Multi-unit System Research Articles

Assessment of various treatment techniques in restoring articulatory balance in the mastication center of occlusion

In clinical practice, due to various factors, we are facing patients with few teeth left in the lower jaw, often without any perspective. As a result of the reduced load capacity, they are loosened and can be completely lost. On the other hand, prosthodontics has a great potential for a unique implant - prosthetic rehabilitation. The goal is to preserve the alveolar ridge and slow down its resorption. With the advancement of the technology, conventional dentures are being replaced with more sophisticated therapeutic methods, such as modern prosthodontic superstructures. One of the latest solutions for this problem is the application of the Brånemark system (Nobel Biocare). These restorations that are applied represent a multi-unit system of implants and one hybrid prosthesis. This represents a mobile prosthetic suprastructure, which can be fixed with specific attachments on 4 implants. This combination of placement of a hybrid prosthetic structure on 4 implants is known as „all on four“. There are many discussions and writings about this multidisciplinary concept in the professional world. Over these 4 implants, a suprastructure with maximum 12 teeth can be immediately placed. The prosthetic construction circularly covers the entire toothless mandibular ridge. The data obtained from the patients show difference in the development of the bite force in the mastication center. The results have also shown a better quality of life in patients using a hybrid denture compared to the conventional prosthetic rehabilitation

Integrated decision of production scheduling and condition-based maintenance planning for multi-unit systems with variable replacement thresholds

In practice, production scheduling and maintenance planning are two popular technologies that are interdependent, but they are most often performed independently. In this study, we have developed an integrated model for coordinating single-machine scheduling and condition-based maintenance planning. Firstly, we build upon existing scheduling research that often views the system as a whole and further consider the degradation and maintenance of multiple units within the system during the scheduling process. Secondly, in contrast to traditional degradation modeling which usually assumes a single threshold to trigger maintenance/replacement, we also consider the variability of replacement thresholds caused by the heterogeneity of scheduling tasks. Based on this foundation, we propose an integrated policy that considers both scheduling and condition-based maintenance. Furthermore, we have developed an integrated decision model for multi-unit systems. The model aims to minimize the total weighted expected completion time over the entire scheduling time horizon. Moreover, the genetic algorithm was employed to optimize the integrated model. Finally, we present an example of an SA adjustable multi-axis machine and conduct a sensitivity analysis of key parameters to validate the integration policy.

Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks

The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and manual investigation of each of the resulting images can be a time-consuming task. This paper presents an automatic brain-tumor diagnosis system that uses a CNN for detection, classification, and segmentation of glioblastomas; the latter stage seeks to segment tumors inside glioma MRI images. The structure of the developed multi-unit system consists of two stages. The first stage is responsible for tumor detection and classification by categorizing brain MRI images into normal, high-grade glioma (glioblastoma), and low-grade glioma. The uniqueness of the proposed network lies in its use of different levels of features, including local and global paths. The second stage is responsible for tumor segmentation, and skip connections and residual units are used during this step. Using 1800 images extracted from the BraTS 2017 dataset, the detection and classification stage was found to achieve a maximum accuracy of 99%. The segmentation stage was then evaluated using the Dice score, specificity, and sensitivity. The results showed that the suggested deep-learning-based system ranks highest among a variety of different strategies reported in the literature.

An integrated framework of preventive maintenance and task scheduling for repairable multi-unit systems

Preventive maintenance and task scheduling are both crucial activities in industrial practice. Although they are interdependent and mutually influential, most existing studies typically analyze them separately. In this paper, an integrated framework of preventive maintenance planning and task scheduling is explored. On one hand, while most scholars focus on the system as a whole in scheduling studies, we further take into account the degradation and maintenance of multiple units during the task scheduling process. On the other hand, instead of only using "direct replacement" as the main maintenance method, we further consider the repairability of the unit by incorporating imperfect maintenance effects into the maintenance modeling. Additionally, the lifecycle partitioning method is proposed to analyze the maintenance groups for multiple units at the completion of scheduling tasks and their corresponding probabilities. Based on this foundation, an integrated decision model is established with the preventive maintenance threshold and scheduling task sequence as decision variables to minimize the total weighted expected completion time. Finally, a novel genetic algorithm based on hybrid encoding is proposed, and the effectiveness of integrated decision-making was validated using a practical example.

Joint Decisions of Components Replacement and Spare Parts Ordering Considering Different Supplied Product Quality

In addition to equipment maintenance decisions, spare parts ordering decisions from different suppliers play a key role in reducing related costs (e.g., maintenance, inventory and ordering costs). Since suppliers may use different production technologies and materials, spare parts (or products) from different suppliers can be different in quality. Nevertheless, in recent studies, the quality of spare parts is rarely considered to incorporate both equipment maintenance and spare parts ordering. In this paper, we investigate the joint optimization of condition-based maintenance and spare parts provisioning policy under two suppliers with different product quality. We formulate a sequential-decision problem with a Markov decision process and consequently obtain an optimal maintenance and ordering policy by an exact value iteration algorithm. To improve computation efficiency, based on the principle of sequential optimization, we develop heuristic methods. Extensive numerical experiments are conducted to assess the overall performance of the developed heuristic methods. Compared to the optimal method, results showed that the average cost gap is about 2% and computation time is reduced by 94% on average under the proposed heuristic method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the observation that automobile industries tried to integrate emergency suppliers from which spare parts have different quality into maintenance schedules to avoid stockout and reduce equipment failure during the Covid-19 pandemic. Specifically, the article focuses on balancing the trade-offs between condition-based maintenance and inventory management from two suppliers with different lead times and spare parts quality for multi-unit systems. On the one hand, effective maintenance scheduling relies on spare parts for replacement to ensure the stability of production. On the other hand, inventory management needs to select the supplier with appropriate lead time and product quality to reduce the ordering cost and avoid stockout based on the degradation states of equipment. The joint optimization of these two aspects serves to reduce the total maintenance and ordering cost. Nevertheless, most existing research aims to optimize them separately. In this paper, we formulate the joint decision problem considering the two aspects based on a Markov decision process. We obtain an optimal maintenance and ordering policy by an exact value iteration algorithm and present heuristics to improve the computation efficiency when the system contains multiple machines. Practitioners can implement the proposed methodology to make condition-based maintenance and inventory management when spare parts with different qualities are ordered from two suppliers. To balance cost and computational efficiency, it is suggested to implement the optimal policy by an exact value iteration algorithm when the number of machines is small in the system and use the heuristic methods when the number of machines is large (i.e., usually larger than 3).

Microparticles and multi-unit systems for advanced drug delivery

Microparticles have unique benefits in the formulation of multiparticulate and multi-unit type pharmaceutical dosage forms allowing improved drug safety and efficacy with favorable pharmacokinetics and patient centricity. On the other hand, the above advantages are served by high and well reproducible quality attributes of the medicinal product where even flexible design and controlled processability offer success as well as possible longer product life-cycle for the manufacturers. Moreover, the specific demands of patients can be taken into account, including simplified dosing regimens, flexible dosage, drug combinations, palatability, and ease of swallowing.In the more than 70 years since the first modified-release formulation appeared on the market, many new formulations have been marketed and many publications have appeared in the literature. More unique and newer pharmaceutical technologies and excipients have become available for producing tailor-made particles with micrometer dimensions and beyond. All these have contributed to the fact that the sub-units (e.g. minitablets, pellets, microspheres) that make up a multiparticulate system can vary widely in composition and properties. Some units have mucoadhesive properties and others can float to contribute to a suitable release profile that can be designed for the multiparticulate formula as a whole. Nowadays, there are some available formulations on the market, which are able to release the active substance even for several months (3 or 6 months depending on the type of treatment).In this review, the latest developments in technologies that have been used for a long time are presented, as well as innovative solutions such as the applicability of 3D printing to produce subunits of multiparticulate systems. Furthermore, the diversity of multiparticulate systems, different routes of administration are also presented, touching the ones which are capable of carrying the active substance as well as the relevant, commercially available multiparticle-based medical devices. The versatility in size from 1 µm and multiplicity of formulation technologies promise a solid foundation for the future applications of dosage form design and development.

Estimation of uncertainty in computation of importance factors and probability of undesirable events for safety-critical multi-unit systems, based on Binary Decisions Diagrams

This paper addresses current needs of the Multi-Unit Probabilistic Safety Assessment (MUPSA) methodology, which consists of a series of eight essential steps for conducting Probabilistic Safety Assessment (PSA) in the context of a multi-unit Nuclear Power Plant (NPP), co-located on a single site. The focus of this study revolves around one of the steps of the MUPSA methodology, which includes the examination of the importance factors and uncertainty analysis. Emphasizing the role of this step within MUPSA, the paper highlights the hypothesis and estimations made, along with the determination of system importance factors and failure frequency. To illustrate the practical application of these concepts, the paper presents three illustrative examples. The first example involves a 3-terminal reliability network, while the second example features an illustrative calculation applied to a system designed for potential use in a real NPP, compared to a Monte-Carlo simulation. Moreover, the second example involves the evaluation of a medium voltage distribution system (MVDS) designed to meet the auxiliary supply requirements of a nuclear power plant. This analysis is performed using a binary decision diagram (BDD), and the results derived from the BDD evaluation are then verified with those obtained through a Monte Carlo simulation for a comprehensive comparative analysis. Finally, an uncertainty analysis is achieved using a BDD reliability model, using the results of fault trees developed by National Company SN Nuclearelectrica S.A. (SNN), Department of Nuclear Safety, in the context of the Coordinated Research Project (CRP- I31031).

Maintenance optimization of multi-unit balanced systems using deep reinforcement learning

Balanced systems are extensively utilized in various engineering fields. However, the study of maintenance optimization for balanced systems under dependent competing risks is very limited, and the existing method for orthodox multi-unit systems cannot solve the maintenance problem of balanced systems. Thus, we present a novel maintenance optimization method for systems with multiple balanced units. The units in the system are assumed to experience multiple failure processes due to continuous degradation and external shock damage. The system failure occurs in the following situations: (1) the deterioration levels of units on the symmetric positions exceed critical thresholds. (2) the deterioration level of any unit reaches the failure threshold. (3) the shock magnitude is greater than the hard failure threshold. By employing a customized deep reinforcement learning algorithm, we derive the optimal maintenance policy under a fixed planning horizon. Notably, our approach differs from previous research on maintenance optimization for balanced systems, the proposed method avoids the discretizing of the deterioration state. This significantly enhances computational efficiency, particularly when applied to multi-unit balanced systems. Numerous numerical studies illustrate the effectiveness of our proposed maintenance optimization method for multi-unit balanced systems.

Simultaneous aerobic nitrogen and phosphate removal by Pseudomonas aeruginosa SNDPR-01: Comprehensive insights on metabolic potential and mechanisms

Versatile metabolic bacteria with simultaneous nitrification–denitrification and phosphate removal (SNDPR) capabilities are promising to reduce the wastewater treatment process from a multi-unit system to a single unit system. Due to the limited study, the metabolic potential and mechanisms of nitrogen (N) and phosphate (P) removal for any SNDPR bacteria remain unknown. In this study, genomic analysis revealed various genes were present in an SNDPR isolate, Pseudomonas aeruginosa SNDPR-01, establishing diverse N- and P-metabolism potential, such as aerobic and anoxic denitrification; dissimilatory nitrate reduction to ammonia; polyphosphate (polyP) metabolism; and utilization of organic P. Phylogenetic analysis predicted that key functional genes related N and P metabolism may co-evolve with chromosomal genes, except for the norB gene, which may originate from horizontal gene transfer. Transcriptomic analysis revealed a partial nitrification and denitrification pathway (NH4+ → NO2– → NO → N2O → N2) to aerobically remove N without the involvement of conventional amoA, hao and napA genes. Unknown genes were confirmed to mediate the oxidation of ammonium to nitrite. Excellent P removal ability was aided by the synthesis of adenosine triphosphate (ATP) and polyP. The formed polyP was utilized under aerobic conditions without accumulation, which is different from traditional polyP accumulation organisms in wastewater treatment plants (WWTPs). This study will deepen the roles of SNDPR bacteria in aerobic ecological niches and enhance their application in WWTPs.

Simulation model for the study of maintenance actions in a homogeneous multi-unit system of interchangeable components, with cannibalization

Simulation model for the study of maintenance actions in a homogeneous multi-unit system of interchangeable components, with cannibalization

Data-driven joint optimization of maintenance and spare parts provisioning for deep space habitats: A stochastic programming approach

The utilization of sensor data to make informed decisions on maintenance schedule and spare parts provisioning will be determinant to the development of self-aware habitable facilities–so called SmartHabs–in deep space regions. To address this novel and challenging problem, this work presents a chance constrained stochastic mixed integer program to jointly optimize maintenance and spare parts provisioning for ensuring reliable and cost-efficient operations of SmartHabs. A SmartHab is modeled as a multi-unit system with non-identical components that randomly degrade over time. The proposed optimization model is driven by telemetry sensor data as the uncertainty of the remaining lifetime of the components is estimated by data-driven prognostic algorithms. The model further incorporates characteristic operational conditions of deep space habitats such as a long planning horizon that requires multiple maintenance decisions for each component, maintenance task assignment to humans and robots, long lead times between resupply trips, and high penalties due to stock-outs of spare parts. The formulation introduces chance constraints restricting the unavailability of critical components and the number of corrective repairs with high probability under the uncertainty of component failures. The nonlinearities induced by the chance constraints are circumvented by the derivation of two linearization methods based on scenario representation and safe approximations, resulting in their tractable mixed integer linear reformulations. The proposed model is validated by simulations, showing reliable and effective plans against benchmark approaches while evidencing to be computationally scalable as the number of components increases.

Opportunistic maintenance strategy optimization considering imperfect maintenance under hybrid unit-level maintenance strategy

Opportunistic maintenance is a widely adopted strategy for maintaining multi-unit systems, which has garnered significant attention from researchers. However, the current opportunistic maintenance still suffers from certain limitations, such as single preventive maintenance strategy at unit-level and lack of flexibility in optimization model. In this paper, an optimization model for scheduled maintenance (SM) and condition-based maintenance (CBM) at the unit-level is presented, and the associated maintenance costs are analyzed. Subsequently, a preventive opportunistic maintenance strategy optimization model under hybrid unit-level maintenance strategies is established by considering imperfect preventive maintenance. An optimization algorithm based on Monte Carlo simulation is designed to solve the model. Finally, the opportunistic maintenance strategy of CNC gear grinding machine is developed. Comparing the opportunistic maintenance strategy given in this paper with other maintenance strategies, the results show that the maintenance strategy presented in this paper can significantly reduce the maintenance cost of the CNC grinding machine. The opportunistic maintenance strategy optimization method proposed in this paper can provide guidance for relevant enterprises to formulate maintenance strategies.

A deep reinforcement learning approach for repair-based maintenance of multi-unit systems using proportional hazards model

A deep reinforcement learning approach for repair-based maintenance of multi-unit systems using proportional hazards model

Joint Optimization of Maintenance and Spare Parts Inventory for Multi-Unit Systems With a Generalized Structure

Abstract Joint optimization of maintenance operations and spare parts inventory is promising for ensuring the reliable and efficient operation of industrial systems. Extensive studies have been conducted on joint optimization emphasizing various aspects. However, those studies still suffer two limitations. (1) The considered system structures are restricted to particular cases, and thus are unable to fully address the joint optimization needs of actual industrial systems possessing various structures, and (2) imperfect maintenance, as has been commonly adopted in industries, is not addressed in existing works, which compromises the economic efficiency of the joint policy. Targeted at these limitations, this paper investigates the joint optimization for multi-unit systems with a generalized structure, i.e., the systems consist of multiple identical units, and each unit possesses a complex subsystem structure. First, a total cost rate model is formulated, in which maintenance actions include both perfect replacement and imperfect maintenance, and the spare parts inventory policy consists of an (s, S) policy and the option to place emergency orders. Based on the formulated model, a simulation optimization method is proposed. The simulation module evaluates the total cost rate iteratively, and the optimization module utilizes a variable neighborhood search-based algorithm to obtain a high-quality joint policy with tractable computation times. Two numerical examples are provided for demonstration, and sensitivity analyses are conducted to study the effects of parameters.

XML-Based Automatic NIOS II Multi-Processor System Generation for Intel FPGAs

Many embedded systems are introducing processing units to accelerate the processing speed of tasks, such as for multi-media applications. The units are mostly customized designs. Another method of designing multi-unit systems is using pre-defined standard intellectual properties. However, the procedure of arranging IP cores in a system and maintaining a high performance as well are the remaining challenges. Implementing softcore processors on field-programmable gate arrays (FPGAs) is a relatively fast and inexpensive choice to design and validate a desired system. This paper describes the rapid prototyping of hardware/software co-design based on FPGAs. A novel system generator to effortlessly design a multiple NIOS II soft-processor core systems is also purposed. The NIOS II CPU is a configurable RISC processor designed by Altera/Intel and can be trimmed to complete specific tasks. The error-prone and time-consuming process of designing an IP block-based system is improved by the new novel system generator. The detail of the implementation of such system is discussed. To test the performance of a multi-NIOS II system, a parallel application is executed on 1-, 2-, 5-, and 10-core NIOS II systems separately. Test results prove the feasibility of the proposed methodology (for an FIR filter, a dual-core system is 29% faster than a single-core system; a 5-core system is 28% faster than the dual-core system).

Review on Starter Pellets: Inert and Functional Cores.

A significant proportion of pharmaceuticals are now considered multiparticulate systems. Modified-release drug delivery formulations can be designed with engineering precision, and patient-centric dosing can be accomplished relatively easily using multi-unit systems. In many cases, Multiple-Unit Pellet Systems (MUPS) are formulated on the basis of a neutral excipient core which may carry the layered drug surrounded also by functional coating. In the present summary, commonly used starter pellets are presented. The manuscript describes the main properties of the various nuclei related to their micro- and macrostructure. In the case of layered pellets formed based on different inert pellet cores, the drug release mechanism can be expected in detail. Finally, the authors would like to prove the industrial significance of inert cores by presenting some of the commercially available formulations.

Novel chaos game optimization tuned-fractional-order PID fractional-order PI controller for load\u2013frequency control of interconnected power systems

In this work, chaos game optimization (CGO), a robust optimization approach, is employed for efficient design of a novel cascade controller for four test systems with interconnected power systems (IPSs) to tackle load–frequency control (LFC) difficulties. The CGO method is based on chaos theory principles, in which the structure of fractals is seen via the chaotic game principle and the fractals’ self-similarity characteristics are considered. CGO is applied in LFC studies as a novel application, which reveals further research gaps to be filled. For practical implementation, it is also highly desirable to keep the controller structure simple. Accordingly, in this paper, a CGO-based controller of fractional-order (FO) proportional–integral–derivative–FO proportional–integral (FOPID–FOPI) controller is proposed, and the integral time multiplied absolute error performance function is used. Initially, the proposed CGO-based FOPID–FOPI controller is tested with and without the nonlinearity of the governor dead band for a two-area two-source model of a non-reheat unit. This is a common test system in the literature. A two-area multi-unit system with reheater–hydro–gas in both areas is implemented. To further generalize the advantages of the proposed scheme, a model of a three-area hydrothermal IPS including generation rate constraint nonlinearity is employed. For each test system, comparisons with relevant existing studies are performed. These demonstrate the superiority of the proposed scheme in reducing settling time, and frequency and tie-line power deviations.

Optimizing opportunistic preventive maintenance strategy for multi-unit system of CNC lathe

Optimizing opportunistic preventive maintenance strategy for multi-unit system of CNC lathe

Performance Analysis of Diverse Energy Storage on Combined ALFC and AVR Control of Multiarea Multiunit System with AC/HVDC interconnection

This paper discusses the significance of various energy storage devices like redox flow battery (RFB), capacitive energy storage (CES), superconducting magnetic energy storage (SMES), and flywheel energy storage system (FESS) impact on the combined control of automatic load frequency control (ALFC) and automatic voltage regulator (AVR) of three areas interconnected power systems having thermal, hydro, gas, and geothermal plants in presence of HVDC link. A novel PD with filter coefficient (PDN) controller cascade with fractional-order PID with filter coefficient (FOPIDN), CPDN-FOPIDN is proposed as a secondary controller for combined ALFC-AVR loop. An artificial flora algorithm is utilized to obtain the various controller parameters under numerous system conditions. The proposed CPDN-FOPIDN controller provides less settling time, overshoots, undershoots, and reduces oscillation compare to the FOPIDN controller. The study also reveals that the RFB provides better system performance than SMES, CES, and FESS. Integration of HVDC link improves the system dynamic response. Furthermore, the sturdiness of the proposed CPDN-FOPIDN controller is tested against the changes in system loading conditions via sensitivity analysis.

Global matrix method for frequency-domain stability analysis of hydropower generating system

Safe and stable operation of hydropower plants is the basis and prerequisite of great importance for clean energy production and integration of renewables. To better analyze the operating stability of hydropower generating systems (HGSs), a novel frequency-domain method (global matrix method) is proposed in this paper. First, the principle of the new method and the mathematical models of each common hydraulic component in HGSs are presented. Second, three general steps for deriving the global matrix of an arbitrary system and order-reduction and numerical calculation of the matrix are illustrated in detail. In two case studies of HGS stability analysis, the transfer function method (TFM) suitable for time-frequency domain analysis is applied to evaluate the global matrix method. For simple single-unit system, the new method is compared by the TFM from both frequency domain and time domain. For complicated multi-unit system, the TFM is not applicable to the frequency-domain stability analysis, and only the time-domain simulation is applied to verify the new method. The results indicate that the stability regions by the two methods are in good agreement, verifying the accuracy of the new approach. The proposed method can replace the TFM for stability analysis of HGSs in frequency domain, outperforming the latter in applicability, flexibility, and quantification function.