Specification Constraints Research Articles

Solving the Workflow Satisfiability Problem Using General Purpose Solvers

The workflow satisfiability problem (WSP) is a well-studied problem in access control seeking allocation of authorised users to every step of the workflow, subject to workflow specification constraints. It was noticed that the number <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> of steps is typically small compared to the number of users in the real-world instances of WSP; therefore <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is considered as the parameter in WSP parametrised complexity research. While WSP in general was shown to be W[1]-hard, WSP restricted to a special case of user-independent (UI) constraints is fixed-parameter tractable (FPT). However, restriction to the UI constraints might be impractical. To efficiently handle non-UI constraints, we introduce the notion of branching factor of a constraint. As long as the branching factors of the constraints are relatively small and the number of non-UI constraints is reasonable, WSP can be solved in FPT time. Extending the results from Karapetyan et al. (2019), we demonstrate that general-purpose solvers are capable of achieving FPT-like performance on WSP with arbitrary constraints when used with appropriate formulations. This enables one to tackle most of practical WSP instances. While important on its own, we hope that this result will also motivate researchers to look for FPT-aware formulations of other FPT problems.

Angle-Based SLAM on 5G mmWave Systems: Design, Implementation, and Measurement

Simultaneous localization and mapping (SLAM) is a key technology that provides user equipment (UE) tracking and environment mapping services, enabling the deep integration of sensing and communication. The millimeter-wave (mmWave) communication, with its larger bandwidths and antenna arrays, inherently facilitates more accurate delay and angle measurements than sub-6 GHz communication, thereby providing opportunities for SLAM. However, none of the existing works have realized the SLAM function under the 5G New Radio (NR) standard due to specification and hardware constraints. In this study, we investigate how 5G mmWave communication systems can achieve situational awareness without changing the transceiver architecture and 5G NR standard. We implement 28 GHz mmWave transceivers that deploy OFDM-based 5G NR waveform with 160 MHz channel bandwidth, and we realize beam management following the 5G NR. Furthermore, we develop an efficient successive cancellation-based angle extraction approach to obtain angles of arrival and departure from the reference signal received power measurements. On the basis of angle measurements, we propose an angle-only SLAM algorithm to track UE and map features in the radio environment. Thorough experiments and ray tracing-based computer simulations verify that the proposed angle-based SLAM can achieve sub-meter level localization and mapping accuracy with a single base station and without the requirement of strict time synchronization. Our experiments also reveal many propagation properties critical to the success of SLAM in 5G mmWave communication systems.

Communication of Design Data in Manufacturing Democratization

Part design is the principal source of communicating design intent to manufacturing and inspection. Design data are often communicated through computer-aided design (CAD) systems. Modern analytics tools and artificial intelligence integration into manufacturing have significantly advanced machine recognition of design specification and manufacturing constraints. These algorithms require data to be uniformly structured and easily consumable; however, the design data are represented in a graphical structure and contain a nonuniform structure, which limits the use of machine learning algorithms for a variety of tasks. This paper proposes an algorithm for extracting dimensional data from three-dimensional (3D) part designs in a structured manner. The algorithm extracts face dimensions and their relationships with other faces, enabling the recognition of underlying patterns and expanding the applicability of machine learning for various tasks. The extracted part dimensions can be stored in a dimension-based numeric extensible markup language (XML) file, allowing for easy storage and use in machine-readable formats. The resulting XML file provides a dimensional representation of the part data based on their features. The proposed algorithm reads and extracts dimensions with respect to each face of the part design, preserving the dimensional and face relevance. The uniform structure of the design data facilitates the processing of data by machine learning algorithms, enabling the detection of hidden patterns and the development of pattern-based predictive algorithms.

IEDW: A BIM-based indoor electric distribution wiring algorithm using graph theory and capacity-limited multiple traveling salesman problem solver

Building Information Modeling (BIM) has emerged as a new paradigm for the construction industry. The combination of electric distribution systems design and BIM is the future development trend of architectural design. The traditional electric distribution wiring design based on BIM is mainly carried out manually, which is laborious and error-prone. To cope with this challenge, some studies have proposed explorations of automated BIM wiring design based on graphs in building structures, HAVC, and AEC fields, but few efforts were made on automatic electric distribution wiring. In view of its complicated nature, actual construction factors such as design specifications, wiring costs, etc. should be considered with electric distribution wiring simultaneously. To address this issue, this study proposes a BIM-based automatic indoor electric distribution wiring (IEDW) scheme using graph theory and capacity-limited multiple traveling salesman problem (CMTSP). Firstly, the IEDW sets up the model for design specifications and actual wiring costs with four constraints of specification, cost, installation, and load. Then, a electric distribution wiring heterogeneous graph model is designed to solve both specification and installation constraints. Thirdly, a CMTSP-based algorithm is designed to solve the problem of automatic electric distribution wiring under cost and load constraints. Finally, the performance of IEDW is evaluated on three types of buildings. The experimental results proved that the IEDW can provide a more reasonable wiring solution than manual wiring in terms of circuit balance and total wiring length. Moreover, the IEDW can reduce the wiring cost by 8.8% on average. The proposed IEDW will enhance the efficiency of BIM-based electric systems design and delivery.

Measurement of Process Capability of Manufacturing Process of Levetiracetam by Applying Concept of Six Sigma

Abstract: Background: A process capacity study is a study that uses capability indices to compare the output of an in-control process to specification constraints. Cp, Cpk, Ppk, and Pp are some of the statistics that can be used to assess process capability. The goal of the six-sigma study of levetiracetam tablets is to enhance and adjust the process to produce defect-free tablets and to maximize customer satisfaction. Process capability guarantees that procedures meet industry firm specifications while lowering process variation, which is essential for obtaining product quality characteristics. Before the batch is commercialized, this capacity research should be used in the industry. It is a cost-effective strategy that can shorten the inspection process. Materials and Methods: The control and capabilities of Antiepileptic tablets are described in this study. The research focuses on data collecting and analysis of process capability utilizing statistical software such as MINITAB 19.1. Results: The results of process capabilities for all the processes like hardness test, thickness test, disintegration test, loss on drying and friability tests were found to be above 1.33. Conclusion: The existing capability of the process is judged to be capable in this research. Keywords: DMAIC phases, Epilepsy disorder, Levetiracetam, Process capability index, Six sigma process.

Artificial intelligence-driven design of fuel mixtures

High-performance fuel design is imperative to achieve cleaner burning and high-efficiency engine systems. We introduce a data-driven artificial intelligence (AI) framework to design liquid fuels exhibiting tailor-made properties for combustion engine applications to improve efficiency and lower carbon emissions. The fuel design approach is a constrained optimization task integrating two parts: (i) a deep learning (DL) model to predict the properties of pure components and mixtures and (ii) search algorithms to efficiently navigate in the chemical space. Our approach presents the mixture-hidden vector as a linear combination of each single component’s vectors in each blend and incorporates it into the network architecture (the mixing operator (MO)). We demonstrate that the DL model exhibits similar accuracy as competing computational techniques in predicting the properties for pure components, while the search tool can generate multiple candidate fuel mixtures. The integrated framework was evaluated to showcase the design of high-octane and low-sooting tendency fuel that is subject to gasoline specification constraints. This AI fuel design methodology enables rapidly developing fuel formulations to optimize engine efficiency and lower emissions.

Research on lightweight and fatigue life of engine hood based on multi-objective particle swarm optimization

A multi-objective optimization method is presented in this paper, aiming at improving the fatigue life of the engine hood while achieving light weight. By analyzing the factors affecting the fatigue life of the engine hood, a multi-objective optimization model was established that considered five design variables including the thickness of the inner plate, the thickness of the outer plate, the stiffness of the hook, the stiffness of the sealing strip, and the height of the buffer block. Then, the multi-objective particle swarm method was used for optimization, and the optimal solution was obtained in the form of a Pareto set. The ideal compromise solution was determined from the Pareto set by using fuzzy membership functions. On this basis, the optimal solution was determined from the Pareto set by comprehensively considering the steel plate material specification, mold and cost constraints. The torsional deformation test and switch fatigue test show that for the optimized engine hood, the fatigue life is increased by 117%, the mass is reduced by 0.51 kg, and the torsional deformation of the engine hood does not increase significantly. The proposed multi-objective optimization method is proved to be feasible and effective in improving engine hood fatigue life and lightweight design.

Design-Window Methodology for Inductorless Noise-Cancelling CMOS LNAs

This paper presents an optimization methodology for inductorless noise-cancelling CMOS Low-Noise Amplifiers (LNA), whose performance typically depends on a tight balance in the design of two transistor stages. Due to the different functions of the two parts, noise-cancelling amplifiers become very difficult to analyze in detail by closed-form expressions or straight simulations: each section significantly affects the results of the other. In addition, opposed specifications, such as gain and cut-off frequency, suppose another grade of complexity due to the interplay of the two branches of the circuit. As a solution, the proposed methodology uses a visualization of the design window in 2-dimensional space to optimize the different parameters of the specifications without compromising the others. All specification constraints are represented in a single figure instead of one graph per parameter. Compared with most optimization methods, the design window methodology observes the design span instead of isolated design points that might not guarantee feasibility. Furthermore, as a simulation-driven exploration method, it benefits from complete device models with high-order effects that would be too complex to include in analytical expressions but critical to achieving maximum efficiency. As an example of the method, the paper describes the optimization of the well-known CS-CG noise-cancelling LNA in 65-nm standard CMOS technology. Final post-layout simulations report very competitive results with a 3.7-dB noise figure, a 17-dB gain, and a cut-off frequency above 7 GHz.

Robust fractional-order [proportional integral derivative] controller design with specification constraints: more flat phase idea

This paper presents a new design methodology for robust fractional-order controllers with more than three parameters for first-order plus dead time systems using the synthesis scheme of the ‘more flat phase’ idea for a fractional-order controller with [proportional integral derivative] (FO[PID]) structure. The stability region of the FO[PID] controller and the synthesis scheme with the ‘more flat phase’ idea are illustrated through a simulation example. The corresponding pseudo-codes of the synthesis scheme are also summarised. The implementation and approximation error of the FO[PID] controller are also discussed. Likewise, the superiority of the FO[PID] controller designed with the ‘more flat phase’ idea is verified by additional simulations and experimental results where the closed-loop system with FO[PID] controller is not sensitive to the variations of the loop gain, ensuring satisfactory control performance. Obtained results show high potential in practical industrial applications.

Variation management of key control characteristics in multistage machining processes considering quality-cost equilibrium

In multistage machining processes (MMPs), variations from key control characteristics (KCCs) continue to propagate and eventually accumulate to deviations in key product characteristics (KPCs). Therefore, the variation control of KCCs is significant to ensure the final product quality. In this paper, a variation management framework for KCCs in MMPs is established to address this issue. The new concept of variation management consists of process-oriented tolerancing and maintenance planning, and the optimal variation management strategy for each KCC is assigned based on its impact to the manufacturing system. The proposed framework deduces corresponding KCC variation distributions for previously unresearched locating schemes, thereby expanding the application scenarios of this method. For the quality specification constraints, geometric tolerances are integrated for the first time beside traditional dimensional tolerances, which expand the error scale of quality control. The modified Chebyshev goal programming (MCGP) approach is adopted to find the equilibrium point between quality and cost effectively. The superiority of the proposed method is verified by a case study of an automotive engine cylinder block MMP. The results show a remarkable improvement on the manufacturing system performance in terms of quality and cost.

Towards efficient deep neural network training by FPGA-based batch-level parallelism

Training deep neural networks (DNNs) requires a significant amount of time and resources to obtain acceptable results, which severely limits its deployment in resource-limited platforms. This paper proposes DarkFPGA, a novel customizable framework to efficiently accelerate the entire DNN training on a single FPGA platform. First, we explore batch-level parallelism to enable efficient FPGA-based DNN training. Second, we devise a novel hardware architecture optimised by a batch-oriented data pattern and tiling techniques to effectively exploit parallelism. Moreover, an analytical model is developed to determine the optimal design parameters for the DarkFPGA accelerator with respect to a specific network specification and FPGA resource constraints. Our results show that the accelerator is able to perform about 10 times faster than CPU training and about a third of the energy consumption than GPU training using 8-bit integers for training VGG-like networks on the CIFAR dataset for the Maxeler MAX5 platform.

Fractional order [Proportional Integral Derivative] Controller Design with Specification Constraints: More Flat Phase Idea

The fractional order proportional integral derivative controller is attracting more and more attention. To design a controller with some specification constraints for the first order plus time delay (FOPTD) system, the idea of the “more flat phase” and the structure of the fractional order [proportional integral derivative] (FO[PID]) controller are proposed in this paper. Firstly, the stability region of the FO[PID] controller and the controller design with the “more flat phase” are introduced. Then the design procedure is presented by a simulation, and the pseudo code of the design procedure to obtain the parameter pairs and the achievable region is offered. The effectiveness of the proposed design method for the FO[PID] controller is verified by the experiment on the Peltier temperature control platform and the experiment results show a great potential in industrial applications.

Dynamic Modelling and Optimisation of the Batch Enzymatic Synthesis of Amoxicillin

Amoxicillin belongs to the β-lactam family of antibiotics, a class of highly consumed pharmaceutical products used for the treatment of respiratory and urinary tract infections, and is listed as a World Health Organisation (WHO) “Essential Medicine”. The demonstrated batch enzymatic synthesis of amoxicillin is composed of a desired synthesis and two undesired hydrolysis reactions of the main substrate (6-aminopenicillanic acid (6-APA)) and amoxicillin. Dynamic simulation and optimisation can be used to establish optimal control policies to attain target product specification objectives for bioprocesses. This work performed dynamic modelling, simulation and optimisation of the batch enzymatic synthesis of amoxicillin. First, kinetic parameter regression at different operating temperatures was performed, followed by Arrhenius parameter estimation to allow for non-isothermal modelling of the reaction network. Dynamic simulations were implemented to understand the behaviour of the design space, followed by the formulation and solution of a dynamic non-isothermal optimisation problem subject to various product specification constraints. Optimal reactor temperature (control) and species concentration (state) trajectories are presented for batch enzymatic amoxicillin synthesis.

Design optimization of semi-rigid space steel frames with semi-rigid bases using biogeography-based optimization and genetic algorithms

This paper performs for the first time a simultaneous optimization for members sections along with semi-rigid beam-to-column connections for space steel frames with fixed, semi-rigid, and hinged bases using a biogeography-based optimization algorithm (BBO) and a genetic algorithm (GA). Furthermore, a member\'s sections optimization for a fully fixed space frame is carried out. A real and accurate simulation of semi-rigid connection behavior is considered in this study, where the semi-rigid base connections are simulated using Kanvinde and Grilli (2012) nonlinear model, which considers deformations in different base connection components under the applied loads, while beam-to-column connections are modeled using the familiar Frye and Morris (1975) nonlinear polynomial model. Moreover, the P-∆ effect and geometric nonlinearity are considered. AISC-LRFD (2016) specification constraints of the stress and displacement are considered as well as section size fitting constraints. The optimization is applied to two benchmark space frame examples to inspect the effect of semi-rigidity on frame weight and drift using BBO and GA algorithms.

Asynchronous machine vector control: PIα controllers for current loops

This article deals with field-oriented control of induction machine squirrel cage. A robust fractional-order controller is applied and investigated to control the induction machine currents isd and isq. The fractional-order gives better fit in regulation operation. For this purpose, this controller form is recommended, especially in industrial systems, thanks to his flexibility, robustness and efficiency to solve complex problems such as electrical parameters changes (i.e. uncertain parameter) caused by the temperature effect. Based on frequency specification and several constraints, the fractional-order controller is designed. The fmincon toolbox optimization is used to adjust ki, kp and α values. In order to show the reliability of the developed controller in the induction machine behavior, several simulation results are carried out and illustrated.

Strategic optimization of water reuse in wafer fabs via multi-constraint linear programming technique

The risk of water shortage has been posing as a threat to water demanding industries in Taiwan, including the high-tech industries where ultrapure water is needed for the production of microchips. Such risks are especially unpredictable in the age of climate change, where more frequent extreme climate events such as prolonged droughts have sent these industries scrambling for securing water supply at a very high cost. The national policy also mandates strict water recycling standards for these high-tech plants, while the risk of water supply shortage also forces the industry to be water-conscious. However, most plants set their water recycling strategies based on experience or “rules of thumb” practices, without implementing optimization tools that can help making decisions in a more scientific approach. In this study we applied linear programming technique to optimize the water recovery path for a microchip assembly plant. A water balance diagram was formulated and completed to determine the existing water recycling performance, and the data was converted to a water flow network. The water flow network was then derived with a mathematical model to formulate a linear optimization problem. The proposed linear programming model is composed of mass balance constraints, unit specification constraints, capacity constraints as well as water quality constraints (discharge limits). The linear programming method was effectively appplied to improve the efficiency of water reuse. With the installation of the regeneration units, an increase of ∼40.1% in the volume of reused water was predicted. The results from water cost structure also indicated that, at higher water tariff, water reuses through reclaiming and generating spent effluents can alleviate the overall water consumption costs.

Optimal Power Control in Rayleigh-Fading Heterogeneous Wireless Networks

Heterogeneous wireless networks provide varying degrees of network coverage in a multi-tier configuration in which low-powered small cells are used to enhance performance. Due to the ad-hoc deployment of small cells, optimal resource allocation is important to provision fairness and enhance energy efficiency. We first study the worst outage probability problem in Rayleigh-fading channels, and solve this nonconvex stochastic program using mathematical tools from nonlinear Perron-Frobenius theory. As a by-product, we solve an open problem of convergence for a previously proposed algorithm in the interference-limited case. We then address a total power minimization problem with outage specification constraints and its feasibility issue. We propose a dynamic algorithm that adapts the outage probability specification in a heterogeneous wireless network to minimize the total energy consumption and to simultaneously provide fairness guarantees in terms of the worst outage probability. Finally, we provide numerical evaluation on the performance of the algorithms and the effectiveness of deploying closed-access small cells in heterogeneous wireless networks to address the tradeoff between energy saving and feasibility of users satisfying their outage probability specifications.

Performance evaluation of fractional-slot tubular permanent magnet machines with low space harmonics

Abstract This paper evaluates the perforamnce of fractional-slot per pole winding configurations for tubular permanent magnet (PM) machines that can effectively eliminate the most undesirable space harmonics in a simple and cost-effective manner. The benefits of the proposed machine topology winding configurations are illustrated through comparison with 9-slot, 10-pole tubular PM machine developed for a free piston energy converter under the same specification and volumetric constraints. It has been shown that the proposed machine topology results in more than 7 times reduction in the eddy current loss in the mover magnets and supporting tube, and hence avoids potential problem of excessive mover temperature and risk of demagnetization.

Optimum design of composite steel frames with semi-rigid connections and column bases via genetic algorithm

A genetic algorithm-based minimum weight design method is presented for steel frames containing composite beams, semi-rigid connections and column bases. Genetic Algorithms carry out optimum steel frames by selecting suitable profile sections from a specified list including 128 W sections taken from American Institute of Steel Construction (AISC). The displacement and stress constraints obeying AISC Allowable Stress Design (ASD) specification and geometric (size) constraints are incorporated in the optimization process. Optimum designs of three different plane frames with semi-rigid beam-to-column and column-to-base plate connections are carried out first without considering concrete slab effects on floor beams in finite element analyses. The same optimization procedures are then repeated for the case of frames with composite beams. A program is coded in MATLAB for all optimization procedures. Results obtained from the examples show the applicability and robustness of the method. Moreover, it is proved that consideration of the contribution of concrete on the behavior of the floor beams enables a lighter and more economical design for steel frames with semi-rigid connections and column bases.

Uplink QoS Scheduling Algorithm with Delay Estimation for LTE Systems

In this paper, we propose a quality of service (QoS) uplink scheduling algorithm for long term evolution (LTE) that collaborates with delay estimation. Unlike downlink scheduling, uplink scheduling cannot incorporate packet delay information due to specification constraints of LTE. The limited information results in difficulty ensuring QoS when conventional scheduling algorithms are employed. To overcome the limitation in QoS scheduling, we estimate delays taking into account buffer status reports (BSRs) of LTE and propose an uplink QoS scheduling scheme with delay estimation. For given system parameters, such as BSR period, an optimal parameter for estimation is also proposed and analyzed. Via simulations, we show that the proposed QoS scheduling scheme can reduce the number of unsatisfied QoS services by half, even with a simple estimation.