem of some auxiliary function constructed with cuts of the parametrized objective function and the constraint functions. The proposed method has some advantages over the parametrization method of the objective function due to a different principle of setting parameter values. The method convergence is substantiated. The properties associated with its implementation are discussed.

Analyzing interval and multi-choice bi-level programming for Stackelberg game using intuitionistic fuzzy programming

The aim of this paper is to provide a computational algorithm for solving interval-valued and multi-choice bi-level programming for Stackelberg game using intuitionistic fuzzy approach. It also includes the cost parameters of upper and lower-level objective functions corresponding to interval numbers and multi-choice types while the parameters of constraints are intuitionistic fuzzy numbers. The interval-valued and multi-choice types objective functions are converted into deterministic form using interval programming and a general transformation technique respectively. Again, the intuitionistic fuzzy parameters of constraints are reduced to an interval by taking expected value of intuitionistic fuzzy number. A conflicting nature between the objective functions is resolved with the help of intuitionistic fuzzy programming by considering nonlinear degree of membership and non-membership functions respectively. The resultant max-min problem is solved with LINGO 15.0 iterative scheme. The developed algorithm is illustrated by an application example and Pareto optimality test is performed as well. The conclusions and an outlook of future study are described at last.

Generation of Rule Bases of Fuzzy Systems Based on Modified Ant Colony Algorithms

This article is dedicated to the development and study of the method of generation of fuzzy systems rule bases of Mamdani-type with the formation of optimal consequents based on modified ant colony optimization algorithms. The obtained method makes it possible to effectively generate the rule bases with the optimal consequents for the Mamdani-type fuzzy systems in the following cases: at insufficient amount of initial information (under conditions of a high degree of information uncertainty); at a sufficiently large number of rules for which the development of a rule base based on the knowledge of experts is not always effective; at different levels of experts qualification. The study of the effectiveness of the proposed method in this work is carried out at the development of the rule base of the Mamdani-type fuzzy controller for the automatic control system of the reactor temperature of the specialized pyrolysis complex, that is intended for utilization of polymer waste. At the same time, the generation of the consequents of the rule base of this fuzzy controller was carried out with various adjustable parameters of the method, such as the total number of agents and the number of elite agents in the population. Based on the analysis of the results of the conducted experiments, the optimal configuration of the population of agents of the method was determined, for which the optimal vector of the consequents of the rule base can be found at the lowest computational and time costs. Also, the results of computer simulations of transients showed that the automatic temperature control system with the developed fuzzy controller rule base based on the proposed by the authors method with the optimal configuration of parameters has a significantly lower value of the objective function and higher control quality parameters than the system with the rule base on the basis of experts knowledge, which, in turn, confirms the high efficiency of this method.

Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm

The deployment of utility-scale energy storage systems (ESSs) can be a significant avenue for improving the performance of distribution networks. An optimally placed ESS can reduce power losses and line loading, mitigate peak network demand, improve voltage profile, and in some cases contribute to the network fault level diagnosis. This paper proposes a strategy for optimal placement of distributed ESSs in distribution networks to minimize voltage deviation, line loading, and power losses. The optimal placement of distributed ESSs is investigated in a medium voltage IEEE-33 bus distribution system, which is influenced by a high penetration of renewable (solar and wind) distributed generation, for two scenarios: (1) with a uniform ESS size and (2) with non-uniform ESS sizes. System models for the proposed implementations are developed, analyzed, and tested using DIgSILENT PowerFactory. The artificial bee colony optimization approach is employed to optimize the objective function parameters through a Python script automating simulation events in PowerFactory. The optimization results, obtained from the artificial bee colony approach, are also compared with the use of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS placement approach can successfully achieve the objectives of voltage profile improvement, line loading minimization, and power loss reduction, and thereby significantly improve distribution network performance.

Quantitative Inversion of Stress and Crack in Ferromagnetic Materials Based on Metal Magnetic Memory Method

Ferromagnetic materials are widely used in engineering structures. Damages caused during manufacture and the use of ferromagnetic materials may seriously undermine the safety of the engineering structure, and even lead to serious industrial accidents. For the early damage testing problem of ferromagnetic materials, it is imperative to propose and adopt new nondestructive testing methods. The metal magnetic memory (MMM) method, which is also known as the micromagnetic testing method, can achieve the early detection of damages in ferromagnetic materials such as steels. However, due to the lack of the quantitative research, the MMM method is only applicable for damage localization. In this paper, the MMM method is utilized to quantitatively evaluate the stress and crack in ferromagnetic materials. The inverse model including the objective function and optimization parameters is proposed. The reconstruction approach is established based on the conjugate gradient inversion method and the exact line search algorithm. In the theoretical analysis, the effectiveness of the MMM method in quantitative determination regarding the position and size of defects is verified with the experimental signal of a hole defect. Following this, the theoretical analyses of localization and sizing of stress and crack are conducted by using the simulation results as the measured signals. It can be found from the results that the MMM method can be applicable for quantitative evaluation of surface rectangular cracks, surface stress concentration zone, convex-type defect, and so on. In addition, the influences of the signal selection, sampling rate, lift-off value, stress level, and noise amplitude on the quantitative analysis of early stress concentration are discussed in details.

Updated Bayesian detection of foundation parameter with Jeeves pattern search theory

Updated Bayesian detection of foundation parameters in the specific foundation mechanical model was studied based on Jeeves pattern search theory. Firstly, the updated Bayesian objective function for general foundation parameters was derived which could synchronously take the stochastic property of systematic parameters and systematic responses into account. Then the governing differential equations for the Winkler foundation model were gained with elastic Mindlin plate theory and the Fourier close form solution of the foundation model was achieved with the Fourier transform method. After the step length of pattern movement was determined with the quadratic parabolic interpolation method, the updated Bayesian detection of stochastic foundation parameters was resolved with Jeeves pattern search theory and then the corresponding detection procedure was completed. Through particular example analysis, the updated Bayesian detection of stochastic foundation parameters has excellent numerical stability and convergence during iterative processes. Jeeves pattern search theory is unconcerned with the partial derivatives of systematic responses to foundation parameters, and undoubtedly has satisfactory iterative efficiency compared with the available Kalman filtering or conjugate gradient detections of the significant foundation parameters. If the iterative processes are efficiently convergent, it is an important prerequisite that the systematic response assignment should be accurate enough. The derived Jeeves pattern search method with updated Bayesian theory can be applied in other kinds of foundation parameters.