Rule Parameters Research Articles

No-code method for lithography template cell parameterization for faster design cycles

This work presents a Python-based architecture packaged as a standalone tool, to enable the parameterization of lithography structures without the need for scripting. By feeding a lithography template file obtained from an existing layout editor into the tool, a ‘scaffold’ shape is created and recognised. The tool allows for the parameterization of created geometries and the establishment of parameterized rules between geometric features, which can be conveniently modified in tabular format. This work facilitates no-code procedural generation of geometrically distinct instances, significantly reducing the time required for complex lithography template development compared to traditional scripting methods.

Optimization of the multidimensional signal interpolator in a lower dimensional space

Adaptive multidimensional signal interpolators are developed. These interpolators take into account the presence and direction of boundaries of flat signal regions in each local neighborhood based on the automatic selection of the interpolating function for each signal sample. The selection of the interpolating function is performed by a parameterized rule, which is optimized in a parametric lower dimensional space. The dimension reduction is performed using rank filtering of local differences in the neighborhood of each signal sample. The interpolating functions of adaptive interpolators are written for the multidimensional, three-dimensional and two-dimensional cases. The use of adaptive interpolators in the problem of compression of multidimensional signals is also considered. Results of an experimental study of adaptive interpolators for real multidimensional signals of various types are presented.

Well-Collision-Avoidance Separation Rule

Summary The well-collision-avoidance separation rule presented in this paper is a culmination of the work and consensus of industry experts from both operators and service companies in the SPE Wellbore Positioning Technical Section (WPTS). This is the second of two papers and complements the first paper, SPE-184730-PA (Sawaryn et al. 2018), which described the collision-avoidance management practices. These practices are fundamental in establishing the environment in which a minimum allowable separation distance (MASD) (in m) between two adjacent wells can be effectively applied. A standardized collision-avoidance rule is recommended, complete with parameter values appropriate to the management of health, safety, and environment (HSE) risk, and benchmarks for testing it. Together, these should help eliminate the disparate and occasionally contradictory methods currently in use. The consequences of an unplanned intersection with an existing well can range from financial loss to a catastrophic blowout and loss of life. The process of well-collision avoidance involves rules that determine the allowable separation and the management of the associated directional planning and surveying activities. The proposed separation rule is dependent on the pedal-curve method and is expressed as a separation factor, a dimensionless number that is an adjusted center-to-center distance between wells divided by a function of the relative positional uncertainty between the two. The recommended values for the rule's parameters result from a comparison of various industry models and experience. The relationships between key concepts such as the MASD and allowable deviation from the plan (ADP) are discussed, together with their interpretation and application. The dependency on the error distributions of the survey-instrument performance models used to establish the tolerance lines is also discussed. The consequences of implementing a standardized separation rule across the industry are far-reaching. This affects slot separations, trajectories, drilling practices, surveying program, and well shut-in. We show how the MASD can be related to a probability of crossing and being in the unacceptable-risk region of an offset well. We show why this qualification is required for safe drilling practices to be preserved. Examples are presented in Appendices A through D to help the reader validate the calculations and the directional-drilling software necessary to perform them. The geometrical and statistical limitations of the methods are explained and areas are highlighted for further work. The methods outlined here, taken together with SPE-184730-MS, will improve efficiency in planning and executing wells and promote industry focus on the associated collision risks during drilling. The WPTS also supports the current development of API RP 78, Recommended Practices for Wellbore Positioning. Mathematical derivations or references are shown for all the calculations presented in the paper.

Composition of Constraint, Hypothesis and Error Models to improve interaction in Human–Machine Interfaces

Although there are many tasks where output strings are automatically generated from a set of evidence, they are not perfect and human intervention is often required to correct the result. In this paper we present a generic Symbol Input Interaction Method for Human–Machine Interfaces that combines multi-source information: an input Hypothesis Model, an Error Model, a Constraint Model and a user interaction scheme.We use Weighted Finite-State Transducers (WFSTs) to represent the different sources of information available: the initial hypotheses, the possible errors, the constraints imposed by the task (interaction language) and the user input. The fusion of these models to find the most probable output string can be performed efficiently by using carefully selected transducer operations. The proposed system initially suggests an output based on the set of hypotheses, possible errors and Constraint Models. Then, if human intervention is needed, a multimodal approach, where the user input is combined with the aforementioned models, is applied to produce, with a minimum user effort, the desired output. This approach offers the practical advantages of a de-coupled model (e.g. input-system + parameterized rules + post-processor), keeping at the same time the error-recovery power of an integrated approach, where all the steps of the process are performed in the same formal machine (as in a typical HMM in speech recognition) to avoid that an error at a given step remains unrecoverable in the subsequent steps. After a presentation of the theoretical basis of the proposed multi-source information system, its application to two real world problems, as an example of the possibilities of this architecture, is addressed. The experimental results obtained demonstrate that a significant user effort can be saved when using the proposed procedure. A simple demonstration, to better understand and evaluate the proposed system, is available on the web https://demos.iti.upv.es/hi/.

An enhanced self-adaptive differential evolution based on simulated annealing for rule extraction and its application in recognizing oil reservoir

This study proposes an enhanced self-adaptive differential evolution based on simulated annealing for rule extraction (ESADE-Rule). ESADE-Rule adopts IF-THEN as the rule's form, AND as the connection word for the rule's antecedent, class label as the rule's consequence. Rules are encoded as individuals in population of ESADE, and each individual represents a rule set consisting of three parts: the rule's parameters (including the controls of the rule, antecedents and class labels), the control parameters (including scaling factors and crossover rates), and the fitness value. Mutation and crossover operations of ESADE are further carried out on the population. Then, selection operation is conducted by comparing the fitness values, through which the best individual would be selected out and be decoded to obtain the optimal rule set. Besides, ten benchmark datasets and three logging datasets are adopted to test ESADE-Rule's performance. The three logging datasets contain oilsk81, oilsk83 and oilsk84 that come from Jianghan oilfield for testing recognition accuracy rate of reservoir. ESADE-Rule is compared with four rule extraction methods from the perspectives of recognition accuracy rate, rules' number, antecedents' number and samples' number that are not covered by the rule set. The results prove that ESADE-Rule performs better at recognition accuracy rate and interpretability. With oilsk81 as training data set, oilsk83 and oilsk84 as testing data set, the testing results of recognition accuracy rate of oil reservoirs illustrate that compared with other four rule extraction methods, ESADE-Rule can obtain more general rules set when the attributes of datasets are similar.

Pid Tuning Rule for Pressure Control Applications

In pressure control applications, servo-valves or variable displacement pumps are used to meter the flow into a supply line or a chamber with relatively constant capacity, thereby controlling its pressure under the influence of disturbances such as flows in and out of the controlled volume. For most applications proportional integral derivative (PID) controllers are suited and widely used in research and practice. However, tuning of PID parameters for pressure control is usually done by trial and error method due to the lack of applicable tuning rules for this case. The paper examines the dynamics of valve controlled pressure applications and proposes a set of effective but simple PID feedback gain formulas. They can be implemented by practitioners on the basis of data that in most cases is available from plant drawings and the valve data sheet. The tuning rule's parameters are based on a straight forward frequency response design. They yield swift and robust performance in simulation and experiment.

Artificial Evolution of Expressive Performance of Music: An Imitative Multi-Agent Systems Approach

This chapter introduces an imitative multi-agent system approach to generate expressive performances of music, based on agents’ individual parameterized musical rules. We have developed a system called IMAP (imitative multi-agent performer). Aside from investigating the usefulness of such an application of the imitative multi-agent paradigm, there is also a desire to investigate the inherent feature of diversity and control of diversity in this methodology: a desirable feature for a creative application, such as musical performance. To aid this control of diversity, parameterized rules are utilized based on previous expressive performance research. These are implemented in the agents using previously developed musical analysis algorithms. When experiments are run, it is found that agents are expressing their preferences through their music performances and that diversity can be generated and controlled.

Computerized segmentation and diagnostics of whole-body bone scintigrams

Bone scintigraphy or whole-body bone scan is one of the most common diagnostic procedures in nuclear medicine. Since expert physicians evaluate images manually some automated procedure for pathology detection is desired. A robust knowledge based methodology for segmenting body scans into the main skeletal regions is presented. The algorithm is simultaneously applied on anterior and posterior whole-body bone scintigrams. Expert knowledge is represented as a set of parameterized rules, used to support standard image processing algorithms. The segmented bone regions are parameterized with algorithms for classifying patterns so the pathologies can be classified with machine learning algorithms. This approach enables automatic scintigraphy evaluation of pathological changes, thus in addition to detection of point-like high-uptake lesions also other types can be discovered. Our study includes 467 consecutive, non-selected scintigrams. Automatic analysis of whole-body bone scans using our segmentation algorithm gives more accurate and reliable results than previous studies. Preliminary experiments show that our expert system based on machine learning closely mimics the results of expert physicians.

Automatic Design of Hierarchical Takagi–Sugeno Type Fuzzy Systems Using Evolutionary Algorithms

This paper presents an automatic way of evolving hierarchical Takagi-Sugeno fuzzy systems (TS-FS). The hierarchical structure is evolved using probabilistic incremental program evolution (PIPE) with specific instructions. The fine tuning of the if - then rule's parameters encoded in the structure is accomplished using evolutionary programming (EP). The proposed method interleaves both PIPE and EP optimizations. Starting with random structures and rules' parameters, it first tries to improve the hierarchical structure and then as soon as an improved structure is found, it further fine tunes the rules' parameters. It then goes back to improve the structure and the rules' parameters. This loop continues until a satisfactory solution (hierarchical TS-FS model) is found or a time limit is reached. The proposed hierarchical TS-FS is evaluated using some well known benchmark applications namely identification of nonlinear systems, prediction of the Mackey-Glass chaotic time-series and some classification problems. When compared to other neural networks and fuzzy systems, the developed hierarchical TS-FS exhibits competing results with high accuracy and smaller size of hierarchical architecture.

Computerized segmentation of whole-body bone scintigrams and its use in automated diagnostics

Bone scintigraphy or whole-body bone scan is one of the most common diagnostic procedures in nuclear medicine used in the last 25 years. Pathological conditions, technically poor image resolution and artefacts necessitate that algorithms use sufficient background knowledge of anatomy and spatial relations of bones in order to work satisfactorily. A robust knowledge based methodology for detecting reference points of the main skeletal regions that is simultaneously applied on anterior and posterior whole-body bone scintigrams is presented. Expert knowledge is represented as a set of parameterized rules which are used to support standard image-processing algorithms. Our study includes 467 consecutive, non-selected scintigrams, which is, to our knowledge the largest number of images ever used in such studies. Automatic analysis of whole-body bone scans using our segmentation algorithm gives more accurate and reliable results than previous studies. Obtained reference points are used for automatic segmentation of the skeleton, which is applied to automatic (machine learning) or manual (expert physicians) diagnostics. Preliminary experiments show that an expert system based on machine learning closely mimics the results of expert physicians.

Relieving pain and foreseeing death: a paradox about accountability and blame.

The authors look closely at the often cited Rule of Double Effect and argue that, in some cases, a physician can legitimately claim to have acted within the Rule's parameters. In other cases, however, physicians can abuse the notion of unintended consequences. The test case they offer concerns palliative care that causes foreseeable death.

A parametric rule for planning and management of multiple‐reservoir systems

A parametric rule for multireservoir system operation is formulated and tested. It is a generalization of the well‐known space rule of simultaneously accounting for various system operating goals, in addition to the standard goal of avoiding unnecessary spills, including avoiding leakage losses, avoiding conveyance problems, taking into account the impacts of the reservoir system topology, and assuring satisfaction of secondary uses. Theoretical values of the rule's parameters for each one of these isolated goals are derived. In practice, parameters are evaluated to optimize one or more objective functions selected by the user. The rule is embedded in a simulation model so that optimization requires repeated simulations of the system operation with specific values of the parameters each time. The rule is tested on the case of the multireservoir water supply system of the city of Athens, Greece, which is driven by all of the operating goals listed above. Two problems at the system design level are tackled. First, the total release from the system is maximized for a selected level of failure probability. Second, the annual operating cost is minimized for given levels of water demand and failure probability. A detailed simulation model is used in the case study. Sensitivity analysis of the rule's parameters revealed a subset of insensitive parameters that allowed for rule simplification. Finally, the rule is validated through comparison with a number of heuristic rules also applied to the test case.Appendices are available on microfiche. Order from AmericanGeophysical Union, 2000 Florida Avenue, N.W., Washington, DC 20009. Document 97WR01034M; $2.50. Payment must accompany order.

Load forecasting for multiple sites: development of an expert system-based technique

Most papers on short-term load forecasting (STLF) emphasize the use of a certain technique rather than the methodology used to arrive at that technique. Moreover, few, if any, of the techniques reported in the literature have looked at the electric power load from a site-independent viewpoint. A newly developed site-independent technique for STLF is analyzed in this paper. The load is modeled using a parameterized rule base and a parameter database. The extraction and representation of knowledge about the load are discussed. A site-independent expert system-based technique is tested using data from different sites around the United States including Alabama, Georgia, Florida, Massachusetts, Texas, Virginia and Washington. The mean absolute percentage errors for 24-h ahead load forecast range from 1.23 to 3.35% over all hours in all seasons. The expert system itself was implemented in UNIX/C, and the solution time per 1 day of forecast is around 4 s.

Discrimination and allocation using a mixture of discrete and continuous variables with some empty states

The likelihood ratio classification rule based on the location model is estimated given: (1) data consist of both binary and continuous variables; (2) some states have either zero frequency or too few observations, the case that usually happens in practice. An iterative proportional fitting of a convenient approximation of the log-linear models as well as a linear additive model are utilized in estimating the rule's parameters. Performance of the obtained rule is then assessed by estimated error rates.