Depth reconstruction uncertainty analysis and improvement – The dithering approach

The depth spatial quantization uncertainty is one of the factors which influence the depth reconstruction accuracy caused by a discrete sensor. This paper discusses the quantization uncertainty distribution, introduces a mathematical model of the uncertainty interval range, and analyzes the movements of the sensors in an Intelligent Vision Agent System. Such a system makes use of multiple sensors which control the deployment and autonomous servo of the system. This paper proposes a dithering algorithm which reduces the depth reconstruction uncertainty. The algorithm assures high accuracy from a few images taken by low-resolution sensors. The dither signal is estimated and then generated through an analysis of the iso-disparity planes. The signal allows for control of the camera movement. The proposed approach is validated and compared with a direct triangulation method. The simulation results are reported in terms of depth reconstruction error statistics. The physical experiment shows that the dithering method reduces the depth reconstruction error.     

Revealing perceptual–cognitive expertise in law enforcement: an iterative approach using verbal-report,temporal-occlusion,and option-generation methods

Despite the attention garnered in the media about police use of force, there have been relatively few investigations of perceptual–cognitive skill in law enforcement using the naturalistic-decision-making approach. In this paper, we provide an overview of a series of four studies in which we investigated experience-based differences in police officer decision making in complex, rapidly unfolding, and uncertain situations. In these naturalistic situations, decision makers must first generate—for themselves—at least one option before intervening or taking action. We sought to test hypotheses about option-generation processes derived from apparently competing theories of skilled decision making and expert sensemaking. These two theories can be considered as representing two phases of decision making: skilled decision making focuses on selecting an appropriate course of action, while expert sensemaking addresses situational assessment and diagnosis. In the studies, we employed a variety of cognitive task analysis techniques, including experiments using option-generation and temporal-occlusion methods and process tracing measures (e.g., retrospective verbal reports, video-stimulated recall). Based on the data, we conclude that the two theoretical approaches—skilled decision making and expert sensemaking—appear to be complementary rather than competing. When the situation is relatively familiar, officers can quickly recognize the situation and identify an appropriate response. However, when situations are less familiar, more complex, and/or more uncertain, officers may need to engage in rapid sensemaking or situational diagnosis so that they can quickly comprehend the situation. We discuss implications for law enforcement officers and for law enforcement training.     

Interpolation and extrapolation of fuzzy quantities revisited – an axiomatic approach

 This paper deals with the problem of rule interpolation and rule extrapolation for fuzzy and possibilistic systems. Such systems are used for representing and processing vague linguistic If-Then-rules, and they have been increasingly applied in the field of control engineering, pattern recognition and expert systems. The methodology of rule interpolation is required for deducing plausible conclusions from sparse (incomplete) rule bases. For this purpose the well-known fuzzy inference mechanisms have to be extended or replaced by more general ones. The methods proposed so far in the literature for rule interpolation are mainly conceived for the application to fuzzy control and miss certain logical characteristics of an inference. First, a set of axioms is proposed in this paper. With this, a definition is given for the notion of interpolation, extrapolation, linear interpolation and linear extrapolation of fuzzy rules. The axioms include all the conditions that have been of interest in the previous attempts and others which either have logical characteristics or try to capture the linearity of the interpolation. A new method for linear interpolation and extrapolation of compact fuzzy quantities of the real line is suggested and analyzed in the spirit of the given definition. The method is extended to non-linear interpolation and extrapolation as well.     

The generic model query language GMQL – Conceptual specification,implementation, and runtime evaluation

The generic model query language GMQL is designed to query collections of conceptual models created in arbitrary graph-based modelling languages. Querying conceptual models means searching for particular model subgraphs that comply with a predefined pattern query. Such a query specifies the structural and semantic properties of the model fragment to be returned. In this paper, we derive requirements for a generic model query language from the literature and formally specify the language’s syntax and semantics. We conduct an analysis of GMQL׳s theoretical and practical runtime performance concluding that it returns query results within satisfactory time. Given its generic nature, GMQL contributes to a broad range of different model analysis scenarios ranging from business process compliance management to model translation and business process weakness detection. As GMQL returns results with acceptable runtime performance, it can be used to query large collections of hundreds or thousands of conceptual models containing not only process models, but also data models or organizational charts. In this paper, we furthermore evaluate GMQL against the backdrop of existing query approaches thereby carving out its advantages and limitations as well as pointing toward future research.     

Modelling stress–strain and volume change behaviour of unsaturated soils using an evolutionary based data mining technique,an incremental approach

Modelling of unsaturated soils has been the subject of many research works in the past few decades. A number of constitutive models have been developed to describe the complex behaviour of unsaturated soils. However, many have proven to be unable to predict all aspects of the behaviour of unsaturated soils in a unified manner. In this paper an alternative new approach is presented, based on the Evolutionary Polynomial Regression (EPR) technique. EPR is a data mining technique that generates a transparent and structured representation of the behaviour of a system directly from input test data. The capabilities of the proposed EPR-based framework in modelling of behaviour of unsaturated soils are illustrated using results from a comprehensive set of triaxial tests on samples of compacted unsaturated soils from literature. The main parameters used for modelling of the behaviour of unsaturated soils during shearing are initial water content, initial dry density, mean net stress, axial strain, suction, volumetric strain, and deviator stress. The model developed is used to predict different aspects of the behaviour of unsaturated soils for conditions not used in the model building process. The results show that the proposed approach provides a useful framework for modelling of unsaturated soils. The merits and advantages of the proposed approach are highlighted.     

Controlled violation of temporal process constraints – Models,algorithms and results

While there has been much work on modeling and analysis of temporal constraints in workflows in the context of many real-world applications, there has not been much work on managing violations of temporal constraints. In real-time workflows, such as in medical processes and emergency situations, and also in logistics, finance and in other business processes with deadlines some violations are unavoidable. Here we introduce the notion of controlled violations as the ability to monitor a running process and develop an approach based on constraint satisfaction to determine the best schedule for its completion in a way so as to minimize the total penalty from the violations. The violations are evaluated in terms of metrics like number of violations, delay in process completion, and penalty of weighted violations. We also relate our work to the concept of controllability in literature and show how it can be checked using our method. Finally, we analyze the properties of our approach and also offer a proposal for implementation.     

An approach to portfolio selection using an ARX predictor for securities’ risk and return

It is well known that every investment carries a risk associated, and depending on the type of investment, it can be very risky; for instance, securities. However, Markowitz proposed a methodology to minimize the risk of a portfolio through securities diversification. The selection of the securities is a choice of the investor, who counts with several technical analyzes to estimate investment’s returns and risks. This paper presents an autoregressive exogenous (ARX) predictor model to provide the risk and return of some Brazilian securities – negotiated at the Brazilian stock market, BOVESPA – to select the best portfolio, herein understood as the one with minimum expected risk. The ARX predictor succeeded in predicting expected returns and risks of the securities, which resulted in an effective portfolio. Additionally the Markowitz theory was confirmed, showing that diversification reduces the risk of a portfolio.     

Assessment and improvement of the Cloud Emission and Scattering Index (CESI) – an algorithm for cirrus detection

This study evaluates and improves the cirrus detection of the middle and low peak Weighting Function (WF) Cloud Emission and Scattering Index (CESI) by using the Visible Infrared Imaging Radiometer Suite (VIIRS) cloud mask product. Results show that CESI-3 (peak WF about 890 hPa) accurately detect cirrus with the Probability of detection (POD) value reaching 0.69 during both at day and nighttimes. The CESI-1 (peak WF about 550 hPa) falsely detects cirrus in the Sahara Desert, south of the Saudi Arabia, north of Tibetan Plateau and in Australia during the ascending process. More false alarm rates (FARs) are detected in the northern mid-latitude around 60° N because of the surface type and seasonal effects. Furthermore, the thresholds of middle and low peak WF CESIs for cirrus detection are determined and the performance is satisfactory. In addition, Linear Discriminant Analysis (LDA) is employed to train four combination types (combining CESI-1 and CESI-2, combining CESI-1 and CESI-3, combining CESI-2 and CESI-3, and combining CESI-1, CESI-2, and CESI-3). Accordingly, results elucidate that Comb-1-3 (combining CESI-1 and CESI-3) is appropriate to detect cirrus for the ascending process, increasing the POD by 11.2% as compared to CESI-3. However, Comb-1-2-3 (combining CESI-1, CESI-2 with peak WF around 790 hPa, and CESI-3) is most suitable for cirrus detection in the descending process, increasing the PODs by 8.5%. We also find that the POD of cirrus detection over the land increases relatively higher than over the sea. The most significant improvement of cirrus detection occurs over the land in the summer in the daytime with a 35% POD increase from 0.57 to 0.76.     

fine-GRAPE: fine-grained APi usage extractor – an approach and dataset to investigate API usage

An Application Programming Interface (API) provides a set of functionalities to a developer with the aim of enabling reuse. APIs have been investigated from different angles such as popularity usage and evolution to get a better understanding of their various characteristics. For such studies, software repositories are mined for API usage examples. However, many of the mining algorithms used for such purposes do not take type information into account. Thus making the results unreliable. In this paper, we aim to rectify this by introducing fine-GRAPE, an approach that produces fine-grained API usage information by taking advantage of type information while mining API method invocations and annotation. By means of fine-GRAPE, we investigate API usages from Java projects hosted on GitHub. We select five of the most popular APIs across GitHub Java projects and collect historical API usage information by mining both the release history of these APIs and the code history of every project that uses them. We perform two case studies on the resulting dataset. The first measures the lag time of each client. The second investigates the percentage of used API features. In the first case we find that for APIs that release more frequently clients are far less likely to upgrade to a more recent version of the API as opposed to clients of APIs that release infrequently. The second case study shows us that for most APIs there is a small number of features that is actually used and most of these features relate to those that have been introduced early in the APIs lifecycle.     

The autonomous industrial plant – future of process engineering,operations and maintenance

Almost every day we read about new advances in self-driving cars and artificial intelligence. For autonomous driving, there already exist established standards with six levels describing the steps from driving without assistance to fully automated, i.e., autonomous driving. While many people and industries – in particular the automotive industry – believe that self-driving cars are feasible in the near future, one may ask how feasible full autonomy is in the process industry? Is it realistic to aim towards a fully autonomous chemical plant, and what does full autonomy even mean for the process industries? This paper introduces a definition of autonomy and corresponding levels of autonomy for the process industry. It starts from an abstract definition of six autonomy levels that are applicable to various industrial domains, also illustrated by recent examples from different industrial applications such as mining and marine. It also discusses how to achieve an autonomous system in general, based on cognitive capabilities and AI technologies potentially implementing them. Then, it discusses the applicability of this definition for process industry in a first step. Finally, the taxonomy is further refined by studying some key autonomous features of two process lifecycle phases: operations and engineering, and examples for the meaning of each autonomy level per feature are given.     

Property specification,process verification,and reporting – A case study with vehicle-commissioning processes

Testing in the automotive industry is supposed to guarantee that vehicles are shipped without any flaw. Respective processes are complex, due to the variety of components and electronic devices in modern vehicles. To achieve error-free processes, their formal analysis is required. Specifying and maintaining properties the processes must satisfy in a user-friendly way is a core requirement on any verification system. We have observed that there are few property templates that testing processes must adhere to, and we describe these templates. They depend on the context of the processes, e.g., the components of the vehicle or testing stations. We have developed a framework that instantiates the templates of properties at verification time and then verifies the process against these instances. To allow an automatic verification we develop a transformation of the commissioning process to a Petri net. Using a novel approach, we are able to report the found violations to the user in a user-friendly way. Our empirical evaluation with the industrial partner has shown that our framework does detect property violations in processes. From expert interviews we conclude that our framework is user-friendly and well suited to operate in a real production environment.     

The optimisation of neural network parameters using Taguchi’s design of experiments approach: an application in manufacturing process modelling

Neural networks have been widely used in manufacturing industry, but they suffer from a lack of structured method to determine the settings of NN design and training parameters, which are usually set by trial and error. This article presents an application of Taguchis Design of Experiments, to identify the optimum setting of NN parameters in a multilayer perceptron (MLP) network trained with the back propagation algorithm. A case study of a complex forming process is used to demonstrate implementation of the approach in manufacturing, and the issues arising from the case are discussed.     

Supporting rescheduling using CSP, RMS and POB—an example application

This paper introduces an innovative approach to the problem of rescheduling within manufacturing industry. An example of a manufacturing context that requires rescheduling capability is given (tyre production). The meaning of rescheduling, possible metrics for assessment of rescheduling and the advantages of applying the new techniques are reviewed. Of particular importance is the notion that the technology for providing rescheduling and explanation capabilities is to a large degree problem and context insensitive. The manner in which an original schedule has been created is irrelevant to the use of the technology described, allowing the advantages of the approach to be realized as an add-on facility to any existing scheduling system that fulfills a minimal set of requirements. These advantages are due to the use of a constraint based approach to new schedule creation used in tandem with dependency analysis techniques based on reason maintenance systems (de Kleer, 1986) and partial order backtracking (Ginsberg and McAllister, 1995; Spragg and Kelleher, 1996).     

Business process compliance checking – applying and evaluating a generic pattern matching approach for conceptual models in the financial sector

Given the strong increase in regulatory requirements for business processes the management of business process compliance becomes a more and more regarded field in IS research. Several methods have been developed to support compliance checking of conceptual models. However, their focus on distinct modeling languages and mostly linear (i.e., predecessor-successor related) compliance rules may hinder widespread adoption and application in practice. Furthermore, hardly any of them has been evaluated in a real-world setting. We address this issue by applying a generic pattern matching approach for conceptual models to business process compliance checking in the financial sector. It consists of a model query language, a search algorithm and a corresponding modelling tool prototype. It is (1) applicable for all graph-based conceptual modeling languages and (2) for different kinds of compliance rules. Furthermore, based on an applicability check, we (3) evaluate the approach in a financial industry project setting against its relevance for decision support of audit and compliance management tasks.     

Nonlinear Finite Element Analysis using an Object-Oriented Philosophy – Application to Beam Elements and to the Cosserat Continuum

An Object-Oriented Programming (OOP) frame-work is presented for solving nonlinear structural mechanics problems by means of the Finite Element Method (FEM). Emphasis is placed on engineering applications (geometrically nonlinear beam model, and elastoplastic Cosserat continuum), and OOP is employed as an effective tool, which plays an important role in the FEM treatment of such applications. The implementation is based on computational abstractions of both mathematical and physical concepts associated to structural mechanics problems involving geometrical and material nonlinearities. The overall class organization for nonlinear mechanics modeling is discussed in detail. All the analyses rely on a generic control class where several classical and modern nonlinear solution schemes are available. Examples which explore, demonstrate and validate the main features of the overall computational system are presented and discussed.     

Effects of mental process integrated nursing training using mobile device on students’ cognitive load,learning attitudes,acceptance, and achievements

Clinical nursing training is important to nursing educators and student nurses in nursing education since safe and competent care depends on good clinical problem solving skills. Therefore, developing better cognitive problem-solving strategies or tools are essential for clinical nursing practices. Moreover, learning diagnosis is also a critical determinant in the acquisition, processing, and application of clinical skills in nursing practices. Bearing this in mind, this study aims to develop a mobile interactive learning and diagnosis (MILD) system to support problem-based learning (PBL) in a clinical nursing course based on the testing-based approach. Using mobile devices as a learning tool to integrate both real-world and digital-world resources for students and adopting PBL as a learning strategy to facilitate the development of the clinical problem solving skills. To show the effectiveness of the proposed approach, an experiment was conducted in a foundations of nursing course at a nursing college in Taiwan. The experimental results show that the proposed approach is helpful to students in improving learning performance and reducing cognitive loads. Moreover, it was also found that most students showed positive perceptions toward the usage of the proposed system.