Stopping Condition Research Articles

AK-MCSi: A Kriging-based method to deal with small failure probabilities and time-consuming models

Reliability analyses still remain challenging today for many applications. First, assessing small failure probabilities is tedious because of the very large number of calculations required. Secondly, mechanical system models can require considerable numerical efforts. To deal with these problems, classical reliability analysis methods may be combined with those of meta-modeling, to enable the construction of a model like the former numerical model but with fewer time-consuming evaluations. Among these approaches, the Active Learning Reliability Method, combining Kriging and Monte Carlo Simulations, has received attention over the last few years. This method has several drawbacks, such as the difficulty to assess small failure probabilities and its inability to parallelize computations. The proposed paper focuses on the improvement of such a method to solve both these issues. It introduces a sequential Monte Carlo Simulation technique to deal with small failure probabilities. A multipoint enrichment technique is also proposed to allow parallelization and thus to reduce numerical efforts. Both these new techniques give rise to the proposal of a new, more conservative stopping condition for learning. The efficiency of this new method, called AK-MCSi, is then demonstrated using three examples for which the results show a significant reduction in the time required and/or the number of iterations needed for an accurate evaluation of the failure probability.

A DIRECT PROOF OF SCHWICHTENBERG’S BAR RECURSION CLOSURE THEOREM

AbstractIn [12], Schwichtenberg showed that the System T definable functionals are closed under a rule-like version Spector’s bar recursion of lowest type levels 0 and 1. More precisely, if the functional Y which controls the stopping condition of Spector’s bar recursor is T-definable, then the corresponding bar recursion of type levels 0 and 1 is already T-definable. Schwichtenberg’s original proof, however, relies on a detour through Tait’s infinitary terms and the correspondence between ordinal recursion for $\alpha < {\varepsilon _0}$ and primitive recursion over finite types. This detour makes it hard to calculate on given concrete system T input, what the corresponding system T output would look like. In this paper we present an alternative (more direct) proof based on an explicit construction which we prove correct via a suitably defined logical relation. We show through an example how this gives a straightforward mechanism for converting bar recursive definitions into T-definitions under the conditions of Schwichtenberg’s theorem. Finally, with the explicit construction we can also easily state a sharper result: if Y is in the fragment Ti then terms built from $BR^{\mathbb{N},\sigma } $ for this particular Y are definable in the fragment ${T_{i + {\rm{max}}\left\{ {1,{\rm{level}}\left( \sigma \right)} \right\} + 2}}$.

Joint anisotropic amplitude variation with offset inversion of PP and PS seismic data

With the increase in exploration target complexity, more parameters are required to describe subsurface properties, particularly for finely stratified reservoirs with vertical transverse isotropic (VTI) features. We have developed an anisotropic amplitude variation with offset (AVO) inversion method using joint PP and PS seismic data for VTI media. Dealing with local minimum solutions is critical when using anisotropic AVO inversion because more parameters are expected to be derived. To enhance the inversion results, we adopt a hierarchical inversion strategy to solve the local minimum solution problem in the Gauss-Newton method. We perform the isotropic and anisotropic AVO inversions in two stages; however, we only use the inversion results from the first stage to form search windows for constraining the inversion in the second stage. To improve the efficiency of our method, we built stop conditions using Euclidean distance similarities to control iteration of the anisotropic AVO inversion in noisy situations. In addition, we evaluate a time-aligned amplitude variation with angle gather generation approach for our anisotropic AVO inversion using anisotropic prestack time migration. We test the proposed method on synthetic data in ideal and noisy situations, and find that the anisotropic AVO inversion method yields reasonable inversion results. Moreover, we apply our method to field data to show that it can be used to successfully identify complex lithologic and fluid information regarding fine layers in reservoirs.

Unsupervised Hierarchical Clustering of Build Orders in a Real-Time Strategy Game

Currently, no artificial intelligence (AI) agent can beat a professional real-time strategy game player. Lack of effective opponent modeling limits an AI agent’s ability to adapt to new opponents or strategies. Opponent models provide an understanding of the opponent’s strategy and potential future actions. To date, opponent models have relied on handcrafted features and expert-defined strategies, which restricts AI agent opponent models to previously known and easily understood strategies. In this paper, we propose size-first hierarchic clustering to cluster players that employ similar strategies in a real-time strategy (RTS) game. We employ an unsupervised hierarchal clustering algorithm to cluster game build orders into strategy groups. To eliminate small outlying clusters, the hierarchal clustering algorithm was modified to first group the smallest cluster with its closest neighbor, i.e., size-first hierarchal clustering. In our analysis, we employ a previously developed dataset based on StarCraft: Brood War game replays. In our proposed approach, principal component analysis (PCA) is used to visualize player clusters, and the obtained PCA graphs show that the clusters are qualitatively distinct. We also demonstrate that a game’s outcome is marginally affected by both players’ clusters. In addition, we show that the opponent’s faction can be determined based on a player’s transition between clusters overtime. The novelty of our analysis is the lack of expert-defined features and an automated stopping condition to determine the appropriate number of clusters. Thus, the proposed approach is bias-free and applicable to any StarCraft-like RTS game.

Uma proposta de um modelo evolutivo para redes de difusão do conhecimento organizacional

Neste artigo, apresentamos um modelo teórico para simular a difusão de conhecimento em redes sociais organizacioanis usando uma abordagem evolutiva. Partindo do pressuposto que redes sociais, baseadas em processos de colaboração e cooperação entre pessoas, evoluem como organismos vivos, como descreveu Charles Darwin em “A Origem das Espécies”, a proposta é apresentar um modelo evolutivo para a difusão do conhecimento, em uma organização, em que os atores atuam como propagadores e / ou receptores de conhecimento, dependendo dos seus atributos. Consideram-se cada atributo como um gene, cujo conjunto, constitui um cromossomo. Baseado na teoria de Darwin, o modelo propõe mecanismos de cruzamentos e utilização de mutação entre os diversosatores envolvidos no proceso de difusão do conhecimento, durante várias gerações, até que seja atingida uma condição de parada. A principal contribuição do modelo é a proposição de um contexto aderente ao estudo da dinâmica de redes, utilizando os atributos dos atores e do ambiente organizacional como parâmetros.

Efficient Performance Monitoring for Ubiquitous Virtual Networks Based on Matrix Completion

Inspired by the concept of software-defined network and network function virtualization, vast virtual networks are generated to isolate and share wireless resources for different network operators. To achieve fine-grained resource control and scheduling among virtual networks (VNs), network performance monitoring is essential. However, due to limitation of hardware, real-time performance monitoring is impossible for a complete virtual network. In this paper, taking advantage of the low-rank characteristic of 90 virtual access points (VAPs) measurement data, we propose an intelligent measurement scheme, namely, adaptive and sequential sampling based on matrix completion (MC), which exploits from the MC to construct the complete data of VN performance from a partial direct monitoring data. First, to construct the initial measurement matrix, we propose a sampling correction model based on dispersion and coverage. Second, a stopping condition for the sequential sampling is introduced, based on the stopping condition, the sampling process for a period can stop without waiting for the matrix reconstruction to reach certain of accuracy level. Finally, the sampled VAPs are determined by referring the back-forth completed matrixes’ normalized mean absolute error. The experiments show that our approach can achieve a constant network perception and maintain a relatively low error rate with a small sampling rate.

Hardware Implementation of IT2FLC using FPGA for Control Applications

Interval Type2 Fuzzy Logic Control (IT2FLC) has been applied to a number of industrial, medical, home and military applications. Hardware implementation of IT2FLC can be achieved in a number of ways. One of these ways is the use of a Field Programmable Gate Array (FPGA).         In this paper, the design and implementation of an IT2FLC using FPGA has been presented. The proposed controller is of Mamdani type. It works in different structures (P/PI/PD/PID like IT2FLC) depending on two control lines, different number of triangular shape memberships (2-7) depending on three control lines, six tunable gains and within a range of sampling time of (0.01-1024) seconds. Three type reduction algorithms are used and it is found that the Enhanced Iterative Algorithm with Stop Condition (EIASC) produced the minimum reduction in FPGA size. Thus less execution time. The reduction size is about 75% than Karnick Mendel (KM) and is about 3% than Enhanced KM (EKM). Linear and nonlinear models are used to test the designed Controller. Gains are tuned manually to reach minimum overshoot, settling time and steady state error.                                                Simulation and Implementation results showed that the proposed controller works in an efficient way under no-load, load and uncertainty in the nonlinear model parameters.

A Practical Randomized CP Tensor Decomposition

The CANDECOMP/PARAFAC (CP) decomposition is a leading method for the analysis of multiway data. The standard alternating least squares algorithm for the CP decomposition (CP-ALS) involves a series of highly overdetermined linear least squares problems. We extend randomized least squares methods to tensors and show the workload of CP-ALS can be drastically reduced without a sacrifice in quality. We introduce techniques for efficiently preprocessing, sampling, and computing randomized least squares on a dense tensor of arbitrary order, as well as an efficient sampling-based technique for checking the stopping condition. We also show more generally that the Khatri-Rao product (used within the CP-ALS iteration) produces conditions favorable for direct sampling. In numerical results, we see improvements in speed, reductions in memory requirements, and robustness with respect to initialization.

Solving Elliptical Equations in 3D by Means of an Adaptive Refinement in Generalized Finite Differences

We apply a 3D adaptive refinement procedure using meshless generalized finite difference method for solving elliptic partial differential equations. This adaptive refinement, based on an octree structure, allows adding nodes in a regular way in order to obtain smooth transitions with different nodal densities in the model. For this purpose, we define an error indicator as stop condition of the refinement, a criterion for choosing nodes with the highest errors, and a limit for the number of nodes to be added in each adaptive stage. This kind of equations often appears in engineering problems such as simulation of heat conduction, electrical potential, seepage through porous media, or irrotational flow of fluids. The numerical results show the high accuracy obtained.

Low Latency Detection of Sparse False Data Injections in Smart Grids

We study low-latency detections of sparse false data injection attacks in power grids, where an adversary can maliciously manipulate power grid operations by modifying measurements at a small number of smart meters. When a power grid is under attack, the detection delay, which is defined as the time difference between the occurrence and detection of the attack, is critical to power grid operations. A shorter detection delay can shorten the response time, thus prevent catastrophic impacts from the attack. The objective of this paper is to develop low-latency false data detection algorithms that can minimize the detection delay subject to constraints on false alarm probability. The false data injection can be modeled with a sparse attack vector, with each non-zero element corresponding to one meter under attack. Since neither the support nor the values of the sparse attack vector is known, a new orthogonal matching pursuit cumulative sum (OMP-CUSUM) algorithm is proposed to identify the meters under attack while minimizing the detection delay. In order to recover the support of the sparse vector, we develop a new stopping condition for the iterative OMP algorithm by analyzing the statistical properties of the power grid measurements. Theoretical analysis and simulation results show that the proposed OMP-CUSUM algorithm can efficiently identify the meters under attack, and reliably detect false data injections with low delays while maintaining good detection accuracy.

GIS tools for preliminary debris-flow assessment at regional scale

The assessment of the areas endangered by debris flows is a major issue in the context of mountain watershed management. Depending on the scale of analysis, different methods are required for the assessment of the areas exposed to debris flows. While 2-D numerical models are advised for detailed mapping of inundation areas on individual alluvial fans, preliminary recognition of hazard areas at the regional scale can be adequately performed by less data-demanding methods, which enable priority ranking of channels and alluvial fans at risk by debris flows. This contribution focuses on a simple and fast procedure that has been implemented for regional-scale identification of debris-flow prone channels and prioritization of the related alluvial fans. The methodology is based on the analysis of morphometric parameters derived from Digital Elevation Models (DEMs). Potential initiation sites of debris flows are identified as the DEM cells that exceed a threshold of slope-dependent contributing area. Channel reaches corresponding to debris flows propagation, deceleration and stopping conditions are derived from thresholds of local slope. An analysis of longitudinal profiles is used for the computation of the runout distance of debris flows. Information on erosion-resistant bedrock channels and sediment availability surveyed in the field are taken into account in the applications. A set of software tools was developed and made available (https://github.com/HydrogeomorphologyTools) to facilitate the application of the procedure. This approach, which has been extensively validated by means of field checks, has been extensively applied in the eastern Italian Alps. This contribution discusses potential and limitations of the method in the frame of the management of small mountain watersheds.

Trace diagrams and biquandle brackets

We introduce a method of computing biquandle brackets of oriented knots and links using a type of decorated trivalent spatial graphs we call trace diagrams. We identify algebraic conditions on the biquandle bracket coefficients for moving strands over and under traces and identify a new stop condition for the recursive expansion. In the case of monochromatic crossings we show that biquandle brackets satisfy a Homflypt-style skein relation and we identify algebraic conditions on the biquandle bracket coefficients to allow pass-through trace moves.

Empirical studies of Gaussian process based Bayesian optimization using evolutionary computation for materials informatics

Using evolutionary computation, we empirically investigate convergence properties of Gaussian process based Bayesian optimization (BO). We use evolutionary computation for the learning of the prediction model and optimization of the acquisition function (auxiliary search) of BO. For practical use for materials informatics, we address three issues in BO: (1) the stopping conditions, (2) the initial data size, and (3) the unknown smoothness of the target function. Then, we introduce a goal-directed acquisition function in which a target value as a desired property of a compound is incorporated. In addition, we present an ensemble method of BO, in which each BO in the ensemble has a random property and a kernel function with a different smoothness. Experimental results for the materials data sets on melting points of binary compounds and hydrogen weight percentages of hydrogen storage materials with two to four constituent elements show the effectiveness of the ensemble method of BO. Additionally, using an ensemble of BOs presents that the obtained results (increase of the number of samples acquired) are not simply a result of additional BOs. The goal-directed acquisition function and the ensemble of BOs which we propose should be techniques that can be used in the realization of a new materials recommendation system with a self-learning algorithm. Due to the self-learning algorithm realized by BO, the property prediction performance of the algorithm would be increasingly improved.

Method for Diffusing Carrier Noise under Zero‐Speed Condition using a Zero‐Sequence Voltage

SUMMARYIn this paper, a technique is proposed to reduce the carrier noise of pulse width modulation (PWM) inverters for a permanent magnet motor drive. The carrier noise is generated by the motor and the reactor driven by the inverter and affected by the switching frequency. When a motor rotates and drives a machine, the carrier noise is hidden by the machine noise. However, when the motor speed is approximately zero, the carrier noise becomes dominant and raucous. The typical method to reduce the noise employs a switching frequency higher than the audible frequency of human hearing. However, the increase in switching frequency results in the inverter suffering a higher switching loss and lower efficiency. In particular, when the permanent magnet motor operates at approximately zero‐speed and full‐load, for example, in the hill‐start conditions of electric vehicles and the start and stop conditions of elevators, the current flows in specific power devices and the switching loss further increases. The proposed technique uses a zero‐sequence voltage, which is generated randomly with the M‐sequence signal, and diffuses the frequency components of the ripple contained in the current. The technique is able to reduce the noise without increasing the switching frequency of the inverter when the motor speed is almost zero. Simulation and experimental results show that the proposed technique can diffuse the carrier noise and the cycle of the M‐sequence signal changes the diffusion effect of the carrier noise.

Changes in heart rate and facial actions during a gaming session with provoked boredom and stress

This paper presents an experiment aimed at exploring the relation between facial actions (FA), heart rate (HR) and emotional states, particularly stress and boredom, during the interaction with games. Subjects played three custom-made games with a linear and constant progression from a boring to a stressful state, without pre-defined levels, modes or stopping conditions. Such configuration gives our experiment a novel approach for the exploration of FA and HR regarding their connection to emotional states, since we can categorize information according to the induced (and theoretically known) emotional states on a user level. The HR data was divided into segments, whose HR mean was calculated and compared in periods (boring/stressful part of the games). Additionally the 6 h of recordings were manually analyzed and FA were annotated and categorized in the same periods. Findings show that variations of HR and FA on a group and on an individual level are different when comparing boring and stressful parts of the gaming sessions. This paper contributes information regarding variations of HR and FA in the context of games, which can potentially be used as input candidates to create user-tailored models for emotion detection with game-based emotion elicitation sources.

Evolutionary computation in zoology and ecology.

Evolutionary computational methods have adopted attributes of natural selection and evolution to solve problems in computer science, engineering, and other fields. The method is growing in use in zoology and ecology. Evolutionary principles may be merged with an agent-based modeling perspective to have individual animals or other agents compete. Four main categories are discussed: genetic algorithms, evolutionary programming, genetic programming, and evolutionary strategies. In evolutionary computation, a population is represented in a way that allows for an objective function to be assessed that is relevant to the problem of interest. The poorest performing members are removed from the population, and remaining members reproduce and may be mutated. The fitness of the members is again assessed, and the cycle continues until a stopping condition is met. Case studies include optimizing: egg shape given different clutch sizes, mate selection, migration of wildebeest, birds, and elk, vulture foraging behavior, algal bloom prediction, and species richness given energy constraints. Other case studies simulate the evolution of species and a means to project shifts in species ranges in response to a changing climate that includes competition and phenotypic plasticity. This introduction concludes by citing other uses of evolutionary computation and a review of the flexibility of the methods. For example, representing species’ niche spaces subject to selective pressure allows studies on cladistics, the taxon cycle, neutral versus niche paradigms, fundamental versus realized niches, community structure and order of colonization, invasiveness, and responses to a changing climate.

Optimization of the operating conditions of gas-turbine power stations considering the effect of equipment deterioration

In recent years in most power systems all over the world, a trend towards the growing nonuniformity of energy consumption and generation schedules has been observed. The increase in the portion of renewable energy sources is one of the important challenges for many countries. The ill-predictable character of such energy sources necessitates a search for practical solutions. Presently, the most efficient method for compensating for nonuniform generation of the electric power by the renewable energy sources—predominantly by the wind and solar energy—is generation of power at conventional fossil-fuel-fired power stations. In Russia, this problem is caused by the increasing portion in the generating capacity structure of the nuclear power stations, which are most efficient when operating under basic conditions. Introduction of hydropower and pumped storage hydroelectric power plants and other energy-storage technologies does not cover the demand for load-following power capacities. Owing to a simple design, low construction costs, and a sufficiently high economic efficiency, gas turbine plants (GTPs) prove to be the most suitable for covering the nonuniform electric-demand schedules. However, when the gas turbines are operated under varying duty conditions, the lifetime of the primary thermostressed components is considerably reduced and, consequently, the repair costs increase. A method is proposed for determination of the total operating costs considering the deterioration of the gas turbine equipment under varying duty and start–stop conditions. A methodology for optimization of the loading modes for the gas turbine equipment is developed. The consideration of the lifetime component allows varying the optimal operating conditions and, in some cases, rejecting short-time stops of the gas turbine plants. The calculations performed in a wide range of varying fuel prices and capital investments per gas turbine equipment unit show that the economic effectiveness can be increased by 5–15% by varying the operating conditions and switching to the optimal operating modes. Consequently, irrespective of the fuel price, the application of the proposed method results in selection of the most beneficial operating conditions. Consideration of the lifetime expenditure included in the optimization criterion enables enhancement of the operating efficiency.

Optimal parameters and performance of artificial bee colony algorithm for minimum cost design of reinforced concrete frames

This study investigates the effect of controlling parameters on the performance of an artificial bee colony (ABC) algorithm in the optimum design of reinforced concrete frames under combination loads according to the ACI318-08 building code requirements for structural concrete. The objective function is the total cost of reinforced concrete frames, which consists of concrete cost, reinforcing bars cost, and formwork cost. The cross section of structure, diameter, and number of reinforcing bars are considered as the design variables. The effect of the number of bees, food source quantities, trial limit, and stopping condition on the cost design are studied and presented with statistical results. Three design examples are collected from related literature to evaluating the performance of ABC algorithm. The results demonstrate that the number of trial limits is critical to the quality of food source, while the numbers of bees, food sources, and trial limits impact the obtained optimum solution and usage time. The statistical results reveal that when the food source quantities are lower than the number of bees, ABC algorithm provides high performance for all the design examples.

Channel Estimation for 3-D Lens Millimeter Wave Massive MIMO System

Channel estimation for 3-D lens millimeter wave massive MIMO system is investigated. First, the structure of beamspace channel is analyzed to show that the dominant entries of beamspace channel matrix form a dual crossing (DC) shape. Then, a DC-based channel estimation algorithm is proposed to iteratively refine the selection of dominant entries until the stop condition is met. Simulation results show that the DC-based algorithm outperforms the existing algorithms, including orthogonal matching pursuit and adaptive support detection (ASD). At signal-to-noise ratio of 15 dB, the DC-based algorithm has 57.7% improvement in terms of normalized mean squared error compared with ASD while the computational complexity is only half of ASD due to fast computation of least squares estimation.

MINIMIZATION OF REAL POWER LOSS BY ENHANCED GREAT DELUGE ALGORITHM

This paper presents Enhanced Great Deluge Algorithm (EDA) for solving reactive power problem. Alike other local exploration methods, this Enhanced Great Deluge Algorithm (EDA) also swap general solution (fresh_Config) with most excellent results (most excellent_Config) that have been found by then. This deed prolong until stop conditions is offered. In this algorithm, new solutions are selected from neighbours. Selection strategy is different from other approaches. In order to evaluate validity of the proposed Enhanced Great Deluge Algorithm (EDA) algorithm, it has been tested on standard IEEE 118 & practical 191 bus test systems and compared to other standard reported algorithms. Results show that Enhanced Great Deluge Algorithm (EDA) reduces the real power loss and voltage profiles are within the limits.