Synergy of Effectuation and Causation: An Emotional Complexity Perspective

This study enriches the literature on entrepreneurial decisions by investigating the antecedents of the synergetic use of causal and effectual logic. Based on entrepreneurial metacognition and emotional complexity theories, we argued that the emotional complexity of an entrepreneur, referred to as the granular experience of, or variety in, experienced emotions during the entrepreneurial task, would contribute to the synergetic use of decision logic. With survey data gathered from 218 Chinese entrepreneurs, we found that entrepreneurs with higher emotional complexity are more likely to adopt two types of entrepreneurial logic in tandem, and cognitive flexibility mediates this positive relationship. Thereby, this study helps to unravel some of the complexities behind the choice of decision logic of entrepreneurs.

An Active Fault-Tolerant Control Strategy for Current Sensors Failure for Induction Motor Drives Using a Single Observer for Currents Estimation and Axes Transformation

This paper proposes a fault-tolerant control technique against current sensors failure in direct torque controlled induction motors drives, based on a new modification of Luenberger observer for currents estimation and axes transformation for vector rotation. Several important aspects are covered in the proposed algorithm, such as the detection of sensors failure, the isolation of faulty sensors, and the reconfiguration of the control system by a correct estimation. A logic circuit ensures fault detection by analyzing the residual signal between the measured and estimated quantities, while a single observer performs the task of estimating the line currents. In addition, a decision logic circuit isolates the erroneous signal and simultaneously selects the appropriate estimated current signal. An axes transformation ensures rotation from (a,b) to (α,β), which keeps a low-cost control using only two current sensors. The proposed scheme is tested on MATLAB/Simulink environment and experimentally validated in a laboratory prototype mainly containing a dS1104 card and 4 kW induction motor.

Fault and calibration shift detection in a robust MEMS accelerometer array

The latest generation micro-electro-mechanical system(MEMS) accelerometers offer high bandwidth and low noisefloors previously limited to piezoelectric (PZT) based sensors.These relatively low cost MEMS sensors drastically expandthe financially practical applications for high frequency,vibration based, prognostics health management (PHM).This paper examines a robust array of MEMS accelerometersfor applications where sensor access after deploymentis difficult or infeasible. Three identical single axis MEMSaccelerometers were place in an array for testing. Insteadof a typical tri-axial configuration, the three sensors werealigned on a common axis. An auto-correlation algorithmwas used to detect gross system faults of individual sensorsin the array. A separate algorithm was developed to detectabnormal sensor sensitivity drift. The 3 sensor array wastested under a variety of conditions to test the developedalgorithms; power supply voltages were systematically variedaffecting the ratio-metric accelerometer sensitivity andindividual sensor mounts were purposely compromised tosimulate common fault symptoms. A decision logic treewas then implemented to respond to both types of faults.Results show the feasibility of implementing robust MEMSaccelerometer arrays using the latest generation of high bandwidthMEMS accelerometers. Planned future work includesdeploying the sensor array on tribology test equipment tovalidate MEMS sensor effectiveness compared to traditionalPZT based accelerometers.

Tackling the DM Challenges with cDMN: A Tight Integration of DMN and Constraint Reasoning

Knowledge-based AI typically depends on a knowledge engineer to construct a formal model of domain knowledge – but what if domain experts could do this themselves? This paper describes an extension to the Decision Model and Notation (DMN) standard, called Constraint Decision Model and Notation (cDMN). DMN is a user-friendly, table-based notation for decision logic, which allows domain experts to model simple decision procedures without the help of IT staff. cDMN aims to enlarge the expressiveness of DMN in order to model more complex domain knowledge, while retaining DMNs goal of being understandable by domain experts. We test cDMN by solving the most complex challenges posted on the DM Community website. We compare our own cDMN solutions to the solutions that have been submitted to the website and find that our approach is competitive. Moreover, cDMN is able to solve more challenges than any other approach.

Application of decision logic trees and game-tree structures in analysis of automatic transmission gearboxes

Introduction (problem statement and relevance). The graphs, logic and game-tree structures methods have been used in mechanics. The purpose of modeling an automatic gearbox with graphs can be versatile, namely: determining the transmission ratio of individual gears, analyzing the speed and acceleration of individual rotating elements.The purpose of the study. The article presents the application of decision trees in the analysis of automatic gearboxes modeled with the Hsu graph.Methodology and research methods. The paper presents a method of generating game tree structures that allow to change the values of decision parameters in the issues of decision making and knowledge generation. Specifying the rank of importance, in which order you should change individual items to active, allows you to detect the so-called redundant or temporarily redundant components for a given gear currently under consideration.Scientific novelty and results. At each stage of optimization, a tree is generated, selecting the optimal decisions. Then, vertices can be added to the tree that represent the optimal responses of the system to changes in arithmetic construction parameters.Practical significance. The most important in this regard will be the selection of the optimal programming environment with the possibility of installing the program in laboratory

QRS Detection Based on Medical Knowledge and Cascades of Moving Average Filters

Heartbeat detection is the first step in automatic analysis of the electrocardiogram (ECG). For mobile and wearable devices, the detection process should be both accurate and computationally efficient. In this paper, we present a QRS detection algorithm based on moving average filters, which affords a simple yet robust signal processing technique. The decision logic considers the rhythmic and morphological features of the QRS complex. QRS enhancing is performed with channel-specific moving average cascades selected from a pool of derivative systems we designed. We measured the effectiveness of our algorithm on well-known benchmark databases, reporting F1 scores, sensitivity on abnormal beats and processing time. We also evaluated the performances of other available detectors for a direct comparison with the same criteria. The algorithm we propose achieved satisfying performances on par with or higher than the other QRS detectors. Despite the performances we report are not the highest that have been published so far, our approach to QRS detection enhances computational efficiency while maintaining high accuracy.

Modeling of quantum-like cognitive phenomena by the Fourier-holography technique under the choice of alternatives

The article is dedicated to the search for a biologically motivated mechanism of the cognitive phenomenon of violation of the classical formula of total probability for the disjunction of incompatible events, which is considered by a number of researchers as a quantum-like phenomenon. A classical mechanism implemented by the 6f Fourier holography scheme of the resonant architecture that does not require reference to quantum mechanics either in its physical nature or at the level of formalism is demonstrated. In the analysis, the decision-making is interpreted as a choice of alternatives by using the non-cooperative game "Prisoner's Dilemma". The approach to the task is based on the search for a mechanism for forming a conditional estimate under a condition that contradicts the rule of monotonous decision logic. It is demonstrated that this estimate, in contrast to the unconditional and conditional one with a non-contradictory condition, is formed by logic with exception. The ring architecture of the holographic setup corresponds to the biologically inspired neural network concept of the excitation ring and implements cognitive dissonance on logic with exception. Conditions and ranges of violation of the classical formula of total probability in relation to the correlation radius of the reference image recorded in a hologram storing the monotone logic inference rule are analytically determined. The analytical model is confirmed by a quantitative coincidence of the results of numerical modeling with the published results of natural experiments.

A Data-Driven Scheme for Fault Detection of Discrete-Time Switched Systems

This paper is concerned with the fault detection issue for a class of discrete-time switched systems via the data-driven approach. For the fault detection of switched systems, it is inevitable to consider the mode matching problem between the activated subsystem and the executed residual generator since the mode mismatching may cause a false fault alarm in all probability. Frequently, studies assume that the switching laws are available to the residual generator, by which the residual generator keeps the same mode as the system plant and then the mode mismatching is excluded. However, this assumption is conservative and impractical because many switching laws are hard to acquire in practical applications. This work focuses on the case of switched systems with unavailable switching laws. In view of the unavailability of switching information, the mode recognition is considered for the fault detection process and meanwhile, sufficient conditions are presented for the mode distinguishability. Moreover, a novel decision logic for the fault detection is proposed, based on which new algorithms are established for the data-driven realization. Finally, a benchmark case on a three-tank system is used to illustrate the feasibility and usefulness of the obtained results.

Performance Evaluation of Machine Learning Frameworks for Aphasia Assessment

Speech assessment is an essential part of the rehabilitation procedure for patients with aphasia (PWA). It is a comprehensive and time-consuming process that aims to discriminate between healthy individuals and aphasic patients, determine the type of aphasia syndrome, and determine the patients’ impairment severity levels (these are referred to here as aphasia assessment tasks). Hence, the automation of aphasia assessment tasks is essential. In this study, the performance of three automatic speech assessment models based on the speech dataset-type was investigated. Three types of datasets were used: healthy subjects’ dataset, aphasic patients’ dataset, and a combination of healthy and aphasic datasets. Two machine learning (ML)-based frameworks, classical machine learning (CML) and deep neural network (DNN), were considered in the design of the proposed speech assessment models. In this paper, the DNN-based framework was based on a convolutional neural network (CNN). Direct or indirect transformation of these models to achieve the aphasia assessment tasks was investigated. Comparative performance results for each of the speech assessment models showed that quadrature-based high-resolution time-frequency images with a CNN framework outperformed all the CML frameworks over the three dataset-types. The CNN-based framework reported an accuracy of 99.23 ± 0.003% with the healthy individuals’ dataset and 67.78 ± 0.047% with the aphasic patients’ dataset. Moreover, direct or transformed relationships between the proposed speech assessment models and the aphasia assessment tasks are attainable, given a suitable dataset-type, a reasonably sized dataset, and appropriate decision logic in the ML framework.