A novel methodology for cross-technology interoperability in CNC machining

In CNC part programmes, the lack of standardisation for representing part geometry and semantics of manufacturing operations leads to the necessity for existence of a unique part programme for each machine. Generating multiple programmes for producing the same part is not a value adding activity and is very time consuming. This wasteful activity can be eliminated if users are given the ability to write an NC program for a specific machine and robustly convert the program to syntax suitable for another CNC machine with a different structure. This, cross-technology interoperability, would enable for parts manufactured on old CNC machines using legacy code to be manufactured on new CNC machines by automatically converting the programmes. Every NC programme is written based on various categories of information such as: cutting tool specifications, process planning knowledge and machine tool information. This paper presents an approach for cross-technology interoperability by refining high-level process information (i.e., geometric features on the part and embedded manufacturing resource data) from NC programmes. These refined items of information stored in compliance with the ISO14649 (STEP-NC) standard may then be combined with new manufacturing resource information to generate NC code in a format that is compatible with machines based on different technologies. The authors provide a framework for this process of identification, semantic interpretation and re-integration of information. The focus of this paper is on asymmetric rotational components as the initial application area. To demonstrate the proposed cross-technology interoperability approach, a C-axis CNC turn–mill machine and a 4 axis CNC machining centre have been used with a simple test component.     

Combining Rough Sets and Bayes' Rule

In rough set theory with every decision rule two conditional probabilities, called certainty and coverage factors, are associated. These two factors are closely related with the lower and the upper approximation of a set, basic notions of rough set theory. It is shown that these two factors satisfy the Bayes' rule.
The Bayes' rule in our case simply shows some relationship in the data, without referring to prior and posterior probabilities intrinsically associated with Bayesian inference. This relationship can be used to "invert" decision rules, i.e., to find reasons (explanation) for decisions thus providing inductive as well as deductive inference in our scheme.     

Covering-based rough set classification system

Medical data classification is applied in intelligent medical decision support system to classify diseases into different categories. Several classification methods are commonly used in various healthcare settings. These techniques are fit for enhancing the nature of prediction, initial identification of sicknesses and disease classification. The categorization complexities in healthcare area are focused around the consequence of healthcare data investigation or depiction of medicine by the healthcare professions. This study concentrates on applying uncertainty (i.e. rough set)-based pattern classification techniques for UCI healthcare data for the diagnosis of diseases from different patients. In this study, covering-based rough set classification (i.e. proposed pattern classification approach) is applied for UCI healthcare data. Proposed CRS gives effective results than delicate pattern classifier model. The results of applying the CRS classification method to UCI healthcare data analysis are based upon a variety of disease diagnoses. The execution of the proposed covering-based rough set classification is contrasted with other approaches, such as rough set (RS)-based classification methods, Kth nearest neighbour, improved bijective soft set, support vector machine, modified soft rough set and back propagation neural network methodologies using different evaluating measures.

     

Spreadsheet calculations for the machine interference queueing model

The machine interference queueing model has numerous applications in different areas. Yet it is seldom used due, mainly, to the complexity of the associated calculations. In this paper, a simple and easy-to-implement spreadsheet application of the model is presented. This application calculates state probabilities and mean values for key parameters: machine utilization, operators’ load and system throughput. The application is parametric in the numbers of machines and operators, mean time between (machine) failures, and mean service time. This facilitates model analysis and turns the spreadsheet application into a powerful tool for analysis, design and decision support.     

Nonparametric estimation of the threshold at an operating point on the ROC curve

In the problem of binary classification (or medical diagnosis), the classification rule (or diagnostic test) produces a continuous decision variable which is compared to a critical value (or threshold). Test values above (or below) that threshold are called positive (or negative) for disease. The two types of errors associated with every threshold value are Type I (false positive) and Type II (false negative) errors. The Receiver Operating Curve (ROC) describes the relationship between probabilities of these two types of errors. The inverse problem is considered; i.e., given the ROC curve (or its estimate) of a particular classification rule, one is interested in finding the value of the threshold ξ that leads to a specific operating point on that curve. A nonparametric method for estimating the threshold is proposed. Asymptotic distribution is derived for the proposed estimator. Results from simulated data and real-world data are presented for finite sample size. Finding a particular threshold value is crucial in medical diagnoses, among other fields, where a medical test is used to classify a patient as “diseased” or “nondiseased” based on comparing the test result to a particular threshold value. When the ROC is estimated, an operating point is obtained by fixing probability of one type of error, and obtaining the other one from the estimated curve. Threshold estimation can then be viewed as a quantile estimation for one distribution but with the utilization of the second one.     

Robotic Cells with Parallel Machines: Throughput Maximization in Constant Travel-Time Cells

We present a general analysis of the problem of sequencing operations in bufferless robotic cell flow shops with parallel machines. Our focus will be cells that produce identical parts. The objective is to find a cyclic sequence of robot moves that maximizes the steady state throughput. Parallel machines are used in the industry to increase throughput, most typically at bottleneck processes having larger processing times.Efficient use of parallel machines requires that several parts be processed in one cycle of robot movements. We analyze such cycles for constant travel-time robotic cells. The number of cycles that produce several parts is very large, so we focus on a subclass called blocked cycles. In this class, we find a dominating subclass called LCM Cycles.The results and the analysis in this paper offer practitioners (i) guidelines to determine whether parallel machines will be cost-effective for a given implementation, (ii) a simple formula for determining how many copies of each machine are required to meet a particular throughput rate, and (iii) an optimal sequence of robot moves for a cell with parallel machines under a certain common condition on the processing times.     

Online hierarchical scheduling: An approach using mathematical programming

This paper considers an online hierarchical scheduling problem on parallel identical machines. We are given a set of m machines and a sequence of jobs. Each machine has a different hierarchy, and each job also has a hierarchy associated with it. A job can be assigned to a machine only if its hierarchy is no less than that of the machine. The objective is to minimize the makespan, i.e., the maximum load of all machines. Two models are studied in the paper. For the fractional model, we present an improved algorithm and lower bounds. Both the algorithm and the lower bound are based on solutions of mathematical programming. For any given m, our algorithm is optimal by numerical calculation. For the integral model, we present both a general algorithm for any m, and an improved algorithm with better competitive ratios of 2.333 and 2.610 for m=4 and 5, respectively.     

串行生产线中机器维修工人的任务分配问题研究

在串行生产线中, 机器会发生故障而且故障间隔时间随机, 因此需要维修工人及时维修, 使得故障的机器恢复加工能力, 否则就可能导致系统吞吐率降低. 如何在满足系统吞吐率的前提下, 使用尽可能少的维修工人来完成机器的维修任务, 本文称这样一个全新的问题为串行生产线中机器维修工人的任务分配问题. 针对该问题, 本文首先建立了问题的优化模型, 并将该优化问题转换为多个判定问题进行求解; 然后, 通过合理地定义机器的维修工作量, 使得判定问题可以类比为并行机调度问题; 最后, 采用了一种基于最长处理时间优先算法(Longest processing time, LPT)和回溯策略的启发式算法, 搜索最优的维修工人任务分配方式. 实验结果表明, 该方法能有效求解维修工人的任务分配问题.     

On shift register realization of sequential machines

This paper deals with the modular realization of sequential machines using shift registers as the modules. For a given minimal machine, the minimum number of modules required for its realization has been determined. It is well known that the number of modules required to realize a given machine can sometimes be reduced by realizing a non-minimal machine equivalent to the given minimal one, i.e. by assigning multiple codes to the states of the given minimal machine. This case is dealt with in the second part of the paper.     

On-line Multi-threaded Scheduling

On-line scheduling problems are studied with jobs organized in a number of sequences called threads. Each job becomes available as soon as a scheduling decision is made on all preceding jobs in the same thread.We consider two different on-line paradigms. The first one models a sort of batch process: a schedule is constructed, in an on-line way, which is to be executed later. The other one models a real-time planning situation: jobs are immediately executed at the moment they are assigned to a machine.The classical objective functions of minimizing makespan and minimizing average completion time of the jobs are studied.We establish a fairly complete set of results for these problems. One of the highlights is that List Scheduling is a best possible algorithm for the makespan problem under the real-time model if the number of machines does not exceed the number of threads by more than 1. Another one is a polynomial time best possible algorithm for minimizing the average completion time on a single machine under both on-line paradigms.     

Fusion of face and speech data for person identity verification

Biometric person identity authentication is gaining more and more attention. The authentication task performed by an expert is a binary classification problem: reject or accept identity claim. Combining experts, each based on a different modality (speech, face, fingerprint, etc.), increases the performance and robustness of identity authentication systems. In this context, a key issue is the fusion of the different experts for taking a final decision (i.e., accept or reject identity claim). We propose to evaluate different binary classification schemes (support vector machine, multilayer perceptron, C4.5 decision tree, Fisher's linear discriminant, Bayesian classifier) to carry on the fusion. The experimental results show that support vector machines and Bayesian classifier achieve almost the same performances, and both outperform the other evaluated classifiers.     

Approximation and universality of fuzzy Turing machines

Fuzzy Turing machines are the formal models of fuzzy algorithms or fuzzy computations. In this paper we give several different formulations of fuzzy Turing machine, which correspond to nondeterministic fuzzy Turing machine using max-＊ composition for some t-norm＊ （or NFTM＊, for short）, nondeterministic fuzzy Turing machine （or NFTM）, deterministic fuzzy Turing machine （or DFTM）, and multi-tape versions of fuzzy Turing machines. Some distinct results compared to those of ordinary Turing machines are obtained. First, it is shown that NFTM＊, NFTM, and DFTM are not necessarily equivalent in the power of recognizing fuzzy languages if the t-norm＊ does not satisfy finite generated condition, but are equivalent in the approximation sense. That is to say, we can approximate an NFTM＊ by some NFTM with any given accuracy; the related constructions are also presented. The level characterization of fuzzy recursively enumerable languages and fuzzy recursive languages are exploited by ordinary r.e. languages and recursive languages. Second, we show that universal fuzzy Turing machine exists in the approximated sense. There is a universal fuzzy Turing machine that can simulate any NFTM＊ on it with a given accuracy.     

Synthesis of distinguishing test cases for timed finite state machines

This paper is devoted to the generation of distinguishing experiments with completely specified timed finite state machines. It is shown, in particular, that two completely specified nondeterministic finite state machines can be distinguished by a multiple preset experiment if and only if these finite state machines are not equivalent. Two finite state machines can be distinguished by a simple adaptive experiment if and only if they are r-distinguishable, i.e., have no common completely specified reduction. The corresponding adaptive experiment is described by a special timed finite state machine. The procedure for constructing such an r-distinguishing timed finite state machine is proposed.     

Performance evaluation of fuzzy-based decision system for pool

An important step in any machine intelligence is to automatically and reliably decide on a solution/outcome based on the inputs given. The mapping of the inputs to an output decision often follows a set of critical rules that mimic the decision that would often be decided by a human being. In this paper, the performance of a fuzzy-based decision system for playing a game of pool is presented by comparing the results involving direct hit with two known systems. The performance is also compared to the decision that would be made by the human players. The design steps for the fuzzy-based decision system are presented using the fuzzy logic approach by employing a three-input one-output fuzzy inference system based on a set of 24 rules. The results have shown that fuzzy logic is able to accurately decide on the best move, which is as good as the human players themselves.     

Evolutionary risk preference inference model using fuzzy support vector machine for road slope collapse prediction

Road slope collapse events are frequent occurrences in Taiwan, often exacerbated by earthquakes and/or heavy rainfall. Such collapses disrupt transportation, damage infrastructure and property, and may cause injuries and fatalities. While significant efforts are regularly invested in reducing road slope collapse risk, most focus exclusively on limiting the potential for slope failure. Collapse prediction efforts may result in inference errors that cause allocated road slope maintenance resources to be expended inefficiently, resulting in relatively higher collapse risk than should be achievable under ideal circumstances. Most maintenance programs rely on decision maker risk preferences, as his/her knowledge and experience can contribute to risk assessment decision making. The decision maker is capable of choosing an acceptable balance between two types of inference error, i.e., α and β errors. This preference may later be used as guidance to minimize inference error. This paper proposed the evolutionary risk preference fuzzy support vector machine inference model (ERP-FSIM) as a hybrid AI system able to make predictions regarding road slope collapse that takes decision maker risk preference into account. Validation results demonstrate ERP-FSIM viability, as level of average error both for the training set and validation set conform to the decision maker risk preference ratio and is significantly lower than the error tolerance of ±10%.     

ON THE STATISTICAL ESTIMATION OF STOCHASTIC FINITE-STATE TRANSDUCERS IN MACHINE TRANSLATION

The inference of finite-state transducers from bilingual training data plays an important role in many natural-language tasks and mainly in machine translation. However, there are only a few techniques to infer such models. One of these techniques is the grammatical inference and alignments for transducer inference (GIATI) technique that has proven to be very adequate for speech translation, text-input machine translation, or computer-assisted translation. GIATI is a heuristic technique that requires segmented training data (i.e., the input sentences and the output sentences must be segmented with the restriction that the input segments and the output segments must be monotone aligned). For the purpose of obtaining segmented training data, pure statistical word-alignment models are used. This technique is revisited in this article. The main goal is to formally derive the complete GIATI technique using classical expectation-maximization statistical estimation procedure. This new approach allows us to avoid a hard dependence on heuristic “external” statistical techniques (statistical alignments and n-grams). A first set of experimental results obtained in a machine-translation task are also reported to initially validate this new version of the inference technique of finite-state transducers.     

heuristics for parallel machine scheduling with delivery times

A parallel machine scheduling problem is considered in which each job has a processing time and a delivery time. The objective is to find a schedule which minimizes the time by which all jobs are delivered. For a single machine this problem is easily solved in polynomial time, form2 machines it becomes NP-hard. Several heuristics using list scheduling as a subroutine are proposed and a tight worst-case analysis is given. The best one of our heuristics has a worst-case performance guarantee of 2–2/(m+1). For the on-line case we give a heuristic with the (best possible) worst-case performance of two.This research was supported by the Christian Doppler Laboratorium für Diskrete Optimierung.     

Online scheduling on semi-related machines

We study the problem of minimizing the makespan on related machines in the following setting: jobs arrive over time, and the machines may become available or unavailable. In either case, advance warning is given, i.e., the next point of time where a job is released or a machine changes state is revealed a little time ahead. We present an optimal online algorithm for this problem.     

On the equivalence between small-step and big-step abstract machines: a simple application of lightweight fusion

We show how Ohori and Sasano's recent lightweight fusion by fixed-point promotion provides a simple way to prove the equivalence of the two standard styles of specification of abstract machines: (1) in small-step form, as a state-transition function together with a ‘driver loop’, i.e., a function implementing the iteration of this transition function; and (2) in big-step form, as a tail-recursive function that directly maps a given configuration to a final state, if any. The equivalence hinges on our observation that for abstract machines, fusing a small-step specification yields a big-step specification. We illustrate this observation here with a recognizer for Dyck words, the CEK machine, and Krivine's machine with call/cc.The need for such a simple proof is motivated by our current work on small-step abstract machines as obtained by refocusing a function implementing a reduction semantics (a syntactic correspondence), and big-step abstract machines as obtained by CPS-transforming and then defunctionalizing a function implementing a big-step semantics (a functional correspondence).     

Computing association probabilities using parallel Boltzmannmachines

A new computational method is presented for solving the data association problem using parallel Boltzmann machines. It is shown that the association probabilities can be computed with arbitrarily small errors if a sufficient number of parallel Boltzmann machines are available. The probability beta(i)(j) that the i th measurement emanated from the jth target can be obtained simply by observing the relative frequency with which neuron v(i,j) in a two-dimensional network is on throughout the layers. Some simple tracking examples comparing the performance of the Boltzmann algorithm to the exact data association solution and with the performance of an alternative parallel method using the Hopfield neural network are also presented.