DECOMPOSITION OF INTEGRATED HIGH-DENSITY IoT DATA FLOW

PurposeThe role of big data and data analytics in the audit engagement process is evident. Notwithstanding, understanding how big data influences cognitive processes and, consequently, on the auditors’ judgment decision-making process is limited. The purpose of this paper is to present a conceptual framework on the cognitive process that may influence auditors’ judgment decision-making in the big data environment. The proposed framework predicts the relationships among data visualization integration, data processing modes, task complexity and auditors’ judgment decision-making.Design/methodology/approachThe methodology to accomplish the conceptual framework is based on a thorough literature review that consists of theoretical discussions and comparative studies of other authors’ works and thinking. It also involves summarizing and interpreting previous contributions subjectively and narratively and extending the work in some fashion. Based on this approach, this paper formulates four propositions about data visualization integration, data processing modes, task complexity and auditors’ judgment decision-making. The proposed framework was built from cognitive theory addressing how auditors process data into useful information to make judgment decision-making.FindingsThe proposed framework expects that the cognitive process of data visualization integration and intuitive data processing mode will improve auditors’ judgment decision-making. This paper also contends that task complexity may influence the cognitive process of data visualization integration and processing modes because of the voluminous nature of data and the complexity of business processes. Hence, it is also expected that the relationships between data visualization integration and audit judgment decision-making and between processing mode and audit judgment decision-making will be moderated by task complexity.Research limitations/implicationsThere is a dearth of studies examining how big data and big data analytics affect auditors’ cognitive processes in making decisions. This paper will help researchers and auditors understand the behavioral consequences of data visualization integration and data processing mode in making judgment decision-making, given a certain level of task complexity.Originality/valueWith the advent of big data and the evolution of innovative audit procedures, the constructed framework can be used as a theoretical foundation for future empirical studies concerning auditors’ judgment decision-making. It highlights the potential of big data to transform the nature and practice of accounting and auditing.

Simulation and fabrication of two Cu TSV electroplating methods for wafer-level 3D integrated circuits packaging

Partial directed coherence (PDC) and directed coherence (DC) which describe complementary aspects of the directed information flow between pairs of univariate components that belong to a vector of simultaneously observed time series have recently been generalized as bPDC/bDC, respectively, to portray the relationship between subsets of component vectors (Takahashi, 2009; Faes and Nollo, 2013). This generalization is specially important for neuroscience applications as one often wishes to address the link between the set of time series from an observed ROI (region of interest) with respect to series from some other physiologically relevant ROI. bPDC/bDC are limited, however, in that several time series within a given subset may be irrelevant or may even interact opposingly with respect to one another leading to interpretation difficulties. To address this, we propose an alternative measure, termed cPDC/cDC, employing canonical decomposition to reveal the main frequency domain modes of interaction between the vector subsets. We also show bPDC/bDC and cPDC/cDC are related and possess mutual information rate interpretations. Numerical examples and a real data set illustrate the concepts. The present contribution provides what is seemingly the first canonical decomposition of information flow in the frequency domain.

[Abstract] An investigation was performed to characterize the mechanical response of extendable, flexible polymer microwires for large area network applications. The increasing need to cover large areas with a high density of sensors and electronic devices for structural health monitoring requires the realization of sensor networks mounted onto flexible polymers which can be expanded to areas 10 4 % times higher than the original areas. This is achieved by building thousands of micronodes interconnected by extendable microwires at the microscale. The mechanical response of the extendable microwires is the key element to building networks that are robust enough to withstand fabrication, elongation and integration into materials or structures. A numerical and experimental study is presented here to describe the elongation process of a single microwire. A 3-D finite element model was used to optimize the microwire design, its extendibility and reliability by minimizing local stress/strain concentrations, avoiding in-plane and out-of plane plastic strains and controlling the maximum extension of the microwires.

Decomposition is an important phase in the design of medium and large-scale systems. Various architectures of software systems and decomposition methods are studied in numerous publications. Presently, formal specifications of software systems are mainly used for experimental purposes; for this reason, their size and complexity are relatively low. As a result, in the development of a nontrivial specification, different approaches to the decomposition should be compared and the most suitable approach should be chosen. In this paper, the experience gained in the deductive verification of the formal specification of the mandatory entity-role model of access and information flows control in Linux (MROSL DP-model) using the formal Event-B method and stepwise refinement technique is analyzed. Two approaches to the refinementbased decomposition of specifications are compared and the sources and features of the complexity of the architecture of the model are investigated.

The ability to classify the gender of occupants in a building has far-reaching applications including security and retail sales. The authors demonstrate the success of machine learning techniques for gender classification. High-sensitivity accelerometers mounted noninvasively beneath an actual building floor provide the input for these machine learning methods. While other approaches using gait measurements, such as vision systems and wearable sensors, provide the potential for gender classification, they each face limitations. These limitations include an invasion of privacy, occupant compliance, required line of sight, and/or high sensor density. Underfloor mounted accelerometers overcome these limitations. The authors utilize the highly-instrumented Goodwin Hall smart building on the Virginia Tech campus to measure vibrations of the walking surface caused by walkers. In this paper, the gait of 15 individual walkers was recorded as they, alone, walked down the instrumented hallway. Fourteen accelerometers, mounted underneath the walking surface, recorded walking trials with the placement of the sensors unknown to the walker. This paper studies bagged decision trees, boosted decision trees, support vector machines, and neural networks as the machine learning techniques for their ability to classify gender. A tenfold-cross-validation method is used to comment on the validity of the algorithm's ability to generalize to new walkers. This paper demonstrates that a gender classification accuracy of 88% is achievable using the underfloor vibration data from the Virginia Tech Goodwin Hall by using decision tree approaches.

This chapter gives our measures of unconditional (or traditional) and conditional connectivity and fault-tolerance of two-dimensional k-covered wireless sensor networks. The latter measures are more realistic than the former as they impose a restriction on the subsets of sensors that can fail at the same time. Precisely, conditional measures take into consideration the inherent properties of k-covered wireless sensor networks, such as high sensor density and sensor heterogeneity. In particular, the neighbour set of a sensor is defined as a forbidden faulty set, and hence cannot fail at the same time. This concept defines the new measure of connectivity, called conditional connectivity, which seems to be more realistic.

A comparison of Adomian's decomposition method and wavelet-Galerkin method for solving integro-differential equations

A new very high-order finite-difference method for linear stability analysis and bi-orthogonal decomposition of hypersonic boundary layer flow

Decomposition of the Estimation and Control Problems in Power Systems

A comparison was made of different digestion methods for the total decomposition of siliceous and organically environmental samples prior to their analysis by inductively coupled plasma optical emission spectrometry (ICP-OES). In the present study, three different digestion methods, including microwave-assisted, hot plate heating and pressurized digestion (pressure bomb), were employed for the determination of nine heavy metals, i.e. Ag, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn in sediment, soil, sludge and oil. The investigation of different combinations of acids through their analytical performance demonstrated that HCl plays a vital role in the determination of silver. The combination of HNO3 and HCl possesses more reactive ability in oxidizing organic matter. The recoveries of all elements of interest in sediment (NIST 2704) obtained by different digestion methods were found to be 86% to 113%, while microwave assisted digestion with various combinations of HNO3-HCl-HF and HNO3-HClO4-HF was considered to be a viable alternative to the conventional digestion systems because of its more intensive reaction conditions. The analytical results of four certified reference materials with different matrices, including sediment (GBW 07305), soil (GBW 07411), sludge (BCR R-143) and oil (NIST 1085a), by the microwave-assisted acid digestion method indicated that the recoveries of all elements of interest were more than 85% and the throughput of applied analytical method could be elevated significantly.

Traffic congestion will not only cause major economic losses to the city, but also cause environmental pollution. Efficient traffic congestion scheduling is one of the key technologies for intelligent transportation. This work presents an efficient traffic congestion scheduling scheme (ETCS), which combined with edge computing. In this scheme, firstly, we designed a ITS architecture combining edge computing, which migrate computing and storage tasks from the cloud to the edge of the network, so as to meet the characteristics of low latency in computing and transmission; Secondly, we propose a method to actively detect and distribute traffic accidents, which improves the response efficiency of congestion processing; Finally, we propose a re-routing algorithm based on probabilistic selection function, which combines the low latency and real-time decision of the edge computing to quickly calculate new alternative routes for vehicles in the congestion area so as to optimize the overall road network condition. Simulation results show the effectiveness of ETCS. When compared to original vehicular mobility trace, the system reduced the average travel time in approximately 39% and the overall CO_2 emissions in 18%.

Market mechanism allows the involvement of bilateral contracts in the electricity business competition. However, the participation of bilateral contracts will face various problems, especially the fair allocation of transmission losses. This paper presents the reasonableness share allocation of transmission line losses based on the separation method of current flow in each transmission line from the converging power flow. The methodology is to combine superposition and decomposition techniques in order to produce accurate transmission loss allocation for bilateral parties. The superposition technique formulates the current flow separation in each transmission line, and the decomposition technique defines the allocation of power losses in each transmission line. This technique is called the Combination of the Superposition and Decomposition Method (CSDM). A numerical test has been performed for method validation and compared with the τ-value method. This method has been able to provide a solution for the allocation of reasonable transmission losses. Based on the CSDM, the portion of transmission loss allocation for bilateral contracts compared with the integrated system is 47.9% and 52.1%, respectively. At the same time, the τ-value method provided the proportion of the bilateral contracts compared with the respective integrated system is 80.5% and 19.5%.

This paper presents a proper orthogonal decomposition (POD) method that uses dynamic basis functions. The dynamic functions are of a prescribed form and do not explicitly depend on time but rather on parameters associated with flow unsteadiness. This POD method has been developed for modeling nonlinear flows with deforming meshes but can also be applied to fixed meshes. The method is illustrated for subsonic and transonic flows in channels with fixed and deforming meshes. This method properly captured flow nonlinearities and shock motion for cases in which the classical POD method failed.

The cavitation of the tip leakage vortex (TLV) induced by tip leakage has always been a difficult problem faced by turbomachinery, and its flow structure is complex and diverse. How to accurately extract the main structures that affect the cavitating flow of the TLV from the two-phase flow field is a key problem. In this study, the main mode extraction and low order mode reconstruction accuracy of the cavitation flow field of TLV downstream of National Advisory Committee for Aeronautics (NACA)0009 hydrofoil by two dynamic mode decomposition (DMD) methods are compared. The research shows that the main modes extracted by the standard DMD method contain a large number of noise modes, while the sparsity-promoting DMD eliminates the noise modes, showing obvious advantages in the reconstruction accuracy of the velocity field. The characteristics of cavitation signals are analyzed, and the cavitation signals are divided into four categories, which explains the reason why DMD methods have low reconstruction accuracy in cavitation. This study provides a theoretical basis and strong guarantee for the extraction of mode decomposition characteristics of the two-phase flow field. This is of great significance for accelerating the prediction of multiphase flow fields based on intelligent flow pattern learning in the future. Meanwhile, it also provides a new method and road for the introduction of artificial intelligence technology in future scientific research.