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In this paper, a simplified model of voltage source converter-based high-voltage direct current (VSC-HVDC) link is proposed that is effective in the analysis of multi-machine systems, even when crucial applications of the link are involved. The model is derived by eliminating the DC dynamics, including the converter-related impedances as a part of the AC system transmission network and obtaining the converter currents in a straightforward manner. Case studies are conducted on 4-machine, 10-bus and 16-machine, 68-bus systems to prove the accuracy of the model. The study clearly indicates the model's ability to reproduce the influence of VSC controllers, impact of variable power levels and effects of multiple HVDC links in a system. It is further verified for significant VSC-HVDC applications. The model is effective at handling frequency support of asynchronous systems and can be applied to VSC-HVDC connected offshore wind farms feeding multi-machine systems. It is demonstrated that the proposed model can be efficiently used for analysis of large AC systems embedded with VSC-HVDC links with lesser modeling complexity and computation time. [ABSTRACT FROM AUTHOR]

The power system stabilizer (PSS) is vital to enhance the dynamic stability of the electrical grid. The conventional PSS, designed at a fixed operating point may fail to retain the system stability in real life due to varying system conditions. Hence, it is essential to develop a robust stabilizer that guarantees system stability and gives the best performance under wide-range scenarios. This research work presents a novel method to design a robust PID-PSS utilizing a decentralized approach. The extended multi-machine power system is divided into subsystems in this study. To depict broad operating conditions resulting from widespread system conditions, a transfer function with interval coefficients is developed for each subsystem. The necessary and sufficient constraints for robust stability are derived using a systematic way, and a performance index is devised to improve PSS performance. Global search and fmincon tool in MATLAB is employed to identify robust controller gains. The proposed method's efficacy is demonstrated using a two-area, 10-bus, four-machine system. The system is tested for short circuit faults that are both symmetrical and asymmetrical, as well as load disturbances. The proposed controller's robustness and superiority over the existing are demonstrated by nonlinear simulation results. [ABSTRACT FROM AUTHOR]

Modern power system networks are inherently complex and susceptible to a range of undesirable events, such as line and generator outages, transmission line faults, and power oscillations. These power oscillations arise following disturbances, causing generators to oscillate in relation to one another. Effectively damping these oscillations is crucial for ensuring the reliable operation of the overall system. In light of this, our research employs a convolutional neural network (CNN) approach to fine-tune the parameters of a conventional power system stabilizer (PSS). The goal is to enhance the damping of power oscillations triggered by abrupt disturbances in a multi-machine system. In the process of training the neural network, input vectors are transformed into image vectors and subsequently trained using a CNN architecture. This trained network is then utilized to derive optimized PSS parameters from test data. The proposed CNN-based PSS is rigorously evaluated across a variety of operational scenarios, demonstrating its effectiveness in enhancing power oscillation damping in both single-machine infinite bus and multi-machine systems. Time-domain simulations are conducted to analyze rotor speed and rotor angle deviations within the system equipped with the proposed CNN-based PSS. Comparative assessments are made against systems employing different conventional PSS configurations and systems without PSS. The advantages of CNN-based PSS are its ability to capture complex patterns and relationships in the data, enabling it to effectively learn and adapt to various operating conditions, resulting in improved damping performance. Remarkably, our proposed PSS exhibits superior performance in terms of damping power oscillations, effectively outperforming conventional PSS approaches. [ABSTRACT FROM AUTHOR]

The escalating risks to network security, stemming from the continual evolution of network infrastructure and application systems, have significantly impacted system availability, with distributed denial of service (DDoS) attacks standing out as a particularly disruptive threat capable of overwhelming and even incapacitating systems. In response, this study introduces a comprehensive system (named uitSDD) designed to detect and mitigate DDoS attacks within software-defined networks (SDNs). Our proposed solution involves a multi-modules architecture operating as an intrusion prevent system, strategically engineered for seamless integration into the Controller device. The system’s functionality includes the periodic extraction of multiple parameters from flow entries on the OpenFlow switch, complemented by machine learning models, enabling the identification of abnormal packets infiltrating the system. Upon detecting an attack, the controller promptly dispatches policies to the OpenFlow switch, initiating the removal of malicious packets. This autonomous system employs common machine learning algorithms and real-time processes to detect and counteract attacks on OpenFlow Switches. The study evaluates the system’s performance using four distinct algorithms such as Decision Tree, K-Nearest neighbors (KNN), Random Forest, and support vector machine (SVM). Notably, when utilizing the CIC-DDOS2019 dataset to evaluate machine learning algorithms in detecting DDoS attacks, the Decision Tree algorithm exhibits the highest F1-score at 99.87%. [ABSTRACT FROM AUTHOR]

The optimal design of Power System Stabilizer (PSS) parameters is a significant optimization problem in power systems. Metaheuristic search (MHS) algorithms are among the most commonly used methods for optimizing PSS parameters. The preferred MHS algorithm must have strong exploration capability and an effective exploitation-exploration balance. The Fitness-Distance Balance (FDB) is a novel method for MHS algorithms, and it is featured by balanced search and effective diversity capabilities. In this paper, the exploration and balanced search capabilities of the Social Network Search (SNS) have been developed by using the FDB method. The FDB method guides the search process in the SNS and enables a more efficient selection of solution candidates in the search space. The performance of FDB-based SNS (FDBSNS) has been tested in the CEC-2014 and CEC-2017 benchmark test suits, and its superiority against SNS has been verified. Moreover, the effect of the FDBSNS was investigated in WSCC 3-machine 9-bus and 10-machine 39-bus New England test systems in the optimal PSS design problem. The obtained results and statistical analyses demonstrated that FDBSNS is a robust MHS algorithm compared to the algorithms in the literature for solving the CEC benchmark suits and optimal PSS design problem. [ABSTRACT FROM AUTHOR]

This paper addresses the joint optimization model of production scheduling, work-in-process (WIP) inventory control and group preventive maintenance (PM) planning in a multi-machine system with multi-components. The objective is to obtain optimum production sequence, PM intervals and grouping of components, which minimize the total expected cost per unit time of the system. A new meta-heuristic named Jaya algorithm and two popular algorithms viz. simulated annealing (SA) and particle swarm optimization (PSO) are applied to optimize the objective function. Initially, the optimum group of components is obtained based on the integer multiples of individual PM intervals. Secondly, the job permutation sequence incorporating group PM intervals is identified with the largest order value (LOV) rule. The shift in optimum PM intervals is realized with an advanced-postpone balancing approach. Computational results reveal that the proposed integrated model along with group PM yields up to 25% cost reductions when compared to the integrated model with individual maintenance as well as 37% savings while no integration is performed. Furthermore, the performance of algorithms is evaluated with large-sized problems. The obtained results show that Jaya and SA yielded comparable results, however, PSO is least productive. Thus, the proposed approach yields better economic performance and brings more improvised solutions as compared to the conventional methods of integrated scheduling and maintenance optimization problems. [ABSTRACT FROM AUTHOR]

This paper deals with simultaneous scheduling of machines, automated guided vehicles (AGVs) and tools in a multi-machine flexible manufacturing system (FMS) considering the transfer times of jobs to minimize makespan (MSN). Only one copy of each type of tools is made available due to economic restrictions, and the tools are stored in a central tool magazine (CTM) that shares with and serves for several machines. Jobs and tools among machines are carried by AGVs and tool transporter (TT). This simultaneous scheduling problem is highly complex in nature as it involves job operations sequencing on machines, allocation of tools and AGVs to job operations and associated trip operations including the times of dead heading trip and loaded trip of AGVs. This paper presents a nonlinear mixed integer programming (MIP) formulation to model the combined scheduling of machines, AGVs and tools and a flower pollination algorithm (FPA) built on the flowers' pollination process for solving this simultaneous scheduling problem. The results indicate the proposed FPA is superior in terms of final solution quality, efficiency, convergence rate and robustness. [ABSTRACT FROM AUTHOR]

The dependence was studied of the level of density fluctuations recorded at different devices on the minor and major radii of the tokamak. It is known that a high level of density fluctuations can negatively affect the operation of diagnostics, in particular, the plasma reflectometry diagnostic. The increase of density fluctuations decreases the quality of raw data by increasing the error of measuring the density profile and, when density fluctuations exceed the threshold value, it renders such measurements impossible. Based on experimental data obtained on devices with substantially different sizes, a dependence of the density fluctuation level on the major and minor tokamak radii was proposed. Since the main experiments were carried out in round limiter tokamaks in ohmic (OH) heating regimes, the extrapolation result is applicable, generally speaking, to installations of larger size with the same configuration and regimes. However, experiments with electron cyclotron heating at the T-10 tokamak also allow one to extend the obtained dependence to regimes with auxiliary heating. It was shown that the obtained dependence is applicable to limiter tokamaks Tore Supra and TFTR of larger size. The applicability of the dependence to installations with diverter configuration is discussed and the possible level of density fluctuations is extrapolated to the international reactor tokamak ITER that is being constructed in France. [ABSTRACT FROM AUTHOR]

In the field of seismic signal analysis, it is of utmost importance to accurately differentiate between earthquakes and underground nuclear explosions. As a contribution for the verification regime of the Comprehensive Nuclear Test Ban Treaty (CTBT), Various methods have been employed for this purpose, including Complexity, Spectral ratio, mb—Ms (body wave and surface wave magnitudes), and corner frequency of P and S waves. These discrimination techniques have been examined to manually identify natural seismic events from nuclear explosions across different regions worldwide, such as China, India, Pakistan, North Korea, and the United States. To gather the necessary data, a comprehensive dataset comprising nuclear explosions and earthquakes of the same magnitude range (4 ≤ mb ≤ 6.5) of 35 seismic events from 1945 to 2017 has been compiled from the International Research Institute for Seismology (IRIS) using broadband and long period seismic stations. The objective of this study is to employ a range of linear and nonlinear Machine Learning (ML) models with the aim of automatically distinguishing between underground nuclear explosions and large earthquakes to enhance the accuracy of manual feature extraction. For this purpose, time domain waveforms and different classifier techniques focused on feature extraction have been used. The ML models employed include logistic regression, K-nearest neighbours classifier, decision tree classifier, random forest classifier, voting classifier, and Naive Bayes. The outcomes of the ROC and AUC analyses were employed to validate the validity of our proposed discrimination algorithm. The results show that the Random Forest Classifier is the most effective model, obtaining 100% accuracy in the case of feature extraction, while the best model for the time domain waveform classifier that achieved 75.5% accuracy is the voting classifier. [ABSTRACT FROM AUTHOR]

A cutting-plane approach is developed for the problem of optimally scheduling jobs with arbitrary precedence constraints on unrelated parallel machines to minimize weighted completion time. While the single machine version of this problem has attracted much research efforts, enabling solving problems with up to 100 jobs, not much has been done on the multiple machines case. A novel mixed-integer programming model is presented for the problem with multiple machines. For this model, many classes of valid inequalities that cut off fractional linear programming solutions are developed. This leads to an increase of the linear programming lower bound from 89.3 to 94.6% of the corresponding optimal solution, and a substantial reduction in the computational time of an optimal branch-and-bound algorithm for this problem. This enables us to report optimal solutions for problem instances with up to 25 jobs and 5 machines, which is more than twice the size of problems for which optimal solutions have been reported in the literature thus far. For a special case of the problem—that of minimizing makespan—application of our model helps solve 18 of 27 previously unsolved problem instances to optimality. [ABSTRACT FROM AUTHOR]

Stabilization of wind farm (WF) is considered one of the major serious issues for developing power systems based on renewable energy sources. A comparison of dynamic performances for FACTS devices like STATCOM and SVC is presented in this paper. Such devices are employed to stabilize WF integrated with multi-machine power system (WFMPS). Fuzzy logic controller (FLC) has been employed for controlling FACTS devices to enhance WFMPS performance under different abnormal conditions. Three-phase to ground short circuit fault has been taken into consideration in different locations of system. MATLAB/Simulink is used to perform the modeling and simulation of WFMPS. The obtained results demonstrate the effect of both SVC- and STATCOM-based FLC on damping the system oscillations and improving the system dynamic performance in the post-fault duration. The comparison confirms superior dynamic performance and fast fault recovery of STATCOM-based FLC through different fault conditions compared with SVC-based FLC. [ABSTRACT FROM AUTHOR]

A system development case study problem based on a set of aircraft landing gear is examined in Hybrid Event-B (an extension of Event-B that includes provision for continuously varying behaviour as well as the usual discrete changes of state). Although tool support for Hybrid Event-B is currently lacking, the complexity of the case study provides a valuable challenge for the expressivity and modelling capabilities of the Hybrid Event-B formalism. The size of the case study, and in particular, the number of overtly independent subcomponents that the problem domain contains, both significantly exercise the multi-machine and coordination capabilities of the modelling formalism. These aspects of the case study, vital in the development of realistic cyberphysical systems in general, have contributed significant improvements in the theoretical formulation of multi-machine Hybrid Event-B itself. [ABSTRACT FROM AUTHOR]

Power system stabilizers (PSSs) are generally used to solve the low-frequency oscillation problem. To overcome this type of oscillation, this paper presented a new method by using the genetic algorithm (GA) to identify PSS settings. The tuning of PSS parameters was formulated relying on an eigenvalue-based objective function aiming at maximizing the stability margin. This was achieved by increasing the sum of the squares of negative real parts of the system eigenvalues. The small disruptions in the form of load change take place routinely; the controller parameters should be adjusted to the changing conditions. To remedy this defect, an adjustment of parameters as a function of the load was required. Thus, a neuronal model by using a historical database determined by the GA solutions for various load levels can approximate the simulation studies, since it could estimate the stabilizer parameters in real time after the learning phase. The validity of the proposed technique was checked through the evolution simulation of the regulator parameters for a load forecast curve. These concepts were discussed in details on a multi-machine network Western System Coordinating Council (WSCC) comprising three generators and nine nodes. The eigenvalue analysis and time domain simulation results were presented at different operating conditions and under various disturbances in order to show the effectiveness of this study. [ABSTRACT FROM AUTHOR]

Recently, the flow shop scheduling problem under blocking has gained broad attention in academic fields. Various papers have been devoted to investigate this issue and have been mostly restricted to the treatment of single objective at a time. Nevertheless, in practice the scheduling decisions often involve simultaneous consideration of multiple objectives (usually contradicting) to give more realistic solutions to the decision maker. In this study, we deal with a bi-objective blocking permutation flow shop scheduling problem where the makespan and total completion time are considered as objective functions. Both measures lead to an NP-hard problem. Our interest is to propose for the first time a Genetic Algorithm based on NSGA-II for searching locally Pareto-optimal frontier for the problem under consideration. The individuals in the algorithm are represented as discrete job permutations. Some specific versions of the NEH heuristic are used to generate the initial population. Non-dominated solutions and differences among parents are taken advantage of when designing the selection operator. The efficiency of the proposed algorithm, based on various metrics, is compared against the multiobjective evolutionary algorithm SPEA-II. [ABSTRACT FROM AUTHOR]

Linear quadratic stabilizers are well-known for their superior control capabilities when compared to the conventional lead-lag power system stabilizers. However, they have not seen much of practical importance as the state variables are generally not measurable; especially the generator rotor angle measurement is not available in most of the power plants. Full state feedback controllers require feedback of other machine states in a multi-machine power system and necessitate block diagonal structure constraints for decentralized implementation. This paper investigates the design of Linear Quadratic Power System Stabilizers using a recently proposed modified Heffron-Phillip's model. This model is derived by taking the secondary bus voltage of the step-up transformer as reference instead of the infinite bus. The state variables of this model can be obtained by local measurements. This model allows a coordinated linear quadratic control design in multi machine systems. The performance of the proposed controller has been evaluated on two widely used multi-machine power systems, 4 generator 10 bus and 10 generator 39 bus systems. It has been observed that the performance of the proposed controller is superior to that of the conventional Power System Stabilizers (PSS) over a wide range of operating and system conditions. [ABSTRACT FROM AUTHOR]

Modeling of unified power flow control (UPFC) into multi-machine power systems by Wang, published by IET, has been widely quoted by many researchers. However, the correctness of the derivation of its equations has not been strictly scrutinized. The model cannot properly represent power system networks in its current form and need to be corrected. In this paper, the model proposed by Wang is revisited and the equations re-derived obtaining different results. The corrected model was developed and tested on three-machine and New England power systems installed with a UPFC. The DC link capacitor voltage is controlled in UPFC by modulating the phase angle of the excitation transformer voltage. The effect of the controller in damping of the low-frequency oscillations is demonstrated by using the proposed correct model and the results are compared with model presented by Wang. [ABSTRACT FROM AUTHOR]

This paper investigates a multi-product multi-machine production-inventory system, characterized by job shop routings and stochastic demand interarrival times, set-up times and processing times. The inventory points and the production system are controlled integrally by a centralized decision maker. We present a heuristic that minimizes the relevant costs by making near-optimal production and inventory control decisions while target customer service levels are satisfied. The heuristic is tested in an extensive simulation study and the results are discussed. [ABSTRACT FROM AUTHOR]

The due date assignment has become a challenging issue related to the interaction between various participants of the supply chain. In this paper, we consider the total earliness and tardiness penalty scheduling simultaneously with the deterioration effects and maintenance activities on an unrelated parallel machine setting. We aim to determine jointly the optimal maintenance activity positions, the optimal common due date for all the jobs, and the optimal schedule to minimize the sum of the earliness and tardiness costs. If the number of machines is fixed, we introduce a polynomial time algorithm for solving the problem. We also propose an efficient polynomial time algorithm with a lower order time complexity for finding the optimal solution of a special case of the problem. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of scheduling a set of independent jobs with sequence-dependent setups and distinct due dates on non-uniform multi-machines to minimize the total weighted earliness and tardiness, and explores the use of artificial neural networks as a valid alternative to the traditional scheduling approaches. The objective is to propose a dynamical gradient neural network, which employs a penalty function approach with time varying coefficients for the solution of the problem which is known to be NP-hard. After the appropriate energy function was constructed, the dynamics are defined by steepest gradient descent on the energy function. The proposed neural network system is composed of two maximum neural networks, three piecewise linear and one log-sigmoid network all of which interact with each other. The motivation for using maximum networks is to reduce the network complexity and to obtain a simplified energy function. To overcome the tradeoff problem encountered in using the penalty function approach, a time varying penalty coefficient methodology is proposed to be used during simulation experiments. Simulation results of the proposed approach on a scheduling problem indicate that the proposed coupled network yields an optimal solution which makes it attractive for applications of larger sized problems. [ABSTRACT FROM AUTHOR]

According to the extant literature, improving the leanness of a production system boosts a company's productivity and competitiveness. However, such an endeavor usually involves managing multiple, potentially conflicting objectives. This study proposes a framework that analyzes lean production methods using simulation and data envelopment analysis (DEA) to accommodate the underlying multi-objective decision-making problem. The proposed framework can help identify the most efficient solution alternative by (i) considering the most common lean production methods for assembly line balancing, such as single minute exchange of dies (SMED) and multi-machine set-up reduction (MMSUR), (ii) creating and simulating various alternative assembly line configuration options via discrete-event simulation modeling, and (iii) formulating and applying DEA to identify the best alternative assembly system configuration for the multi-objective decision making. In this study, we demonstrate the viability and superiority of the proposed framework with an application case on an automotive spare parts production system. The results show that the suggested framework substantially improves the existing system by increasing efficiency while concurrently decreasing work-in-process (WIP). [ABSTRACT FROM AUTHOR]

Contemporary large-scale and systematic agricultural operations demand the collaborative efforts of multiple agricultural machines with distinct functionalities. However, the failure of a single agricultural machine during collaborative operations jeopardizes the entire undertaking. To address this challenge, this paper proposes a multi-machine collaborative dynamic job allocation method based on the improved ant colony algorithm. Initially, the improved ant colony algorithm is employed to determine the optimal solution for harvester scheduling. This solution is then fed into the data conversion algorithm to acquire the necessary unloading point information for transport vehicle scheduling. Subsequently, the improved ant colony algorithm is once again utilized to optimize the transport vehicle scheduling. In cases of agricultural machinery failure or changes in the operating environment, two distinct methods are employed based on the situation. The first involves double-layer rescheduling of both harvester and transport vehicles, while the second employs single-layer rescheduling exclusively for the transport vehicles, yielding the respective rescheduling results. The outcomes demonstrate that the proposed solution method effectively identifies the current optimal scheduling plan for both the harvester and transport vehicle in the event of malfunctions. Moreover, under the premise that the unproductive waiting time of the harvester is reduced to zero, and the number of transport vehicles is minimized, it achieves the minimization of operating time cost and transportation cost. This method exhibits significant potential for seamless integration into the practical application of unmanned farms, providing a foundation for addressing scheduling and management challenges in multi-agricultural machinery collaborative operations within complex farmland operating environments. [ABSTRACT FROM AUTHOR]

Power systems exhibit nonlinearity. causing dynamic instability and complex power oscillations. This research proposes an innovative strategy using the Novel Bat Algorithm (NBA) to achieve ideal Power System Stabilizers (PSSs) in a multimachine power system. The approach shifts electromechanical modes to specific areas in the s-plane. Enhancing the multi-machine power system and establishing stabilizer parameters for dynamic performance. The study examines the designed approach aptitude for standard lead-lag PSSs configurations. In order to elevate the global search problem and transfer some static operators for the optimum optimization process. the chaos mapping. also known as CNBA. is introduced into NBA. Four different forms of chaos maps are compared in experiments to resolve unconstrained mathematical issues in order to illustrate CNBA performance. In any other case. the challenge of designing PSS under a wide range of loading situations is transformed into an optimization challenge with the damping ratio of electromechanical modes with low damping as the target function. The optimal stabilizers' gains are gotten by employing the CNBA algorithm. Second plan. an effective technique is astutely established to delineate the PSS location and quantity using CNBA and another side using participation factor. To examine the efficacy of the proposed CNBA-based PSS on a large system; it is tested on the interconnected of New-England/New-York (16 generators and 68 buses) power grid. and verified by comparative study with NBA through eigenvalue analysis and nonlinear simulation to provide evidence the algorithmic competence of CNBA. The CNBA approach yields a minimum damping ratio of 37%. which is consistent with the its eigenvalue. In contrast, the NBA approach achieves a minimum damping ratio of 31%. The simulation results reveal the fine performance of the proposed CNBA-PSS in a convincing manner and its capacity to provide an excellent damping for inter-area and local oscillations under diverse operating cases compared to NBA-PSS then in the case of PSS location. [ABSTRACT FROM AUTHOR]

Multi-machine, single-inverter induction motor drives are attractive in situations in which all machines are of similar ratings, and operate at approximately the same load torques. The advantages include small size compared to multi-inverter system, lower weight and overall cost. However, field oriented control of such drives is a challenge since no two motors will have exactly the same operating conditions at any time. In general, at least some motors in the system will operate away from perfect field orientation. It is therefore important to analyse their torque dynamics carefully. This paper discusses existing multi-machine field-oriented control methods, and analyses and compares them from the viewpoint of dynamic performance. For this, an analytical framework based on small-signal analysis is developed to compare multi-machine control methods. This analysis is verified by simulations and experiments. [ABSTRACT FROM AUTHOR]

Objectives: The Image Biomarker Standardization Initiative has helped improve the computational reproducibility of MRI radiomics features. Nonetheless, the MRI sequences and features with high imaging reproducibility are yet to be established. To determine reproducible multiparametric MRI radiomics features across test–retest, multi-scanner, and computational reproducibility comparisons, and to evaluate their clinical value in brain tumor diagnosis. Methods: To assess reproducibility, T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) were acquired from three 3-T MRI scanners using standardized phantom, and radiomics features were extracted using two computational algorithms. Reproducible radiomics features were selected when the concordance correlation coefficient value above 0.9 across multiple sessions, scanners, and computational algorithms. Random forest classifiers were trained with reproducible features (n = 117) and validated in a clinical cohort (n = 50) to evaluate whether features with high reproducibility improved the differentiation of glioblastoma from primary central nervous system lymphomas (PCNSLs). Results: Radiomics features from T2WI demonstrated higher repeatability (65–94%) than those from DWI (38–48%) or T1WI (2–92%). Across test–retest, multi-scanner, and computational comparisons, T2WI provided 41 reproducible features, DWI provided six, and T1WI provided two. The performance of the classification model with reproducible features was higher than that using non-reproducible features in both training set (AUC, 0.916 vs. 0.877) and validation set (AUC, 0.957 vs. 0.869). Conclusion: Radiomics features with high reproducibility across multiple sessions, scanners, and computational algorithms were identified, and they showed higher diagnostic performance than non-reproducible radiomics features in the differentiation of glioblastoma from PCNSL. Clinical relevance statement: By identifying the radiomics features showing higher multi-machine reproducibility, our results also demonstrated higher radiomics diagnostic performance in the differentiation of glioblastoma from PCNSL, paving the way for further research designs and clinical application in neuro-oncology. Key Points: • Highly reproducible radiomics features across multiple sessions, scanners, and computational algorithms were identified using phantom and applied to clinical diagnosis. • Radiomics features from T2-weighted imaging were more reproducible than those from T1-weighted and diffusion-weighted imaging. • Radiomics features with good reproducibility had better diagnostic performance for brain tumors than features with poor reproducibility. [ABSTRACT FROM AUTHOR]

This paper introduces Simulink-based programs developed for dynamic analysis of electrical power systems. The program can be used for research studies or as a teaching tool. With the program, time-domain simulation, modal analysis, participation factor analysis and visualization, frequency response analysis, and design of conventional and intelligent controllers can be obtained. A special case of constant impedance load is also studied. IEEE 9-bus, IEEE 68-bus, Texas 2007-bus 282-machine, and the 25,000-bus northeastern US test systems are employed in this paper. The synchronous machines are assumed to be equipped with exciter, turbine, and stabilizer. Static var compensator is added using conventional and adaptive neuro-fuzzy controllers. Different types of disturbances are applied to the systems including generator-side and network-side disturbances. The program is free of algebraic loops that may increase the errors and slow down the simulation. All blocks and signals in the Simulink model are in vector form that can be used to simulate a power system of any size. [ABSTRACT FROM AUTHOR]

Directed energy deposition (DED) technology is gaining traction in the biomedical and aviation industries due to its ability to deposit a variety of materials, create complex structures, and achieve high processing efficiency. 316L stainless steel, known for its exceptional oxidation resistance, mechanical properties, and corrosion resistance, finds extensive applications in the nuclear power sector. However, there is a dearth of literature on tool wear when machining additively manufactured alloys, especially under dry and thermal cutting conditions. These conditions are particularly relevant for hybrid additive and subtractive manufacturing (HASM), which aims to reduce clamping steps and energy consumption. Thermal milling appears to be an effective solution for streamlining the processing complexity and enhancing the bonding strength of the HASM interface. This paper aims to reduce energy consumption associated with the complex and time-consuming multi-machine tool machining process. Additionally, it seeks to evaluate the mechanisms of tool wear during the milling of DED 316L stainless steel under dry and thermal conditions using a TiAlN-coated tool at various cutting speeds. The study investigates the effects of milling temperature, number of cutting edges, and milling speed on tool wear, surface roughness, and surface residual stress. Analysis techniques such as 3D optical profilometry are employed to assess abrasive and adhesive wear. The results obtained demonstrate that cooling-free HASM can reduce clamping time and energy consumption. Quadruple-edged tools operating at 3000 r/min exhibit superior milling performance compared to double-edged tools at 2000 r/min. Furthermore, the entire tool wear process is analyzed using parameters such as Ra, Sa, and surface morphology. Examining the impact of high-temperature workpiece milling parameters on tool wear provides valuable insights for producing additive manufacturing parts with superior performance and surface quality. [ABSTRACT FROM AUTHOR]

Due to diminishing system inertia, system operators currently mandate solar photovoltaic (PV) systems to participate in frequency control. In the literature, the standard approach for achieving this has been to combine a PV system with energy storage. However, it has a number of technical and financial drawbacks. With the goal of providing power reserve control (PRC) and allowing PV systems to participate in frequency regulation, this article offers a novel storage-free master-slave-based power control technique for solar photovoltaic (PV) systems. A substantially small-rated master PV operating in maximum power point (MPP) mode estimates the irradiance and temperature online, which are further utilised to evaluate the entire PV curve of the slave PVs. A novel adaptive voltage control mechanism (inner control) is designed for rapid conversion of a power instruction into an appropriate voltage instruction for the linking DC-DC converter. The proposed method merges the MPP and PRC operating regimes into a single integrated regime, enabling a seamless transition between the modes. Additionally, an inertial and damping control scheme is integrated with the proposed PV system, with active power reserves maintained to modulate the PV power reference of the inner controller in response to system requirements. The proposed control strategy is verified on a three-area test system and the Kundurs multi-machine test system in order to quantify the impact on system performance. The findings show that the suggested control method significantly lowers frequency and tie-power variations. [ABSTRACT FROM AUTHOR]

Utility Grid outages may affect the industrial power system load-generation balance and cause rapid frequency decays particularly due to low inertia and small spinning reserve. Frequency stability is an essential perspective for the invulnerable operation of islanded industrial power systems with in-plant generation. Multi-machine system frequency response (MMSFR) model with the dynamic of governors, overall inertia of the system (generators and loads), and load damping is considered to find the approximate value of load shedding (LS) amount. The frequency variation of the islanded system is evaluated for various operating scenarios with a calculated approximate LS amount to determine the optimum amount of LS. In this paper, an artificial neural network (ANN)-based optimum and adaptive LS technique have been presented. The total in-plant generation, spinning reserve, total power import, total demand, and frequency decay rate have been selected as the input neurons of the ANN. The regrouping particle swarm optimization (RegPSO) algorithm has been adopted to obtain more accurate and faster training of the ANN. The oil refinery power distribution system with in-plant generation is considered to evaluate the performance of the proposed RegPSO-ANN load shedding algorithm. The performance of the proposed scheme is evaluated in comparison with the under-frequency relay-based conventional load shedding scheme and Levenberg–Marquardt back-propagation ANN-based LS scheme. [ABSTRACT FROM AUTHOR]

This paper presents a scheme for power swing detection and stability assessment for multi-machine power systems in addition to a brief survey on that topic. The scheme adopts smart grid perception. It is an asynchronous scheme that uses the rate of change of the angle of the generator bus voltage instead of synchronously estimating the absolute rotor angle value for stability assessment. Therefore, it does not require phasor measurement units (PMUs). Instead, it uses commercially available intelligent electronic devices (IEDs). It introduces a new application of the multicast Generic Object Oriented Substation Event (GOOSE) protocol or its routable version (R-GOOSE) to reflect the disturbance in the power grid on the transmission pattern of data packets over the grid data network. In this scheme, the angle change information is transmitted in the form of GOOSE packets simultaneously from the IEDs that readily measure the angles of the affected generators. On the other hand, the detecting device identifies the stability status of each affected generator. The proposed scheme is validated by simulating disturbances on the IEEE 39-bus system. The results show that the scheme can discriminate between stable and unstable swings. In addition, the loss of synchronism of the unstable generator is predicted ahead of its occurrence by enough time to take an appropriate action. [ABSTRACT FROM AUTHOR]

Low-frequency oscillations (LFO) are a significant problem for multi-machine electrical power system (EPS). These oscillations are undesirable as they reduce the power transfer capability of the transmission line and thus directly influence competitive electrical markets. The power system stabilizers (PSS) play a vital role in damping low-frequency oscillations to improve the dynamic stability of the power system. However, these controllers must be tuned in a coordinated and robust manner for effective performance. PSS tuning is characterized by a complex optimization problem that may involve hundreds of variables. The tuning procedure is modeled as an optimization problem which aims at maximizing the damping ratio coefficients of the closed-loop power system considering multiple operating points. In this context, this article employs and compares three metaheuristics with quantum characteristics, namely quantum particle swarm optimization (QPSO), quantum flower pollination algorithm (QFPA) and quantum gray wolf optimizer (QGWO). The aforementioned metaheuristics are used in PSS tuning in four test systems with 5 generators–7 buses, 10 generators–39 buses, 50 generators–616 buses and 170 generators–3584 buses. The results obtained by the quantum algorithms are compared through statistical indices and boxplot. The optimization results show that the QFPA and QGWO provide better system damping than the QPSO for the large electrical system. [ABSTRACT FROM AUTHOR]

This paper presents a new multi-machine robust control based on an electric differential system for electric vehicle (EV) applications which is composed of four in-wheel permanent magnet synchronous motors. It is based on a new master-slave direct torque control (DTC) algorithm, which is used for the control of bi-machine traction systems based on a speed model reference adaptive system observer. The use of an electric differential in the design of a new EV constitutes a technological breakthrough. A classical system with a multi-inverter and a multi-machine comprises a three-phase inverter for each machine to be controlled. Another approach consists of only one three-phase inverter for several permanent magnet synchronous machines. The control of multi-machine single-inverter systems is the subject of this study. Several methods have been proposed for the control of multi-machine single-inverter systems. In this study, a new master-slave based DTC strategy is developed to generate an electric differential system. The entire system is simulated by Matlab/Simulink. The simulation results show the effectiveness of the new multi-machine robust control based on an electric differential system for use in EV applications. [ABSTRACT FROM AUTHOR]

The BOUT++ transport code is run to study the effects of plasma drifts on the divertor out-in asymmetries (DOIAs) of particle and heat fluxes and their decay widths for EAST lower single null H-mode discharges. The diamagnetic drift seems to have no effects on the DOIAs of total particle and heat fluxes due to its divergence-free nature. However, it could significantly increase the DOIAs of peak particle and heat fluxes and the flux decay widths. The E × B drift is found to induce a large plasma flow to the divertor region, enhancing the DOIAs of both total and peak particle and heat fluxes and the flux decay widths. Both the radial and poloidal components of the E × B drift are necessary in increasing the DOIAs, however, the radial E × B drift seems to play a more important role. The effects on the DOIAs caused by both diamagnetic and E × B drifts are reversed with the reverse of toroidal magnetic field. The heat flux decay width λq and spreading width Sq are important physical and engineering parameters for the divertors and could be obtained by fitting the heat flux profiles at divertor targets. The λq at the outer target from the simulation case with all drifts could well match with the multi-machine scaling proposed by Eich and the DOIA of λq is in reasonable agreement with the scaling proposed by Goldston. [ABSTRACT FROM AUTHOR]

The paper demonstrates a comprehensive performance assessment of the two metaheuristic swarm-based optimization algorithms namely PSO (Particle swarm optimization), BFOA (Bacterial foraging optimization algorithm), and the hybrid PSO-BFOA optimizer for the alleviation and control of the power oscillations in a two-area four generator system integrated with a large-scale PV-farm. After sunset, the PV-plant operates as VSC (Voltage Source Converter)-STATCOM (Static synchronous compensator) using its overall inverting capabilities for the power system stability improvement. While in the daytime during the faults, the PV-farm immediately stops the active power production and behaves as PV-STATCOM until the normal operating conditions are resumed. The modified version of Kundur's two-area system comprising of a large-scale PV-farm is simulated with MATLAB software. An innovative control strategy employing the two PI controllers distinctly controls the DC-AC currents of the PV-STATCOM. The series compensation is set to an optimal value of 85% and subjected to a 3-φ fault. Zero mechanical dampings, along with extra disturbances of 20% variation in reference voltage and electromagnetic torque are introduced to flaunt the worst damping scenarios. The simulation outcomes and time-domain analysis for various test conditions: without a controller, with PSO-based PV-STATCOM, with BFOA-based PV-STATCOM, and with the Hybrid PSO-BFOA-based PV-STATCOM, reveal that all the system modes are stabilized with PSO application. The stability of modes is progressively improved with BFO control, eventually, the modes are optimally stabilized by deploying the hybrid PSO-BFO algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization procedure based on eigenvalues to carry out the stabilization function of the Gate-Controlled Series Capacitor (GCSC) in a power system. It is aimed to provide a reliable damping framework by means of a GCSC based multi-objective damping controller. The proposed method employs Particle Swarm Optimization (PSO) to search for optimal parameter settings of a widely used multi-objective lead-lag damping controller. The eigenvalue analysis is considered as the cornerstone of the performed studies in order to investigate the multi-objective methodology in which the unstable or lightly damped modes are scheduled to effectively shift to some prescribed stability zones in the s-plane. The effectiveness of the suggested approach in damping local and interarea oscillations modes in a multi-machine power system, over a wide range of loading conditions, is confirmed through eigenvalue analysis and time simulation. [ABSTRACT FROM AUTHOR]

In a few of the isolated DC power-transmission systems with hydropower units (and the associated water hammer effect), inappropriate governor control parameters may weaken the system damping and stability. It has the potential to result in ultra-low-frequency oscillations (ULFO) which is below 0.1 Hz. To carry out this issue, a linearized state-space model of a multi-machine system that includes hydropower and steam turbine governor control systems is presented in this paper. The oscillation mode of ULFO about the damping characteristics of the governor control system is analyzed by the damping torque analysis method. A governor power system stabilizer (GPSS) design model predicated on phase compensation principle to heighten the damping of the governor control system to subdue ULFO is planned. To verify its effectiveness, the designed GPSS is applied to a single-machine system, a 4-machine 2-area system as well as the Yunnan power grid system of China. The simulation results demonstrate that GPSS effectively suppresses ULFO with heightened ULFO damping by the optimized settings of governor control parameters. [ABSTRACT FROM AUTHOR]

With the defects of LED chip as the research object, in LED chip defect recognition, an efficient and scalable parallel algorithm is critical to the deep model using a large data set training. As the implementation of parallel in multi-machine is relatively difficult, the paper puts forward a recognition method based on convolution neural network algorithm, so as to improve the small batch stochastic gradient descent algorithm that is very popular in the industry. This method has overcome the shortcomings of the existing defects recognition algorithms: It requires to manually extract features and needs a heuristic method. Furthermore, this method has made the following improvements to traditional methods: (1) It adds a "copy of model parameters" of critical resources to reduce the waiting time of a GPU in requesting for model parameters in the process of model parameters updating; (2) distribution mechanism of small batch data is designed, which, training set is some small batch data that are randomly distributed at GPU side, and is selected by using the critical resource p variable; (3) a certain amount of memory space storage gradients are created on the GPU side and the parameter server side, and the gradient propagation is passed through a "gradient distribution" thread for scheduling. The experimental data show that the recognition rate of network for short-shot defects has reached 99.4%. In addition, compared with BP neural network, it can be seen from the experiment that the recognition rate of the proposed method is significantly better than that of BP neural network, so the method has a good application prospect. [ABSTRACT FROM AUTHOR]

Low-frequency electromechanical oscillations damping is powerfully crucial in power system operation. In order to fulfill this requirement, power oscillation damping (POD) controllers are installed in synchronous generators and flexible alternating current transmission system devices. These controllers can have either a conventional fixed structure composed by stages of gain and phase compensation or a multi-band modern structure (PSS4B) composed by three bands that correspond to a specific frequency range (low, intermediate and high frequency). In the PSS4B structure, each band consists of two branches based on differential filters (with a gain, lead–lag blocks and a hybrid block). This paper investigates the application of PSS4B as a POD controller for the static VAR compensator (PSS4B-SVC-POD) to damp oscillations in multi-machine power systems. The proposed PSS4B-SVC-POD and power system stabilizers (fitted on generators) are simultaneously designed through an optimization approach aiming at maximizing the closed-loop damping ratio. Modal analysis, frequency responses and time simulations for the well-known two-area four-generator power system show the good performance of the proposed controller fitted on static VAR compensator. [ABSTRACT FROM AUTHOR]

The paper introduces a new automatic drilling and riveting system for aircraft panel assembly which is composed of two five-axis machines in coordination. Kinematic modeling and base frame calibration are fundamental problems for the dual-machine system to improve its positioning accuracy. In this paper, a comprehensive kinematic model for the dual-machine system is established based on the modularization concept, and a new base frame calibration method is proposed which consists of the direct calibration based on laser tracker and calibration error compensation based on a specially designed instrument. In the kinematic modeling stage, the kinematic model of the dual-machine system is divided into three parts and three corresponding sub-models are established. With the pose constraints of coordinated machines, a comprehensive kinematic model of the dual-machine system is built. In the base frame calibration stage, the base frames of dual machines are firstly established by laser tracker. Due to many error sources in the calibration process, the relative pose errors of dual machines exist with the relationship between the base frames of coordinated machines. Then, the calibration error is measured by a specially designed instrument and used to modify the primary base frame calibration results. To verify the validity of the proposed method, experiments have been performed and the results have shown great improvement in base frame calibration accuracy. With the advantage of easy operation and high accuracy, this method can be extended to other multi-machine and multi-robot systems. [ABSTRACT FROM AUTHOR]

The selectivity property of distance relay is affected due to the operation of power swing blocking element which is used to prevent the false tripping imposed by power swing. Therefore, the fault detection algorithm is required to overcome such type of problems. Further, the fault detection during power swing for a series-compensated line is more challenging as the metal oxide varistor protecting the series capacitor imposes other frequency components and adds transients in the voltage and current signals. To resolve such protection challenges, this paper proposes an integrated approach for fault detection, which is based on Teager-Kaiser energy operators of instantaneous zero sequence voltage (Scheme 1) and phasor of negative sequence current (Scheme 2). Instantaneous zero sequence voltage appears only for unsymmetrical ground faults, and it is not affected by current transformer saturation. Other types of faults such as symmetrical, line-to-line faults are detected by Scheme 2, which is immune to capacitor coupling voltage transformer transients. However, both the schemes cover all fault scenarios. The main advantage of proposed scheme is that it is independent of the threshold. It is also not influenced by voltage and current inversions imposed by series capacitor and can detect the fault within 3 ms. The proposed scheme is validated using voltage and current data obtained by simulating 400 kV, 50 Hz, 9-bus multi-machine system using PSCAD/EMTDC. Further, to validate the proposed scheme in real time, the experiments are also carried out on real-time digital simulator. [ABSTRACT FROM AUTHOR]

In this paper, we consider a special variant of a capacitated dynamic lot sizing problem which, despite its practical relevance, has not been treated sufficiently in the literature. In addition to the significant complexity introduced by the sequence dependency of setup times, the problem is further complicated by the fact that there is only one single setup operator which is responsible for all setups on all machines. Hence, the multi-machine problem cannot be decomposed into multiple single machine problems, as the setup operations must be coordinated among the different machines. In addition, lots are produced in batches whereby the processing time of a lot is a step-wise function of the number of batches per lot. Due to perishability and quarantine issues, time windows for the production are given. We formulate a big-bucket lot sizing model and apply MIP-based heuristics to two industrial data sets. [ABSTRACT FROM AUTHOR]

Complex equipment system has the characteristics of time-varying, hierarchy, certain redundancy, and limited fault diagnosis experience, which makes it difficult for traditional diagnosis methods to meet its diagnosis and maintenance requirements. In order to improve the accuracy and efficiency of fault diagnosis for large-scale complex equipment, an intelligent fault diagnosis method is proposed. In this paper, the problem of fault diagnosis and sustainable remanufacturing of complex equipment under uncertain conditions is studied. Big data analysis and machine learning technology are applied to fault prediction diagnosis of equipment operation process, and fault information is mined from big data of complex equipment operation characteristics to realize rapid diagnosis of operation faults. The error between the natural frequency of low-speed complex equipment obtained by numerical simulation and the natural frequency calculated by finite element method is less than 5%. By comparing them, the accuracy of fault description is proved. We make full use of network technology, give full play to its advantages of wide coverage and resource sharing, and establish a network monitoring and fault diagnosis system, which can realize various software and hardware resource sharing, multi-machine redundant backup, and parallel processing. [ABSTRACT FROM AUTHOR]

Job scheduling has always been a challenging task in modern manufacturing and the most real life scheduling problems which involves multi-criteria, multi-machine environments. In this research, the single-machine scheduling problem is studied in which job processing times are controllable, namely, they may vary within a specified interval. The goal of this research is to minimize total tardiness and earliness on a single machine, simultaneously. In this context, we first propose a mathematical model for the considered problem and then a net benefit compression-net benefit expansion heuristic is presented for obtaining the set of amounts of compression and expansion of jobs processing times in a given sequence. Two meta-heuristic approaches are then employed to solve medium-to-large-sized problems as local search methods. Thereafter, we apply a hybrid method based on our heuristic as well as these two meta-heuristics in order to obtain solutions with higher quality within lesser computational time. The addressed problem is NP-hard since the single machine total tardiness problem is already NP-hard. The computational results show that our proposed heuristics can effectively solve such Just-In-Time problem with a high-quality solution. [ABSTRACT FROM AUTHOR]

We interpret and explain a phenomenon in short-term swing dynamics of multi-machine power grids that we term the Coherent Swing Instability (CSI). This is an undesirable and emergent phenomenon of synchronous machines in a power grid, in which most of the machines in a sub-grid coherently lose synchronism with the rest of the grid after being subjected to a finite disturbance. We develop a minimal mathematical model of CSI for synchronous machines that are strongly coupled in a loop transmission network and weakly connected to the infinite bus. This model provides a dynamical origin of CSI: it is related to the escape from a potential well, or, more precisely, to exit across a separatrix in the dynamical system for the amplitude of the weak nonlinear mode that governs the collective motion of the machines. The linear oscillations between strongly coupled machines then act as perturbations on the nonlinear mode. Thus we reveal how the three different mode oscillations-local plant, inter-machine, and inter-area modes-interact to destabilize a power grid. Furthermore, we present a phenomenon of short-term swing dynamics in the New England (NE) 39-bus test system, which is a well-known benchmark model for power grid stability studies. Using a partial linearization of the nonlinear swing equations and the proper orthonormal decomposition, we show that CSI occurs in the NE test system, because it is a dynamical system with a nonlinear mode that is weak relative to the linear oscillatory modes. [ABSTRACT FROM AUTHOR]

In this paper, a system consisting of a network of machines with random breakdown and repair times is considered. The machines in this system can be in one of four states: operational, in repair, starved, and blocked. Failure and repair times of the machines are exponentially distributed. Previous research on multi-machine failure-prone manufacturing systems (FPMS) has focused on systems consisting of machines in series or in parallel. This paper considers a network of machines with relationship constraints. Additionally, the system under study models work in process for multiple products, intermediate and final buffers and one type of final product. The demand rate for the final commodity is constant and unmet demand is either backlogged or lost. The objective of this control problem is to find the production rates and policies of the different machines so as to minimize the long run average inventory and backlog cost. The applied control policy is the hedging point policy that is determined by factors representing the level of buffer inventory for each machine. Obtaining analytical solutions is generally impossible for such complex systems. To simultaneously control the production rates of the machines we have therefore developed a method based on a combination of stochastic optimal control theory, discrete event simulation, experimental design and automated response surface methodology (RSM). The application of an automated RSM for Network FPMS is another contribution of this paper. The model can be extended easily to systems with age-dependent failure rates, a preventive repair maintenance policy and non-exponentially distributed up and down times. [ABSTRACT FROM AUTHOR]

In this paper, we study the multi-machine scheduling problem with earliness and tardiness penalties and sequence dependent setup times. This problem can be decomposed into two subproblems—sequencing and timetabling. Sequencing focuses on assigning each job to a fixed machine and determine the job sequence on each machine. We call such assignment a semi-schedule. Timetabling focuses on finding an executable schedule from the semi-schedule via idle-time insertion. Sequencing is strongly NP-hard in general. Although timetabling is polynomial-time solvable, it can become a computational bottleneck if the procedure is executed many times within a larger framework. This paper makes two contributions. We first propose a quantum improvement to the computational efficiency of the timetabling algorithm. We then apply it within a squeaky wheel optimization framework to solve the sequencing and overall problem. Finally, we demonstrate the strength of our proposed algorithms by experiments. [ABSTRACT FROM AUTHOR]

Abstract. We will give a survey on results related to scheduling problems where machines are not continuously available for processing. We will deal with single and multi machine problems and analyze their complexity. We survey NP-hardness results, polynomial optimization and approximation algorithms. We also distinguish between on-line and off-line formulations of the problems. Results are concerned with criteria on completion times and due dates. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of determining a permutation schedule for n jobs in an m-machine flow shop that operates in a sequence-dependent setup time (SDST) environment. Two constructive heuristic algorithms are developed with the minimisation of makespan as the objective. The first heuristic algorithm termed as setup ranking algorithm obtains the sequence using the setup times of jobs only. The second heuristic algorithm, fictitious job setup ranking algorithm (FJSRA), is developed using the concept of fictitious jobs. Pairs of jobs with minimum setup time between them constitute the fictitious jobs. Both these algorithms are compared with an existing constructive algorithm. For the purpose of experimentation, Taillard benchmark problems are used to develop SDST benchmark problems at eight different levels of sequence-dependent setup times. Graphical analysis, relative performance index analysis and statistical analysis are carried out on the results obtained for all the eight sets of benchmark problems. The analysis reveals that FJSRA emerges as the better algorithm for larger problems and for smaller problems with higher level of setup time. The results of statistical analysis are used to develop setup time dominance matrix for deciding upon the algorithm to be used for a particular size of problem. [ABSTRACT FROM AUTHOR]