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This article investigates the impacts of uncertainties in duration and resource availability and usage on project time and cost. This is done by modeling different scenarios as relevant resource-constrained project scheduling problems. The aim is to develop a scheduling risk assessment framework (SRAF) to model uncertainties in duration and resources and to observe their impact on project objectives (i.e., completion time and cost). This framework provides appropriate information for managers and decision makers and facilitates project planning (scheduling and budgeting) by incorporating uncertainties and risks. To visualize the risk, this article uses an extended version of the integrated definition standard as the example business process. To demonstrate the applicability of the proposed SRAF under varied dynamic conditions, we have considered and solved a practical case study using a self-developed heuristic approach, titled enhanced move-based local search heuristic. The computational results and useful insights open an avenue for future research in the field of project management, which will provide some important propositions or guidelines for project management practitioners. [ABSTRACT FROM AUTHOR]

The selection of an optimal maintenance strategy is one of the principal strategic decisions that must be taken in many contexts in order to maintain an asset with minimum deterioration and to deliver maximum output with high quality. When considering maintenance cost, reliability, and safety level of industrial assets, decision makers must select an appropriate maintenance strategy, preferably, with a known degree of uncertainty. This article utilizes a new interval type-2 fuzzy (IT2F) multicriteria decision-making method based on the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS), which can effectively handle the degree of uncertainty in group decision making and assist in the selection of the most feasible and optimal industrial asset maintenance strategy. The proposed method is compared with the results obtained from the conventional AHP-TOPSIS and type-1 fuzzy AHP-TOPSIS—the rank obtained from IT2F-AHP-TOPSIS is identical to those methods and can be used as an effective alternative to the type-1 fuzzy method. Compared with conventional AHP-TOPSIS and type-1 fuzzy AHP, IT2F-AHP-TOPSIS has the added advantage that it allows decision makers to define the membership function with greater flexibility and is able to handle uncertainty during decision making. [ABSTRACT FROM AUTHOR]

Real-time forecasting of the financial time-series data is challenging for many machine learning (ML) algorithms. First, many ML models operate offline, where they need a batch of data, which may not be available during training. Besides, due to a fixed architecture of the majority of the offline-based ML models, they suffer to deal with the uncertain nature of financial time-series data. In contrast, online learning mode evolving-structured ML models could be promising for financial time-series forecasting. For real-time deployment of such models, low memory demand is a must. Besides, the model’s explainability plays a crucial role in forecasting financial time-series. Considering all the requirements, a rule-based autonomous neuro-fuzzy learning algorithm called the parsimonious learning machine (PALM) is proposed here to forecast time-varying stock indices. To provide efficient automation of the proposed algorithm by maintaining the model explainability in terms of limited number linguistic IF-THEN rules, two popular multiobjective evolutionary algorithms (MEAs), such as a real-coded genetic algorithm (GA) and a self-adaptive differential evolution (DE) algorithm are utilized here. In addition, fuzzy type-2 variants of PALMs’ are considered here due to better uncertainty handling capacity than their type-1 counterparts. To evaluate the proposed algorithm’s performance, the closing stock price of fifteen (15) different stock market indices are predicted here. From the results, it is observed that the MEA-based PALMs are performing better than the state-of-the-art benchmark online ML models and providing a rule-based explainable model to the end-user. [ABSTRACT FROM AUTHOR]

Conventional PI controllers are vulnerable to changes in parameters and are difficult to tune. In this work, an artificial neural network (ANN) based controller is developed for the robust operation of a single-phase modified packed U-cell five-level inverter (MPUC-5) for solar PV application under variable insolation conditions. An MPUC-5 is a converter with a main and an auxiliary dc link of equal magnitude; although five-level operation is also still feasible with different voltages also. The maximum power point (MPP) of a PV array changes with the variation in the solar insolation. This results in a variable voltage at the output of the boost converter while maintaining the load line at the MPP. Consequently, the fundamental value of the output of the MPUC-5 also tends to change. Thus, it is required to produce angles that commit to an ac output voltage with a constant fundamental value and constrained to a minimum total harmonic distortion along with a third-order harmonic mitigation as per the grid codes, irrespective of the change in the dc-link voltages. A genetic algorithm is employed for this purpose. A large dataset is prepared for two-angle and four-angle operation of MPUC-5 under various dc-link voltages and constraints with which an ANN-based controller is trained. A neural network with a hidden layer is trained with the backpropagation technique; and once a correlation is developed, the network can be operated for a wide range of operating conditions. The robustness of the controller is verified through simulation in MATLAB/Simulink environment and validated by experimental emulation in an hardware in loop environment. [ABSTRACT FROM AUTHOR]

Despite elegance being universally recognized as a goal for system design, there is little guidance in the related research and literature that is relevant to the design, development, or evaluation of elegant systems. This paper begins by reviewing the rather loose usage of elegance as a property of a system and shows that there are both abstract and practical views. Further, from analysis of the practical views of elegance in systems, it is clear that most current definitions of elegance are focused on the sufficiency of the system as a solution to the stated problem, rather than on elegance, perse. This paper disambiguates the usage of sufficiency and elegance, offers a formal definition of the two terms, and then provides several simple examples by way of illustration. Elegance and sufficiency must be considered as separate properties of a system, if only because the essential act of validation is an examination of the sufficiency of a solution, not its elegance. Any system attribute desired by the customer must be included as part of the solution's sufficiency and designed into the solution, not be a serendipitous unintended outcome of that design. Sufficiency is shown to be binary—a solution is either sufficient or it is not—and there may be more than one sufficient solution. Elegance is then defined to be the least complex sufficient solution. Furthermore, an elegant solution must be sufficient, but a sufficient system does not have to be (and is not always desirable to be) elegant. [ABSTRACT FROM AUTHOR]

Axiomatic design (AD) theory is a general framework for system design process based on mapping between several domains. In the AD theory, only the functional requirements (system functions) are considered in the design process, since the mapping between requirements and design parameters in AD does not include nonfunctional requirements (system attributes). This paper introduces an extended view of the system design process based on AD theory framework to incorporate nonfunctional requirements into the mapping between the problem domain and the solution domain. A systematic way of including nonfunctional requirements in the design process is presented, and an extended design matrix is introduced, which includes the mapping between a complete set of requirements (functional and nonfunctional) and corresponding design parameters (physical domain). We also present an approach to decide between solutions that satisfy nonfunctional requirements to a varying degree through the employment of the analytical hierarchy process. [ABSTRACT FROM PUBLISHER]

Underwater acoustic sensor networks (UASNs) can be employed in a vast range of applications, retrieving accurate and up-to-date information from underneath the ocean's surface. Although widely used by terrestrial sensor networks, radio frequencies (RFs) do not propagate well underwater. Therefore, acoustic channels are employed as an alternative to support long-distance and low-power communication in underwater sensor networks even though acoustic signals suffer from long propagation delay and have very limited bandwidth. In this paper, we introduce an adaptive propagation-delay-tolerant collision- avoidance protocol (APCAP) for the media access control (MAC) sublayer of UASN. The protocol includes an improved handshaking mechanism that improves efficiency and throughput in UASN where there is a large propagation delay. The mechanism guarantees nodes that can potentially interfere with a forthcoming transmission are properly informed. It also allows a node to utilize its idle time while waiting for messages to propagate, which is otherwise wasted by most existing MAC protocols. The simulation results indicate that where employed by UASN, APCAP exhibits good performance and outperforms the other MAC protocols examined in this paper. [ABSTRACT FROM AUTHOR]

Presents a study which developed four-leg inverters to power unbalanced/nonlinear three-phase loads. Circuit topology, modeling and transforms; Synchronous-frame controller; Conclusions.

Discusses a jointly optimized spatial mean-normalized vector quantization (M-NVQ)/spectral discrete cosine transform (DCT) technique for hyperspectral image data. Lossy compression by M-NVQ; DCT of vector quantization residuals; Optimized spatial-spectral compression; Classification performance on compressed data.

Presents a probabilistic method for the determination of a power flux density (PFD) trigger threshold, along with a recommended PFD trigger level for the coordination of the Broadcast Satellite Service--Sound, BSS(S) with terrestrial point-to-multipoint Digital Radio Concentrator System (DRCS). Modified antenna model; Interference analysis.

Analyzes fundamental sine-wave inverter topologies, two-leg (one-phase, two-wire), three-leg (three-phase, three-wire) and four-leg (three-phase, four-wire), all of which are full-bridged voltage-source inverters with LC filters suitable for producing sinusoidal output voltages. Switching states and corresponding output voltage vectors produced by the three inverters.

Focuses on the disclosing of spectral responses that would seem ideal candidates for compression by vector quantization by the structure of hyperspectral images. Outlining of results of an investigation of lossless vector quantization of 224-band Airborne/Visible Infrared Imaging Spectrometer (AVIRIS) images; Identification of various vector formation techniques; How images are compressed.

Presents a study on a LCL resonant direct current-direct current (dc-dc) converter topology. LCL-resonant push-pull converter topology; Converter simulations; Experimental results.

Presents several state-space control topology options for a single-phase Uninterruptible Power System (UPS) inverters. Sinewave inverter components and state-space model; Filter inductor current regulator; Filter capacitor current regulator; Disturbance rejection comparison via dynamic stiffness; Experimental results for the capacitor current feedback controller topology; Conclusions.