Strength Of Interconnections Research Articles

Training Artificial Neural Network with a Cultural Algorithm

Artificial neural networks are amongst the artificial intelligence techniques with their ability to provide machines with some functionalities such as decision making, comparison, and forecasting. They are known for having the capability of forecasting issues in real-world problems. Their acquired knowledge is stored in the interconnection strengths or weights of neurons through an optimization system known as learning. Several limitations have been identified with commonly used gradient-based optimization algorithms, including the risk of premature convergence, the sensitivity of initial parameters and positions, and the potential for getting trapped in local optima. Various meta-heuristics are proposed in the literature as alternative training algorithms to mitigate these limitations. Therefore, the primary aim of this study is to combine a feed-forward artificial neural network (ANN) with a cultural algorithm (CA) as a meta-heuristic, aiming to establish an efficient and dependable training system in comparison to existing methods. The proposed artificial neural network system (ANN-CA) evaluated its performance on classification tasks over nine benchmark datasets: Iris, Pima Indians Diabetes, Thyroid Disease, Breast Cancer Wisconsin, Credit Approval, Glass Identification, SPECT Heart, Wine and Balloon. The overall experimental results indicate that the proposed method outperforms other methods included in the comparative analysis by approximately 12% in terms of classification error and approximately 7% in terms of accuracy.

Multiple models for decentralised adaptive control of discrete‐time systems

AbstractThis paper investigates decentralised adaptive control of discrete‐time systems consisting of linear time‐invariant systems and a class of nonlinear systems. The parameters of the subsystems and the interconnection strengths between the subsystems are assumed unknown. Multiple adaptive prediction models with switching are used in this paper to address the relatively poor transient performance that typically results from a single prediction model. However, the application of this methodology requires indirect model reference discrete‐time adaptive control, and there appears to be very little focus in the literature on this. The principal contribution of this paper is to fill this void by arriving at proof of global stability of such decentralised adaptive systems using the theory of Lyapunov and properties of square‐summable sequences. The chosen representation of the system enables the same results to be directly applicable to both linear time‐invariant and linear‐in‐the‐parameters nonlinear subsystems. We consider two parametric update algorithms, one of which has a normalisation factor. Simulation studies included in this paper demonstrate the improvement in transient performance.

Process-Factor Optimization of Small-Area Sintered Interconnects for Power Electronics Applications

AbstractPower electronics is concerned with the use of electronic devices to control and transfer electric power from one form to another. Power electronics can be found in laptop chargers, electric grids, and solar inverters. Die-attach interconnections form a critical part of power electronics devices. Silver sintering has been traditionally used for die-attach interconnections because of its high melting point and ability to form very thin thicknesses. However, the processing time compared with soldering is very long. Sintered layers might contain large voids that affect the mechanical stability of the structure. Stresses caused by mechanical and environmental conditions might cause degradation and possibly early failures. This work focuses on studying the combined effect of process factors on the shear strength of small-area die-attach interconnections in silver sintering. Design of experiments (DoE) tools were used to build an experimental matrix with a 95% confidence level. The results have shown that holding time has a considerable effect on the mechanical stability of the die-attach interconnections. Intermetallic compounds formed in the sintered joints at higher holding times resulted in fewer voids. Furthermore, the treatment level of the holding time highly affects the shear strength under other factors such as temperature and pressure.

Promoting systems thinking and pro-environmental policy support through serious games

We evaluated whether teaching the public about the “critical zone”–the Earth’s outer skin, critical to all life—via a digital serious game can affect adults’ systems thinking about the environment and support policies to protect the environment. An experiment (N = 152) compared the effects of playing “CZ Investigator” versus viewing a static website on systems thinking about the Food-Energy-Water (FEW) nexus and support for relevant public policies. The serious game had the strongest effects on our outcomes of interest for those participants with less past science education. For these individuals, the serious game, relative to the static website, increased perceptions of the strength of interconnections across food, energy, and water systems (p < .01) and support for policies that regulated human impacts on the environment (p < .01). Mediation analysis revealed that increases in systems thinking explain increases in policy support. This group of users also indicated that the game was easier, more enjoyable, and more effective for learning than the website. Mediation analyses also revealed that perceived learning effectiveness was a stronger mediator than ease and enjoyment effects of the game on systems thinking and policy support. These results are valuable for environmental education because understanding interconnections within complex systems is vital for solving environmental problems, particularly for learners with less background in science.

Data-driven modeling predicts gene regulatory network dynamics during the differentiation of multipotential hematopoietic progenitors.

Cellular differentiation during hematopoiesis is guided by gene regulatory networks (GRNs) comprising transcription factors (TFs) and the effectors of cytokine signaling. Based largely on analyses conducted at steady state, these GRNs are thought to be organized as a hierarchy of bistable switches, with antagonism between Gata1 and PU.1 driving red- and white-blood cell differentiation. Here, we utilize transient gene expression patterns to infer the genetic architecture-the type and strength of regulatory interconnections-and dynamics of a twelve-gene GRN including key TFs and cytokine receptors. We trained gene circuits, dynamical models that learn genetic architecture, on high temporal-resolution gene-expression data from the differentiation of an inducible cell line into erythrocytes and neutrophils. The model is able to predict the consequences of gene knockout, knockdown, and overexpression experiments and the inferred interconnections are largely consistent with prior empirical evidence. The inferred genetic architecture is densely interconnected rather than hierarchical, featuring extensive cross-antagonism between genes from alternative lineages and positive feedback from cytokine receptors. The analysis of the dynamics of gene regulation in the model reveals that PU.1 is one of the last genes to be upregulated in neutrophil conditions and that the upregulation of PU.1 and other neutrophil genes is driven by Cebpa and Gfi1 instead. This model inference is confirmed in an independent single-cell RNA-Seq dataset from mouse bone marrow in which Cebpa and Gfi1 expression precedes the neutrophil-specific upregulation of PU.1 during differentiation. These results demonstrate that full PU.1 upregulation during neutrophil development involves regulatory influences extrinsic to the Gata1-PU.1 bistable switch. Furthermore, although there is extensive cross-antagonism between erythroid and neutrophil genes, it does not have a hierarchical structure. More generally, we show that the combination of high-resolution time series data and data-driven dynamical modeling can uncover the dynamics and causality of developmental events that might otherwise be obscured.

Neural Interactions in Developing Rhythmogenic Spinal Networks: Insights From Computational Modeling.

The mechanisms involved in generation of rhythmic locomotor activity in the mammalian spinal cord remain poorly understood. These mechanisms supposedly rely on both intrinsic properties of constituting neurons and interactions between them. A subset of Shox2 neurons was suggested to contribute to generation of spinal locomotor activity, but the possible cellular basis for rhythmic bursting in these neurons remains unknown. Ha and Dougherty (2018) recently revealed the presence of bidirectional electrical coupling between Shox2 neurons in neonatal spinal cords, which can be critically involved in neuronal synchronization and generation of populational bursting. Gap junctional connections found between functionally-related Shox2 interneurons decrease with age, possibly being replaced by increasing interactions through chemical synapses. Here, we developed a computational model of a heterogeneous population of neurons sparsely connected by electrical or/and chemical synapses and investigated the dependence of frequency of populational bursting on the type and strength of neuronal interconnections. The model proposes a mechanistic explanation that can account for the emergence of a synchronized rhythmic activity in the neuronal population and provides insights into the possible role of gap junctional coupling between Shox2 neurons in the spinal mechanisms for locomotor rhythm generation.

Effectiveness assessment of ship navigation safety countermeasures using fuzzy cognitive maps

The fuzzy cognitive map method is adopted to construct the systematic mapping relationship between ship navigational risk factors and safety level for assessing the effectiveness of ship navigation safety countermeasures. The proposed approach integrates fuzzy inference system and evidential reasoning to convert initial input values of variables and obtain the strength of interconnections, respectively. Non-linear Hebbian learning algorithm is employed to refine the model. The constructed model is then applied with an illustrative case study associated with the middle stream region of the Yangtze river, where the current safety situation and several scenarios for improving navigational safety in the future are evaluated. The logicality and robustness constructed model and the assessing results are validated by applying sensitivity analysis. Through this research, it is found that the proposed method has the characteristics of high efficiency, lightweight and stability, which simulates the safety dynamics of the ship navigational system intuitively like the mode of natural thinking. This work provides an alternative for the shipping safety decision maker to understand the navigation safety status and control the evolving trend of it for a foreseeable period.

Information domain analysis of respiratory sinus arrhythmia mechanisms.

Ventilation related heart rate oscillations - respiratory sinus arrhythmia (RSA) - originate in human from several mechanisms. Two most important of them - the central mechanism (direct communication between respiratory and cardiomotor centers), and the peripheral mechanism (ventilation-associated blood pressure changes transferred to heart rate via baroreflex) have been described in previous studies. The major aim of this study was to compare the importance of these mechanisms in the generation of RSA non-invasively during various states by quantifying the strength of the directed interactions between heart rate, systolic blood pressure and respiratory volume signals. Seventy-eight healthy volunteers (32 male, age range: 16.02-25.77 years, median age: 18.57 years) participated in this study. The strength of mutual interconnections among the spontaneous beat-to-beat oscillations of systolic blood pressure (SBP), R-R interval (RR signal) and respiration (volume changes - RESP signal) was quantified during supine rest, orthostatic challenge (head-up tilt, HUT) and cognitive load (mental arithmetics, MA) using bivariate and trivariate measures of cardio-respiratory information transfer to separate baroreflex and nonbaroreflex (central) mechanisms. Our results indicate that both basic mechanisms take part in RSA generation in the intact cardiorespiratory control of human subjects. During orthostatic and mental challenges baroreflex based peripheral mechanism becomes more important.

The effects of TiO2 nanoparticles addition on the thermal shock resistance, shear strength and IMC layer growth of SAC305 alloy

The present study explores the effect of nano-sized TiO2 ceramic particles on interfacial structure, creep and shear strength of eutectic Sn-3Ag-0.5Cu (SAC305 in wt.%) interconnect material under isothermal aging and thermal shock cycles. The study revealed that the non-reacting and non-coarsening TiO2 nanoparticles improved the creep, shear strength and thermal shock resistance through refined the structure. An interfacial microstructural analysis confirmed that a layer types Cu6Sn5 and Cu3Sn phases are grown at the interface of SAC305/Cu system. When SAC305 was in contact with the Au/Ni-plated Cu pad ball grid array (BGA) substrate, a ternary (Cu, Ni)-Sn phase was detected. The thickness of IMC phase is increased with the aging time. However, the growth of IMC layer as well as the IMC phases inside the matrix e.g., Ag3Sn, Cu6Sn5, and AuSn4 is slower in the composite interconnection systems than that of reference interconnection systems. Furthermore, during the thermal shock cycles some micro-cracks are found at the interface and matrix regions in the reference interconnection systems. The measured shear strength of composite interconnections exhibited higher values as compare to the reference interconnection systems. However, the thermal shock cycles degrades the shear strength of interconnection faster than the situation in isothermal aging.

Navigability of multiplex temporal network

•We consider the dynamics of three walk processes and the multiplex temporal network at the same time scale.•The multiplex structure with the different interconnection strengths D may change the nodes importance to some extent.•The strategies of RWC and RWP speed up the less diffusive of the layers.•The strategy of RWE enhances the diffusion of both layers.

Zero forcing number, constrained matchings and strong structural controllability

The zero forcing number is a graph invariant introduced to study the minimum rank of the graph. In 2008, Aazami proved the NP-hardness of computing the zero forcing number of a simple undirected graph. We complete this NP-hardness result by showing that the non-equivalent problem of computing the zero forcing number of a directed graph allowing loops is also NP-hard. The rest of the paper is devoted to the strong controllability of a networked system. This kind of controllability takes into account only the structure of the interconnection graph, but not the interconnection strengths along the edges. We provide a necessary and sufficient condition in terms of zero forcing sets for the strong controllability of a system whose underlying graph is a directed graph allowing loops. Moreover, we explain how our result differs from a recent related result discovered by Monshizadeh et al. Finally, we show how to solve the problem of finding efficiently a minimum-size input set for the strong controllability of a self-damped system with a tree-structure.

Adaptation and optimization of the synchronization gains in the adaptive spacecraft attitude synchronization

This work considers various optimization aspects for the attitude synchronization of spacecraft formation. Revisiting the synchronization controllers that address unknown parameters via adaptive techniques, this work proposes a modification to the synchronization weights employed to enforce state agreement amongst the networked spacecraft. By augmenting a standard adaptive controller that accounts for unknown parameters, with adaptation of the synchronization weights, one opts to improve spacecraft synchronization. The interconnection strengths of the network nodes, responsible for enforcing synchronization amongst spacecraft, are weighted dynamically in proportion to the disagreement between the spacecraft states. Additionally, when all spacecraft are assumed identical, differing only in their orientation and initial conditions, a modification of the standard adaptive parameter laws allows to reach parameter consensus via a dynamic penalization on the pairwise differences of parameter estimates. The proposed adaptive control architecture which allows for adaptation of both parameter uncertainties and synchronization penalty terms is demonstrated via numerical studies of a four-spacecraft network with limited connectivity. As an alternative to the dynamic adjustment of the synchronization gains, a scheme is proposed to optimally select these gains. By considering the sum of deviation-from-the-mean and rotational kinetic energy as appropriate metrics for synchronization, the numerical studies provide insights into the selection of optimal edge-dependent synchronization gains when the initial conditions are assumed available.

Navigability of interconnected networks under random failures.

Assessing the navigability of interconnected networks (transporting information, people, or goods) under eventual random failures is of utmost importance to design and protect critical infrastructures. Random walks are a good proxy to determine this navigability, specifically the coverage time of random walks, which is a measure of the dynamical functionality of the network. Here, we introduce the theoretical tools required to describe random walks in interconnected networks accounting for structure and dynamics inherent to real systems. We develop an analytical approach for the covering time of random walks in interconnected networks and compare it with extensive Monte Carlo simulations. Generally speaking, interconnected networks are more resilient to random failures than their individual layers per se, and we are able to quantify this effect. As an application--which we illustrate by considering the public transport of London--we show how the efficiency in exploring the multiplex critically depends on layers' topology, interconnection strengths, and walk strategy. Our findings are corroborated by data-driven simulations, where the empirical distribution of check-ins and checks-out is considered and passengers travel along fastest paths in a network affected by real disruptions. These findings are fundamental for further development of searching and navigability strategies in real interconnected systems.

Effect of Thermal Aging and Salt Spray Testing on Reliability and Mechanical Strength of Sn–58Bi Lead-Free Solder

Low-cost manufacturing in the electronics industry is becoming more demanding, particularly in the production of consumer electronics. Such manufacturing processes require reliable and low-cost lead-free solders. Among the low-temperature lead-free solders, eutectic Sn-Bi solder attracts a great deal of interest as it offers good reliability compared with that of Sn-Pb solders. In this paper, the reliability of eutectic 42%Sn-58%Bi (wt.%) lead-free solder is studied using combinations of environmental tests such as thermal aging (TA) at 100 °C, salt spray test (SST), and a sequential combination of TA and SST. Microstructural studies on the samples are performed at different time intervals. To study the effect of salt spray and TA on the mechanical reliability, drop testing is performed on the samples. Failure analysis is conducted after different tests. Marked corrosion is seen after the SST. This also has a considerable effect on the impact strength of the solder interconnections. TA increases the thickness of the intermetallic layers, which is found to affect the drop performance.

Initial-Condition Estimation in Network Synchronization Processes: Algebraic and Graphical Characterizations of the Estimator

We pursue a graph-theoretic analysis of state inference in network synchronization (or diffusive) processes. Precisely, we study estimation of a non-random initial condition of a canonical synchronization dynamics defined on a graph, from noisy observations at a single network node. By characterizing the maximum-likelihood estimation of the initial condition and the associated Cramer-Rao bound, we identify graph properties (e.g., symmetries, interconnection strengths, spectral measures) that determine 1) whether or not estimation is possible and 2) the quality of the estimate.

Measurement of Adhesion Strength and Nanoindentation of Metal Interconnections of Al/Ni and TiW/Ni Layers Formed on Glass Substrate

Measurement of Adhesion Strength and Nanoindentation of Metal Interconnections of Al/Ni and TiW/Ni Layers Formed on Glass Substrate

Performance analysis and comparison of a minimum interconnections direct storage model with traditional neural bidirectional memories

This study proposes an efficient and improved model of a direct storage bidirectional memory, improved bidirectional associative memory (IBAM), and emphasises the use of nanotechnology for efficient implementation of such large-scale neural network structures at a considerable lower cost reduced complexity, and less area required for implementation. This memory model directly stores the X and Y associated sets of M bipolar binary vectors in the form of (MxN(x)) and (MxN(y)) memory matrices, requires O(N) or about 30% of interconnections with weight strength ranging between +/-1, and is computationally very efficient as compared to sequential, intraconnected and other bidirectional associative memory (BAM) models of outer-product type that require O(N(2)) complex interconnections with weight strength ranging between +/-M. It is shown that it is functionally equivalent to and possesses all attributes of a BAM of outer-product type, and yet it is simple and robust in structure, very large scale integration (VLSI), optical and nanotechnology realisable, modular and expandable neural network bidirectional associative memory model in which the addition or deletion of a pair of vectors does not require changes in the strength of interconnections of the entire memory matrix. The analysis of retrieval process, signal-to-noise ratio, storage capacity and stability of the proposed model as well as of the traditional BAM has been carried out. Constraints on and characteristics of unipolar and bipolar binaries for improved storage and retrieval are discussed. The simulation results show that it has log(e) N times higher storage capacity, superior performance, faster convergence and retrieval time, when compared to traditional sequential and intraconnected bidirectional memories.

Processing the Bouguer anomaly map of Biga and the surrounding area by the cellular neural network: application to the southwestern Marmara region

Abstract An image processing technique called the cellular neural network (CNN) approach is used in this study to locate geological features giving rise to gravity anomalies such as faults or the boundary of two geologic zones. CNN is a stochastic image processing technique based on template optimization using the neighborhood relationships of cells. These cells can be characterized by a functional block diagram that is typical of neural network theory. The functionality of CNN is described in its entirety by a number of small matrices (A, B and I) called the cloning template. CNN can also be considered to be a nonlinear convolution of these matrices. This template describes the strength of the nearest neighbor interconnections in the network. The recurrent perceptron learning algorithm (RPLA) is used in optimization of cloning template. The CNN and standard Canny algorithms were first tested on two sets of synthetic gravity data with the aim of checking the reliability of the proposed approach. The CNN method was compared with classical derivative techniques by applying the cross-correlation method (CC) to the same anomaly map as this latter approach can detect some features that are difficult to identify on the Bouguer anomaly maps. This approach was then applied to the Bouguer anomaly map of Biga and its surrounding area, in Turkey. Structural features in the area between Bandirma, Biga, Yenice and Gonen in the southwest Marmara region are investigated by applying the CNN and CC to the Bouguer anomaly map. Faults identified by these algorithms are generally in accordance with previously mapped surface faults. These examples show that the geologic boundaries can be detected from Bouguer anomaly maps using the cloning template approach. A visual evaluation of the outputs of the CNN and CC approaches is carried out, and the results are compared with each other. This approach provides quantitative solutions based on just a few assumptions, which makes the method more powerful than the classical methods.

Decentralized PDC for large-scale T-S fuzzy systems

This paper studies the decentralized stabilization problem for a large-scale system. The considered large-scale system comprises of a number of subsystems and each subsystem is represented by a Takagi-Sugeno (T-S) fuzzy model. The interconnection between any two subsystems may be nonlinear and satisfies some matching condition. By the concept of parallel distributed compensation (PDC), the decentralized fuzzy control for each subsystem is synthesized, in which the control gain depends on the strength of interconnections, maximal number of the fired rules in each subsystem, and the common positive matrix P/sub i/. Based on Lyapunov criterion and Riccati-inequality, some sufficient conditions are derived and the common P/sub i/ is solved by linear matrix inequalities (LMI) so that the whole closed loop large-scale fuzzy system with the synthesized fuzzy control is asymptotically stable. Finally, a numerical example is given to illustrate the control synthesis and its effectiveness.

DECENTRALIZED ADAPTIVE CONTROL WITH IMPROVED STEADY STATE PERFORMANCE

In this paper we propose a new output decentralized model reference adaptive control scheme to improve the steady state performance for a case large-scale systems with unknown interconnection strengths as well as uncertainties in subsystem dynamics. Additional model reference feedforward signals are introduced in the adaptive scheme. The proposed scheme uses decentralized local output feedback with centralized model reference coordination and provides zero tracking errors. In this way the totally decentralized structure of the current information update is saved, since there is no exchange of signals between the different subsystems. The simulation results have shown the effectiveness of our proposed scheme.