Role Identification Based on the Information Dependency Complexity

In order to reduce power losses in a power system and to improve voltage profile, researchers have examined and studied optimizing Photovoltaic Power distributed generation (DG) which is location and size in a power system, but the results have some drawbacks. Several approaches developed to make this important issue more efficient, including coming up with new algorithms and improving those already in existence. Many of the proposed algorithms are only concerned with the real power loss, however. Voltage stability control is a critical factor in modern power systems, which makes incorporating reactive power losses in optimizing DG allocation for voltage profile improvement necessary. The goal of this work is to solve this issue by combining Genetic Algorithm and Improved Particle Swarm optimization to optimize DG size and location by considering both real and reactive power losses. Power loss sensitivity factors and real and reactive power flow factors used in identifying which buses will receive DGs. A MATLAB-based program was developed and tested on a test system using distributed generators, considering the proposed method. As compared to Genetic Algorithm, Particle Swarm optimization and Improved Particle Swarm optimization methods, the Hybrid Genetic Algorithm Improved Particle Swarm optimization method is better for reducing both real and reactive power losses.

Due to its complexity and non-deterministic polynomial-time hard characteristic, multirobot task allocation problem remains a challenging issue in the field of cooperative robotics. Thanks to its easy implementation and promising convergence speed, the particle swarm optimization method has recently aroused increasing research interest in the area of multirobot task allocation problem. However, the efficiency of the standard particle swarm optimization is hindered by several deficiencies such as the inefficient capabilities in balancing exploration and exploitation, as well as the high likelihood of plunging into stagnation. Aiming at enhancing the performance of particle swarm optimization via remedying these two drawbacks, this paper proposes an improved particle swarm optimization method, which integrates standard particle swarm optimization 2011 with evolutionary game theory. To prevent particles being locked into stagnation, particles in the proposed particle swarm optimization first adopt the updating rules of standard particle swarm optimization 2011 to undertake their movements. Subsequently, attempting to well trade off the exploration and exploitation capabilities of particles, a novel self-adaptive strategy, which is determined by the evolutionary stable strategies of evolutionary game theory and the iteration number of particle swarm optimization, is presented to adaptively adjust the main control parameters of particles in the proposed particle swarm optimization. Since the convergence of particle swarm optimization remains paramount and dramatically affects the performance of particle swarm optimization, this paper also analytically investigates the convergence of the proposed particle swarm optimization and provides a convergence-guaranteed parameter selection principle for the proposed method. Finally, leveraging the development of the proposed particle swarm optimization, this paper completes the design of a new particle swarm optimization–based multirobot task allocation method. The performance of the new particle swarm optimization–based multirobot task allocation method is tested through three different allocation cases against four well-known evolutionary methods. Experimental results confirm that the proposed method generally outperforms its contenders in terms of the solution quality. Moreover, the proposed method performs slightly better than the majority of its peers as far as the computation time is concerned.

Nowadays, it is crucial for several organizations to look for solutions allowing to reduce costs incurred by the execution of their business processes. Previously, we proposed a general approach for the extension of business process models with cost perspective based on process mining, independently of the model notation. This approach includes a cost data analysis based on applying classification algorithms selected dependently on the case in hand. Moreover, the proposed cost data analysis allows extracting knowledge about factors influencing on cost at process and activity levels. These factors include attributes corresponding only to the organizational and data perspectives. However, the control-flow perspective is not taken into account while it may also have influence on the business process incurred costs at the process level. Moreover, the proposed approach for selecting the appropriate classification algorithm is generally presented and illustrated. Thus, in this paper, we propose to improve the cost data analysis approach by including the control-flow perspective and by providing formal details about the context-aware classification algorithm selection as well as a detailed illustration of the proposed approach. This leads to improve decision making support for business process incurred cost reduction.

Business processes in dentistry are quickly evolving towards 'digital dentistry'. This means that many steps in the dental process will increasingly deal with computerized information or computerized half products. A complicating factor in the improvement of process performance in dentistry, however, is the large number of independent dental professionals that are involved in the entire process. In order to reap the benefits of digital dentistry, it is essential to obtain an accurate view on the current processes in practice. In this paper, so called process mining techniques are applied in order to demonstrate that, based on automatically stored data, detailed process knowledge can be obtained on dental processes, e.g. it can be discovered how dental processes are actually executed. To this end, we analyze a real case of a private dental practice, which is responsible for the treatment of patients (diagnosis, placing of implants and the placement of the final restoration), and the dental lab that is responsible for the production of the final restoration. To determine the usefulness of process mining, the entire process has been investigated from three different perspectives: (1) the control-flow perspective, (2) the organizational perspective and (3) the performance perspective. The results clearly show that process mining is useful to gain a deep understanding of dental processes. Also, it becomes clear that dental process are rather complex, which require a considerable amount of flexibility. We argue that the introduction of workflow management technology is needed in order to make digital dentistry a success.

This study proposes an improved particle swarm optimization method to find optimal power flow by using the power transmission loss as an objective function. In the literature, PSO is a well — known intelligent search method to handle the solution of optimal power flow problem. A novel scheme which is called Improved PSO (IPSO) is defined by modifying the initialization step of PSO algorithm and choosing the load bus voltages, generator active and reactive powers, line flow capacities as penalty functions in the objective function. PSO and IPSO-based optimal power flow solutions are compared with each other on IEEE 118 and 300 bus systems. According to the test results, the power loss obtained by IPSO-based solution has less power loss than PSO-based solution. This proposed method can be used to obtain faster desirable solutions and better power loss results for the optimal power flow problem in case of power loss minimization.

This paper presents an improved particle swarm optimization (PSO) method and applies it to the design of a hybrid asymmetric load modulated balanced amplifier (H-ALMBA) with high linearity. A description is provided of the H-ALMBA topology as well as the improved PSO method with individually self-adaptive hyperparameters (Improved PSO-ISH). The design process as well as the optimization procedure are described in detail. In addition to output power and efficiency, linearity performance, including AM/AM and AM/PM characteristics, is also considered during optimization. The performance of H-ALMBA optimized by the proposed method is presented and compared to the initial design, the ADS built-in optimizer, and other methods of PSO that are currently available. Due to the introduction of Latin hypercube sampling (LHS) and a secondary optimization step in the improved PSO-ISH method, the optimization performance has been greatly enhanced. According to the measured results, the H-ALMBA designed using the proposed method achieves a saturation drain efficiency (DE) between 59.5% and 64.8%, and a DE of 45.7%-58.1% in the 10dB output back-off state (OBO), with an output power of 41.5 dBm-44.2 dBm from 1.8GHz to 2.6GHz. The variation range of AM/AM is less than 4.1dB and that of AM/PM is less than 11.5°, demonstrating high linearity. Additionally, modulated signal tests are conducted. A 20-MHz 5G new radio (NR) signal with 10-dB peak-to-average power ratios (PAPRs) is used. A high level of linearity is achieved without the use of digital predistortion (DPD).

Process Mining is a research area that meets the gap between business processes and various IT systems. Most of the works in this area focus on the control flow perspective, while very few of them address the organizational aspect. The organization perspective of process mining supports the discovery of social network within organization by analyzing events logs recorded during real process execution. For process owners, it is very important to know how users perform their activities in process. In this paper, we introduce a process discovery method that combines an organizational perspective with probabilistic approach. Combining these two approaches we are able to fit distribution of users work and distribution of instance generation in process. We use different statistic methods like Cullen and Frey graph, Kolmogorov-Smirnov statistic test, Carmen-von-Mises statistic test and Anderson-Darling statistic test. After finding appropriate distribution we estimate its parameters. Research conducted and presented in this paper reveals that the information about users behaviour in process is significantly useful in further analysis: in simulations, to identify bottlenecks, to improve productivity of resource management and to identify task complexity in process.

An improved particle swarm optimization method with multistage inertia weights is proposed for the design of gradient coils for use in magnetocardiography systems. With this method, the design of the gradient coils is transformed into a constrained nonlinear objective optimization problem, and the particle swarm optimization method with multistage inertia weights is used to solve this problem and thus obtain optimized size parameters for the coils. Furthermore, through comprehensive use of several parameters in combination, including the magnetic field derivative, the target field information, and the optimization algorithm, a highly linear magnetic field is produced by considering the constraints of the coil structure and the turns ratio. Simulation results show that the linearity spatial deviation of the designed gradient coil is one order of magnitude lower than that of the Maxwell coil with a sphere of a radius of $0.5~R$ . Using the proposed method, two optimized gradient coils are manufactured for magnetocardiography system applications with magnetic fields that are matched well with those produced by the theoretical simulation model. Experimental results show that the maximum of the linearity spatial deviation of the proposed gradient coils reaches $2.2\times 10^{-3}$ and $1.4\times 10^{-3}$ along the $z$ -axis within ± $0.5~R$ , respectively.

Proposal of Organization Framework Model, using Business Processes and Hierarchical Patterns to provide Agility and Flexibility in Competitiveness Environments

Path planning of Unmanned Underwater Vehicle (UUV) is of considerable significance for the underwater navigation, the objective of the path planning is to find an optimal collision-free and the shortest trajectory from the start to the destination. In this paper, a new improved particle swarm optimization (IPSO) was proposed to process the global path planning in a static underwater environment for UUV. Firstly, the path planning principle for UUV was established, in which three cost functions, path length, exclusion potential field between the UUV and obstacle, and attraction potential field between UUV and destination, were considered and developed as an optimization objective. Then, on the basis of analysis traditional particle swarm optimization (PSO), the time-varying acceleration coefficients and slowly varying function were employed to improve performance of PSO, time-varying acceleration coefficients was utilized to balance the local optimum and global optimum, and slowly varying function was introduced into the updating formula of PSO to expand search space and maintain particle diversity. Finally, numerical simulations verify that, the proposed approach can fulfill path planning problems for UUN successfully.

In this paper, for the first time an Improved Particle Swarm Optimization (IPSO) algorithm, is developed to evaluate the 2.5-D basement of sedimentary basin and consequently to simulate its bottom, by using the density contrast that varies parabolically with depth simultaneously. The IPSO method is capable of improving the global search of particles in all of the search fields. Finding the optimum solution is adjusted by an inertia weight and acceleration coefficients. Here, we have examined the ability of the IPSO inversion by the synthetic gravity data due to a sedimentary basin, with and without noise. The calculated depth and gravity of the synthetic model do not differ too much from assumed values due to set limits for model parameters and are always within the range. Also, the mentioned method has been applied for the 2.5-D gravity inverse modelling of a sedimentary basin in Iran. We also have modelled the sedimentary basin in 2-D along seven profiles. Furthermore, using the depth values estimated by IPSO from all profiles, a 3-D model of the sedimentary basin was generated. The obtained maximum depth for this sedimentary basin is 2.62 km.

An optimization for injection of alkali/surfactant/polymer (ASP) flooding in oil recovery is considered in this paper. This optimal control problem (OCP) is formulated as a parameter identification system, where the objective function is revenue maximization and the governing equation is multiphase flow in porous media. We use the ASP concentrations and slug size as the control and give the pointwise constraint for the control. An improved particle swarm optimization (IPSO) which is a particle swarm optimization (PSO) with second-order oscillatory in velocity, is applied to solve the OCP. Finally, an example of the OCP for ASP flooding is exposed and the results show that the IPSO method is effective and feasible.

An efficient and simple method of parameter identification for semiconductor devices is presented in this paper. The method is based on improved particle swarm optimization (IPSO), which can overcome some deficiencies of standard PSO. A typical semiconductor device, Schottky-barrier diode (SBD), has been used as an example for demonstration. The performance of the IPSO was compared with the standard PSO and the genetic algorithm (GA) that is commonly applied to solve parameter identification problems of semiconductor devices. The comparative results indicate that the IPSO method can obtain optimum solutions more easily than others.

Business Process Management (BPM) has been increasingly used in recent years in the healthcare domain to analyze, optimize, harmonize and compare clinical and healthcare processes. The main aim of this methodology is to model the interactions between medical and organizational activities needed to deliver health services, measure their complexity, variability and deviations to improve the quality of care and its efficiency. Among the different tools, languages and notations developed in the decades, UML (Unified Modeling Language) represents a widely adopted technique to model, analyze and compare business processes in healthcare. We adopted its diagrams in the MOCHA project to compare the different ways of organizing, coordinating and delivering child care across 30 EU/EEA countries both from an organization and control-flow perspectives. This paper provides an overview of the main components used to represent the business process using UML diagrams, also highlighting how we customized them to capture the specificity of the healthcare domain taking into account that processes are reconstructed on the basis of country experts’ responses to questionnaires. The benefits of the application of this methodology are demonstrated by providing examples of comparing different aspects of child care.

The need to extend business process languages with the capability to model complex data objects along with the control flow perspective has lead to significant practical and theoretical advances in the field of Business Process Modeling (BPM).On the practical side, there are several suites for control flow and data modeling; nonetheless, when it comes to formal verification, the data perspective is abstracted away due to the intrinsic difficulty of handling unbounded data. On the theoretical side, there is significant literature providing decidability results for expressive data-aware processes. However, they struggle to produce a concrete impact as being far from real BPM architectures and, most of all, not providing actual verification tools. In this paper we aim at bridging such a gap: we provide a concrete framework which, on the one hand, being based on Petri Nets and relational models, is close to the widely used BPM suites, and on the other is grounded on solid formal basis which allow to perform formal verification tasks. Moreover, we show how to encode our framework in an action language so as to perform reachability analysis using virtually any state-of-the-art planner.