Minimizing the cost of integrated systems approach to process control and maintenance model by EWMA control chart using genetic algorithm

This paper studies integrated systems approach to Statistical Process Control (SPC) and Maintenance Management (MM). Previously, only four policies which are in control alert signal, out of control alert signal, in control no signal, and out of control no signal, were used in the consideration (Zhou & Zhu, 2008). The objectives of this research are to develop an integrated model between Statistical Process Control and Planned Maintenance of the EWMA control chart. To do this, warning limit is considered to increase the policy from four to six such as warning limit alert signal and warning limit no signal. A mathematical model is given to analyze the cost of the integrated model before the genetic algorithm approach is used to find the optimal values of six variables (n, h, w, k, η, r) that minimize the hourly cost. A comparison between four-policy and six-policy models shows that the six policy model contains the hourly cost higher than that of the four policy model, it is because the addition of the warning limit in the model leads into increased ability of defective product detection. This consequently results to the increase of repairing and maintenance of machines; therefore the hourly cost is higher. Finally, multiple regressions are employed to demonstrate the effect of cost parameters.     

Study of generalized delay-timers in alarm configuration

In process plants, alarms are configured to notify operators of any abnormalities or faults. However, in practice a majority of raised alarms are false or nuisance and create problems for operators as they face an increasing number of alarms to handle. Adding delay-timers is a simple technique that can reduce this problem and is widely exercised in industry. In this work we propose a generalized delay-timer framework where instead of consecutive n samples in the conventional case, n₁ out of n consecutive samples (n₁ ≤ n) are considered to raise an alarm. For the generalized delay-timer, three important performance indices, namely, the false alarm rate (FAR), the missed alarm rate (MAR) and the expected detection delay (EDD), are calculated using Markov processes. Moreover, the performance and sensitivity of generalized delay-timers are compared with conventional delay-timers.     

A decision rules approach for improvement of airport service quality

The passenger’s perception of the airport’s level of service (LOS) may have a significant impact on promoting or discouraging future tourism and business activities. In this study, we take a look at this problem, but unlike in traditional statistical analysis, we apply a new method, the dominance-based rough set approach (DRSA), to an airport service survey. A set of “if … then … ” decision rules is used in the preference model. The passengers indicate their perception of airport LOS by rating a set of criteria/attributes. The proposed method provides practical information that should be of help to airport planners, designers, operators, and managers to develop LOS improvement strategies. The model was implemented using survey data from a large sample of customers from an international airport in Taiwan.     

Bin packing problems with rejection penalties and their dual problems

In this paper we consider the following problems: we are given a set of n items {u₁, … , u_n} and a number of unit-capacity bins. Each item u_i has a size w_i ∈ (0, 1] and a penalty p_i ⩾ 0. An item can be either rejected, in which case we pay its penalty, or put into one bin under the constraint that the total size of the items in the bin is no greater than 1. No item can be spread into more than one bin. The objective is to minimize the total number of used bins plus the total penalty paid for the rejected items. We call the problem bin packing with rejection penalties, and denote it as BPR. For the on-line BPR problem, we present an algorithm with an absolute competitive ratio of 2.618 while the lower bound is 2.343, and an algorithm with an asymptotic competitive ratio arbitrarily close to 1.75 while the lower bound is 1.540. For the off-line BPR problem, we present an algorithm with an absolute worst-case ratio of 2 while the lower bound is 1.5, and an algorithm with an asymptotic worst-case ratio of 1.5. We also study a closely related bin covering version of the problem. In this case p_i means some amount of profit. If an item is rejected, we get its profit, or it can be put into a bin in such a way that the total size of the items in the bin is no smaller than 1. The objective is to maximize the number of covered bins plus the total profit of all rejected items. We call this problem bin covering with rejection (BCR). For the on-line BCR problem, we show that no algorithm can have absolute competitive ratio greater than 0, and present an algorithm with asymptotic competitive ratio 1/2, which is the best possible. For the off-line BCR problem, we also present an algorithm with an absolute worst-case ratio of 1/2 which matches the lower bound.     

Improved fault detection employing hybrid memetic fuzzy modeling and adaptive filters

We propose an improved fault detection (FD) scheme based on residual signals extracted on-line from system models identified from high-dimensional measurement data recorded in multi-sensor networks. The system models are designed for an all-coverage approach and comprise linear and non-linear approximation functions representing the interrelations and dependencies among the measurement variables. The residuals obtained by comparing observed versus predicted values (i.e., the predictions achieved by the system models) are normalized subject to the uncertainty of the models and are supervised by an incrementally adaptive statistical tolerance band. Upon violation of this tolerance band, a fault alarm is triggered. The improved FD methods comes with two the main novelty aspects: (1) the development of an enhanced optimization scheme for fuzzy systems training which builds upon the SparseFIS (Sparse Fuzzy Inference Systems) approach and enhances it by embedding genetic operators for escaping local minima ⟶ a hybrid memetic (sparse) fuzzy modeling approach, termed as GenSparseFIS. (2) The design and application of adaptive filters on the residual signals, over time, in a sliding-window based incremental/decremental manner to smoothen the signals and to reduce the false positive rates. This gives us the freedom to tighten the tolerance band and thus to increase fault detection rates by holding the same level of false positives. In the results section, we verify that this increase is statistically significant in the case of adaptive filters when applying the proposed concepts onto four real-world scenarios (three different ones from rolling mills, one from engine test benches). The hybridization of sparse fuzzy inference systems with genetic algorithms led to the generation of more high quality models that can in turn be used in the FD process as residual generators. The new hybrid sparse memetic modeling approach also achieved fuzzy systems leading to higher fault detection rates for some scenarios.     

Optimal number of minimal repairs before replacement based on a cumulative repair-cost limit policy

In this paper, we consider a replacement model with minimal repair based on a cumulative repair-cost limit policy, where the information of all repair costs is used to decide whether the system is repaired or replaced. As a failure occurs, the system experiences one of the two types of failures: a type-I failure (repairable) with probability q, rectified by a minimal repair; or a type-II failure (non-repairable) with probability p (=1 − q) that calls for a replacement. Under such a policy, the system is replaced anticipatively at the nth type-I failure, or at the kth type-I failure (k < n) at which the accumulated repair cost exceeds the pre-determined threshold, or any type-II failure, whichever occurs first. The object of this paper is to find the optimal number of minimal repairs before replacement that minimizes the long-run expected cost per unit time of this polish. Our model is a generalization of several classical models in maintenance literature, and a numerical example is presented for illustration.     

Evaluating the use of ECG signal in low frequencies as a biometry

Traditional strategies, such as fingerprinting and face recognition, are becoming more and more fraud susceptible. As a consequence, new and more fraud proof biometrics modalities have been considered, one of them being the heartbeat pattern acquired by an electrocardiogram (ECG). While methods for subject identification based on ECG signal work with signals sampled in high frequencies (>100 Hz), the main goal of this work is to evaluate the use of ECG signal in low frequencies for such aim. In this work, the ECG signal is sampled in low frequencies (30 Hz and 60 Hz) and represented by four feature extraction methods available in the literature, which are then feed to a Support Vector Machines (SVM) classifier to perform the identification. In addition, a classification approach based on majority voting using multiple samples per subject is employed and compared to the traditional classification based on the presentation of single samples per subject each time. Considering a database composed of 193 subjects, results show identification accuracies higher than 95% and near to optimality (i.e., 100%) when the ECG signal is sampled in 30 Hz and 60 Hz, respectively, being the last one very close to the ones obtained when the signal is sampled in 360 Hz (the maximum frequency existing in our database). We also evaluate the impact of: (1) the number of training and testing samples for learning and identification, respectively; (2) the scalability of the biometry (i.e., increment on the number of subjects); and (3) the use of multiple samples for person identification.     

Fire detection model in Tibet based on grey-fuzzy neural network algorithm

The fire signals are much weaker in low oxygen concentration and low pressure environment such as Tibet. Fire detectors which were calibrated in correlating standard conditions cannot work well in such condition. This paper presents a synthesis method of GM(1, 1) grey prediction model and adaptive neuro-fuzzy inference system (ANFIS) in advance to detect fire and to make it work in the environment. The theoretical analysis of the algorithm and experimental evaluation in Tibet are presented. In this process, the grey GM(1, 1) predict model can anticipate the development of fire signals without any assumption, thus allowing earlier fire alarm than traditional fire detection equipments, meanwhile, ANFIS can make sure the data processing more accurate to avoid false alarms. This work will supply useful suggestions with the fire detectors design in low ambient pressure and low oxygen concentration such as Tibet, etc.     

On decidability of monadic logic of order over the naturals extended by monadic predicates

A fundamental result of Büchi states that the set of monadic second-order formulas true in the structure (Nat,  <) is decidable. A natural question is: what monadic predicates (sets) can be added to (Nat,  <) while preserving decidability? Elgot and Rabin found many interesting predicates P for which the monadic theory of 〈Nat, <,  P〉 is decidable. The Elgot and Rabin automata theoretical method has been generalized and sharpened over the years and their results were extended to a variety of unary predicates. We give a sufficient and necessary model-theoretical condition for the decidability of the monadic theory of (Nat, <, P₁, … , P_n).We reformulate this condition in an algebraic framework and show that a sufficient condition proposed previously by O. Carton and W.Thomas is actually necessary. A crucial argument in the proof is that monadic second-order logic has the selection and the uniformization properties over the extensions of (Nat, <) by monadic predicates. We provide a self-contained proof of this result.     

Fabrication and characterization of a thermal switch

The development of a thermal switch based on arrays of liquid–metal micro-droplets is presented. Prototype thermal switches are assembled from a silicon substrate on which is deposited an array of 1600 30-μm liquid–metal micro-droplets. The liquid–metal micro-droplet array makes and breaks contact with a second bare silicon substrate. A gap between the two silicon substrates is filled with either air at 760 Torr, air at of 0.5 Torr or xenon at 760 Torr. Heat transfer and thermal resistance across the thermal switches are measured for “on” (make contact) and “off” (break contact) conditions using guard-heated calorimetry. The figure of merit for a thermal switch, the ratio of “off” state thermal resistance over “on” state thermal resistance, R_off/R_on, is 129 ± 43 for a xenon-filled thermal switch that opens 100 μm and 60 ± 17 for an 0.5 Torr air-filled thermal switch that opens 25 μm. These thermal resistance ratios are shown to be markedly higher than values of R_off/R_on for a thermal switch based on contact between polished silicon surfaces. Transient temperature measurements for the liquid–metal micro-droplet switches indicate thermal switching times of less than 100 ms. Switch lifetimes are found to exceed one-million cycles.     

Comparative analysis of a randomized N-policy queue: An improved maximum entropy method

We analyze a single removable and unreliable server in an M/G/1 queueing system operating under the 〈p, N〉-policy. As soon as the system size is greater than N, turn the server on with probability p and leave the server off with probability (1 ? p). All arriving customers demand the first essential service, where only some of them demand the second optional service. He needs a startup time before providing first essential service until there are no customers in the system. The server is subject to break down according to a Poisson process and his repair time obeys a general distribution. In this queueing system, the steady-state probabilities cannot be derived explicitly. Thus, we employ an improved maximum entropy method with several well-known constraints to estimate the probability distributions of system size and the expected waiting time in the system. By a comparative analysis between the exact and approximate results, we may demonstrate that the improved maximum entropy method is accurate enough for practical purpose, and it is a useful method for solving complex queueing systems.     

Spoken emotion recognition through optimum-path forest classification using glottal features

A new method for the recognition of spoken emotions is presented based on features of the glottal airflow signal. Its effectiveness is tested on the new optimum path classifier (OPF) as well as on six other previously established classification methods that included the Gaussian mixture model (GMM), support vector machine (SVM), artificial neural networks – multi layer perceptron (ANN-MLP), k-nearest neighbor rule (k-NN), Bayesian classifier (BC) and the C4.5 decision tree. The speech database used in this work was collected in an anechoic environment with ten speakers (5 M and 5 F) each speaking ten sentences in four different emotions: Happy, Angry, Sad, and Neutral. The glottal waveform was extracted from fluent speech via inverse filtering. The investigated features included the glottal symmetry and MFCC vectors of various lengths both for the glottal and the corresponding speech signal. Experimental results indicate that best performance is obtained for the glottal-only features with SVM and OPF generally providing the highest recognition rates, while for GMM or the combination of glottal and speech features performance was relatively inferior. For this text dependent, multi speaker task the top performing classifiers achieved perfect recognition rates for the case of 6th order glottal MFCCs.     

Transition-aware DVS algorithm for real-time systems using tree structure analysis

Dynamic voltage scaling (DVS) is a key technique for embedded real-time systems to reduce energy consumption by lowering the supply voltage and operating frequency. Many existing DVS algorithms have to generate the canonical schedules or estimate the lengths of slack time in advance for generating the voltage scaling decisions. Therefore, these methods have to compute the schedules with exponential time complexities in general. In this paper, we consider a set of jitter-controlled, independent, periodic, hard real-time tasks scheduled according to preemptive pinwheel model. Our approach constructs a tree structure corresponding to a schedule and maintains the data structure at each early-completion point. Our approach consists of off-line and on-line algorithms which consider the effects of transition time and energy. The off-line and on-line algorithm takes O(k + n log n) and O(k + (p_max/p_min)) time complexity, respectively, where n, k, p_max and p_min denotes the number of tasks, jobs, longest and shortest task period, respectively. Experimental results show that the proposed approach is effective in reducing computational complexity, transition time and energy overhead.     

Modal analysis and modified cascade neural networks in identification of geometrical parameters of circular arches

A hybrid computational system, composed of the finite element method (FEM) and cascade neural network system (CNNs), is applied to the identification of three geometrical parameters of elastic arches, i.e. span l, height f and cross-sectional thickness h. FEM is used in the direct (forward) analysis, which corresponds to the mapping α = {l, f, h} → {ω_j}, where: α – vector of control parameters, ω_j – arch eigenfrequencies. The reverse analysis is related to the identification procedure in which the reverse mapping is performed {ω_j} → {α_i}. For the identification purposes a recurrent, three level CNNs of structure (D^k-H^k-1)_s was formulated, where: k – recurrence step, s = I, II, III-levels of cascade system. The Semi-Bayesian approach is introduced for the design of CNNs applying the MML Maximum Marginal Likelihood) criterion. The computation of hyperparameters is performed by means of the Bayesian procedure evidence. The numerical analysis proves a great numerical efficiency of the proposed hybrid approach for both the perfect (noiseless) values of eigenfrequencies and noisy ones simulated by an added artificial noise.     

Feasible prediction in S-system models of genetic networks

To construct the model of gene expression using microarray techniques can reveal the regulation rules from the gene expression profiles. From S-system model, it is able to analyze the regulatory system dynamics. However, with 2N(N + 1) parameters (called a set), an S-system model of N-gene genetic networks takes lots of iterations to have convergent gene expression profiles. To mining the association between the gene expression profiles and 2N(N + 1) parameters may provide information about the probability of the convergent gene expression profiles instead of trying to obtain the convergent gene expression profiles in lots of iteration. Based on this novel approach, higher accuracy of the binary classifier can be used to analyze and prediction the convergence of the gene expression profiles from an initial set to reduce the search time of the inference problem. This paper applies popular data mining algorithms to the classification tasks and compares their accuracy rates with a dataset (250 cases, including 176 training cases and 74 test cases). According to decision rules of the chosen classifier, we can provide a convergence prediction of time-series gene expression profiles on the given set of parameters.     

Neuro-fuzzy model based on digital images for the monitoring of coffee bean color during roasting in a spouted bed

An adaptive-network-based fuzzy inference system based on color image analysis was used to estimate coffee bean moisture content during roasting in a spouted bed. The neuro-fuzzy model described the grain moisture changes as a function of brightness (L^*), browning index (BI) and the distance to a defined standard (ΔE). An image-capture device was designed to monitor color variations in the L^*a^*b^* space for high temperatures samples taken from the reactor. The proposed model was composed of three Gaussian-type fuzzy sets based on the scatter partition method. The neuro-fuzzy model was trained with the Back-propagation algorithm using experimental measurements at three air temperature levels (400, 450 and 500 °C). The performance of the neuro-fuzzy model resulted better compared to conventional methods obtaining a coefficient of determination > 0.98, a root mean square error < 0.002 and a modified Schwarz–Rissanen information criterion < 0. The simplicity of the model and its robustness against changes in the input variables make it suitable for monitoring on-line the roasting process.     

Computational assessment of several hydrogen-free high energy compounds

Tetrazino-tetrazine-tetraoxide (TTTO) is an attractive high energy compound, but unfortunately, it is not yet experimentally synthesized so far. Isomerization of TTTO leads to its five isomers, bond-separation energies were empolyed to compare the global stability of six compounds, it is found that isomer 1 has the highest bond-separation energy (1204.6 kJ/mol), compared with TTTO (1151.2 kJ/mol); thermodynamic properties of six compounds were theoretically calculated, including standard formation enthalpies (solid and gaseous), standard fusion enthalpies, standard vaporation enthalpies, standard sublimation enthalpies, lattice energies and normal melting points, normal boiling points; their detonation performances were also computed, including detonation heat (Q, cal/g), detonation velocity (D, km/s), detonation pressure (P, GPa) and impact sensitivity (h₅₀, cm), compared with TTTO (Q = 1311.01 J/g, D = 9.228 km/s, P = 40.556 GPa, h₅₀ = 12.7 cm), isomer 5 exhibites better detonation performances (Q = 1523.74 J/g, D = 9.389 km/s, P = 41.329 GPa, h₅₀ =  28.4 cm).     

Solutions to the functional equation I(x, y) = I(x, I(x, y)) for three types of fuzzy implications derived from uninorms

This paper investigates an iterative Boolean-like law with fuzzy implications derived from uninorms. More precisely, we characterize the solutions to the functional equation I(x, y) = I(x, I(x, y)) that involve RU-, (U, N)- and QLU-implications generated by the most usual classes of uninorms.     

Sleep in a live-in mining operation: The influence of start times and restricted non-work activities

The amount of sleep obtained between shifts is influenced by numerous factors including the length of work and rest periods, the timing of the rest period relative to the endogenous circadian cycle and personal choices about the use of non-work time. The current study utilised a real-world live-in mining environment to examine the amount of sleep obtained when access to normal domestic, family and social activities was restricted. Participants were 29 mining operators (26 male, average age 37.4 ± 6.8 years) who recorded sleep, work and fatigue information and wore an activity monitor for a cycle of seven day shifts and seven night shifts (both 12 h) followed by either seven or fourteen days off. During the two weeks of work participants lived on-site. Total sleep time was significantly less (p < 0.01) while on-site on both day (6.1 ± 1.0 h) and night shifts (5.7 ± 1.5 h) than days off (7.4 ± 1.4 h). Further, night shift sleep was significantly shorter than day-shift sleep (p < 0.01). Assessment of subjective fatigue ratings showed that the sleep associated with both days off and night shifts had a greater recovery value than sleep associated with day shifts (p < 0.01). While on-site, participants obtained only 6 h of sleep indicating that the absence of competing domestic, family and social activities did not convert to more sleep. Factors including shift start times and circadian influences appear to have been more important.