Stopping Condition Research Articles

A user-driven feedback approach for 2D direct current resistivity inversion based on particle swarm optimization

We have developed an inversion approach based on particle swarm optimization (PSO) for 2D direct current resistivity data. Once the PSO algorithm is implemented, cost-function changes on constraints and misfit criteria can be easily performed and an approximate invariance of the Lagrange multipliers is obtained if each term of the cost function is properly normalized. More importantly, the interpreter can divide the mesh into partitions and apply a different constraint or combination of constraints in each partition. We explore several restrictions on the log-resistivity variation, either in single or in joint versions, including spatial continuity constraints in the [Formula: see text]-and [Formula: see text]-norms, total variation, sparsity constraints using the discrete cosine transform and Daubechies bases, and minimum moment of inertia constraints to impose concentration of resistive or conductive materials along target axes. In the latter case, the earth surface might be used also as a target axis. In addition, the final state of the PSO algorithm, once a stopping condition has been reached, contains not only the best solution but also a cluster of good suboptimal solutions that can be used for uncertainty analysis. As a result, the interpreter has the flexibility to perform an interpretation process based on a feedback trial-and-error inversion approach, in a similar manner that he/she has when using a friendly forward modeling software. The interpreter can then drive the tentative solutions obtained along the inversion process to incorporate their conceptions about the geologic environment, besides appraising misfit and stability of the solutions. We evaluate synthetic and field data examples. In the first case, we determine how the interpreter can drive the inversion process in a karst environment to image dissolution features. In the second case, we explore a similar situation aiming to optimize borehole locations in fracture zones in crystalline rocks.

SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0)

ABSTRACTSdpnal+ is a MATLAB software package that implements an augmented Lagrangian based method to solve large scale semidefinite programming problems with bound constraints. The implementation was initially based on a majorized semismooth Newton-CG augmented Lagrangian method, here we designed it within an inexact symmetric Gauss-Seidel based semi-proximal ADMM/ALM (alternating direction method of multipliers/augmented Lagrangian method) framework for the purpose of deriving simpler stopping conditions and closing the gap between the practical implementation of the algorithm and the theoretical algorithm. The basic code is written in MATLAB, but some subroutines in C language are incorporated via Mex files. We also design a convenient interface for users to input their SDP models into the solver. Numerous problems arising from combinatorial optimization and binary integer quadratic programming problems have been tested to evaluate the performance of the solver. Extensive numerical experiments conducted in [L.Q. Yang, D.F. Sun, and K.C. Toh, SDPNAL+: A majorized semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints, Math. Program. Comput. 7 (2015), pp. 331–366] show that the proposed method is quite efficient and robust, in that it is able to solve 98.9% of the 745 test instances of SDP problems arising from various applications to the accuracy of in the relative KKT residual.

Combined FATEMD‐based band selection method for hyperspectral images

Feature selection, which is called band selection for hyperspectral data, is widely used for hyperspectral images. A novel hyperspectral band selection method based on combined fast and adaptive tridimensional empirical mode decomposition (cFATEMD) is proposed in this study. The hyperspectral data is decomposed into a set of tridimensional intrinsic mode functions (TIMFs) and a residual (RES) by FATEMD, which can reduce high-frequency noise and signal. A stop condition of the decomposition is proposed based on the k-means clustering algorithm and the Dunn validity index, which can prevent excessive decomposition and make generated RES contain as much useful information as possible. In consideration of the useful information in decomposition results, these TIMFs and the RES are combined into a new data based on the spectral similarity between themselves and the original data. Four state-of-the-art band selection methods, cooperating with the proposed cFATEMD, are used to select bands by the new combined data. Several experiments are conducted on three publicly available hyperspectral datasets and the results are compared with corresponding methods' results using the original data. Experimental results demonstrate that the proposed method yields great classification appearance.

A new method for reliability analysis of structures with mixed random and convex variables

• A new method for reliability analysis of structures with mixed random and convex variables is proposed. • It is proved that approximation accuracy of projection outlines is crucial for failure probability estimation . • A new projection-outline-based active learning strategy is proposed to sequentially update Kriging model . • A quantification function of metamodel uncertainty is developed for the stopping condition of Kriging model update. • The proposed method is tested by four examples and shows high accuracy and efficiency. This paper proposes a method combining projection-outline-based active learning strategy with Kriging metamodel for reliability analysis of structures with mixed random and convex variables. In this method, it is determined that the approximation accuracy of projection outlines on the limit-state surface is crucial for estimation of failure probability instead of the whole limit-state surface. To efficiently improve the approximation accuracy of projection outlines, a new projection-outline-based active learning strategy is developed to sequentially obtain update points located around the projection outlines. Taking into account the influence of metamodel uncertainty on the estimation of failure probability, a quantification function of metamodel uncertainty is developed and introduced in the stopping condition of Kriging metamodel update. Finally, Monte Carlo simulation is employed to calculate the failure probability based on the refined Kriging metamodel. Four examples including the Burro Creek Bridge and a piezoelectric energy harvester are tested to validate the performance of the proposed method. Results indicate that the proposed method is accurate and efficient for reliability analysis of structures with mixed random and convex variables.

Navigating around Patented Routes by Preserving Specific Motifs along Computer-Planned Retrosynthetic Pathways

Summary By keeping track of lists of specific bonds one wishes to preserve, a computer program is able to identify the key disconnections used in the patented syntheses and design synthetic routes that circumvent these approaches. Here, we provide examples of computer-designed syntheses relevant to medicinal chemistry, in which the machine avoids “strategic” disconnections common to industrial patents and is forced to use different starting materials. The ability of modern retrosynthetic planners to navigate around patented solutions may have significant implications for the ways in which intellectual property related to multistep syntheses is protected and/or challenged.

New formulation of power plants’ general complex orders on European electricity markets

The adequate modeling of power generation is one of the most challenging issues in the field of electricity markets. Practical market clearing requires simple and transparent formulations but some of the financial and technical conditions of power plants are inherently complicated. In Europe, this predicament is usually handled by introducing special order types on day-ahead power exchanges. This approach often leads to algorithmic complications for power exchanges because new order types usually imply specific constraints some of which can affect the behavior of their overall mathematical model.The officially proposed clearing algorithm of the all-European exchange (namely, EUPHEMIA) is supposed to handle so-called complex orders for suppliers. These are composed of a set of traditional hourly step bids with linking conditions i.e. the Minimum Income Condition, the Load Gradient Condition and the auxiliary Scheduled Stop Condition. A new formulation is proposed for the case when all three conditions are attached to the complex order. A single convex Mixed Integer Problem is solved for market clearing which is conceptually similar to the models already widely used in Europe. Numerical case studies are discussed along with practical viability.

Electric Camshaft Phasing System to Meet Euro 6/BS-VI Emission Norms for Gasoline Engine

<div class="section abstract"><div class="htmlview paragraph">In today’s ever-changing scenario, gasoline engine is going to be more acceptable passenger vehicle prime mover, as it meets Bharat Stage-VI (BS-VI) emission standard and need less cost of up-gradation. Variable cam phasing (VCP) system is well known & proven advanced technology in automotive world, which already used by many OEMs globally to improve fuel consumption and reduce engine emissions. Electric Cam Phasing (ECP) is an integration of electro-mechanical system. In ECP, angle shifting is independent of engine oil pressure, which allows a more aggressive engine calibration of valve timing to minimize active intervention in the ignition and fuel injection sequences. Early advance timing makes it possible for the combustion engine to build up torque more quickly during acceleration, which means that ECP not only helps to achieve high operating efficiency, but also good driving performance. This paper will describe use of advanced features of ECP over Hydraulic Cam Phaser (HCP) in achieving improved fuel consumption & reduction in HC & CO emissions to meet BS-VI standard by precise controlling of valve timing at engine start condition and to enhance driving comfort, low engine vibrations during engine stop condition.</div></div>

Bayesian optimum stopping rule for software release

This Paper proposes a Bayesian approach to find out the optimum stopping rule of software testing. We consider a discrete periodic debugging framework so that software can be released for market once the criteria are fulfilled. Simplification of stopping rules were obtained by using some specific prior distributions of the number of remaining bugs. We also develop necessary and sufficient conditions for stopping the software testing. Some illustrative examples are presented.

Active contour algorithm with discriminant analysis for delineating tumors in positron emission tomography.

In the context of cancer delineation using positron emission tomography datasets, we present an innovative approach which purpose is to tackle the real-time, three-dimensional segmentation task in a full, or at least nearly full automatized way. The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset. The algorithm takes care of identifying an optimal and user-independent region of interest around the anomalous tissue and located on the slice containing the highest standardized uptake value so to start the successive segmentation task. The three-dimensional volume is then reconstructed using a slice-by-slice marching approach until a suitable automatic stop condition is met. On each slice, the segmentation is performed using an enhanced local active contour based on the minimization of a novel energy functional which combines the information provided by a machine learning component, the discriminant analysis in the present study. As a result, the whole algorithm is almost completely automatic and the output segmentation is independent from the input provided by the user. Phantom experiments comprising spheres and zeolites, and clinical cases comprising various body districts (lung, brain, and head and neck), and two different radio-tracers (18 F-fluoro-2-deoxy-d-glucose, and 11C-labeled Methionine) were used to assess the algorithm performances. Phantom experiments with spheres and with zeolites showed a dice similarity coefficient above 90% and 80%, respectively. Clinical cases showed high agreement with the gold standard (R2 = 0.98). These results indicate that the proposed method can be efficiently applied in the clinical routine with potential benefit for the treatment response assessment, and targeting in radiotherapy.

Evaluation example of safety function (stop before accident) in safety confirmation interlock system

The safety confirmation interlock system, which is based on the principle of safety (confirmation) as the basis for safety, is an operation start command signal or a safety confirmation signal from a machine / system operation command and a safety state confirmation sensor / switch. An operation continuation permission signal is output. This is shown for signal output (safety state confirmation) and non-output (safety state unconfirmed). It becomes non-output and stop of the machine and system is executed by the normally closed type actuator. That is, the concept is to secure safety by stopping, but in the previous research, it is only about signal output and non-output, and the conditions for stopping are not discussed. In this report, we report the case of stopping in the safety confirmation interlock system and the evaluation case when stopping is considered as the safety function.

Knock Detection Based on Recursive Variational Mode Decomposition and Multilevel Semi-Supervised Local Fisher Discriminant Analysis

Knock is an abnormal combustion phenomenon in gasoline engines. Strong knocks will reduce the efficiency and durability of engine, while with slight knocks engines can run on a high-efficiency state. It is necessary to detect knock and control the state of knock in order to improve the thermal efficiency of engine. This paper proposes a novel approach for detecting engine knocks in various intensities based on vibration signal of engine block using variational mode decomposition (VMD) and semi-supervised local fisher discriminant analysis (SELF). Since the quadratic penalty of recursive VMD has a great influence on decomposition results, the approach establishes the connection between the quadratic penalty and the stop condition by analyzing a large amount of data and quantifies the relationship by polynomial fitting, which reduces the complexity and subjectivity of recursive VMD. A multilevel SELF is developed for solving the problem that SELFs sometimes may not find ideal embedding space under large scale dimensionality reduction. This method adopts multi embedding spaces, with gradually decreasing dimension, to reduce the dimensionality of original data by a series of small steps. Verifications show the proposed approach can achieve high classification accuracy in knock detection and is able to identify the intensity of knock. This research contributes to the field of engine abnormality detection and can be implemented on vibration-based faults diagnosis area.

A Spark-Based Parallel Fuzzy $c$ -Means Segmentation Algorithm for Agricultural Image Big Data

With the explosive growth of image big data in the agriculture field, image segmentation algorithms are confronted with unprecedented challenges. As one of the most important images segmentation technologies, the fuzzy c-means (FCMs) algorithm has been widely used in the field of agricultural image segmentation as it provides simple computation and high-quality segmentation. However, due to its large amount of computation, the sequential FCM algorithm is too slow to finish the segmentation task within an acceptable time. This paper proposes a parallel FCM segmentation algorithm based on the distributed memory computing platform Apache Spark for agricultural image big data. The input image is first converted from the RGB color space to the lab color space and generates point cloud data. Then, point cloud data are partitioned and stored in different computing nodes, in which the membership degrees of pixel points to different cluster centers are calculated and the cluster centers are updated iteratively in a data-parallel form until the stopping condition is satisfied. Finally, point cloud data are restored after clustering for reconstructing the segmented image. On the Spark platform, the performance of the parallel FCMs algorithm is evaluated and reaches an average speedup of 12.54 on ten computing nodes. The experimental results show that the Spark-based parallel FCMs algorithm can obtain a significant increase in speedup, and the agricultural image testing set delivers a better performance improvement of 128% than the Hadoop-based approach. This paper indicates that the Spark-based parallel FCM algorithm provides faster speed of segmentation for agricultural image big data and has better scale-up and size-up rates.

An Effective Approach for Obtaining a Group Trading Strategy Portfolio Using Grouping Genetic Algorithm

To determine an appropriate trading time for buying or selling stocks is always a difficult task. The common way to deal with it is using trading strategies formed by technical or fundamental indicators. Lots of approaches have been presented on how to form trading strategies and how to set suitable parameters for those strategies. Furthermore, some approaches were also designed to optimize a trading strategy portfolio, which is a set of strategies where the return and risk of the portfolio can be maximized and minimized, respectively. To provide a more useful trading strategy portfolio, we first define a group trading strategy portfolio (GTSP). Then, an algorithm that utilizes the grouping genetic algorithm is designed for solving the GTSP optimization problem. In the chromosome representation, the grouping, strategy, and weight parts are employed to encode a possible GTSP. The fitness value of a chromosome is calculated by the group balance, weight balance, portfolio return, and risk to assess the quality of every possible solution. Genetic operators, including crossover, mutation, and inversion, are applied on the population to form a new offspring. Evolution is continued until the stop conditions are reached. Lastly, experiments were conducted on two real datasets with different trends to show that the advantages and the effectiveness of the presented approach.

Piecewise Nonlinear Dynamic Modeling for Gear Transmissions With Rotary Inertia and Backlash

Establishing an accurate dynamic model for gear transmission is challenging because of the complex dynamic mechanical behavior of the gear transmission, particularly under difficult working conditions such as heavy loads and frequent starts and stops. This feature significantly increases the difficulty level of the dynamic analysis, control, and real-time fault diagnosis of such a gear transmission, given that sufficiently accurate dynamic models are indispensable for them. Thus, this study proposes a piecewise nonlinear model for gear transmissions by considering rotary inertia, backlash, and friction in control applications under difficult working conditions. The model parameters of gear transmissions can be calculated by using analytical calculation formulas. The experiments are conducted for the gear transmission system of a heavy-duty manipulator in start–stop and continuous rotation conditions. The experimental results show that the proposed piecewise nonlinear model is in good agreement with the practical dynamic gear transmission behavior, which is feasible for the control applications of gear transmissions under difficult working conditions.

Consequence Analysis of LNG Leaks During Offloading Operations: Effects of Substrate Types, Atmospheric Conditions, and Basic Process Control System Intervention

With the increasing demands for liquefied natural gas (LNG), more and more LNG terminals are being constructed, and the risk of leakage during LNG off-loading processes has been given increasing amounts of attention. In the case of leakage accident during unloading, the control system is still running when the emergency stop condition is not reached. The adjustment of the control system to unloading parameters will affect the size of the leakage amount. However, the consequences of hazardous material leakage accidents predicted by correlation models depend on the amount of leakage. For precisely calculating the leakage amount and analyzing the leakage consequence, an HYSYS model and a flame acceleration simulator (FLACS) model were presented. This paper consists of two main parts: 1) an HYSYS model to calculate the spill flow rate according to various operating conditions that are with and without basic process control system (BPCS) intervention and 2) a FLACS model that estimates the consequences of vapor explosions and pool fires. This paper presents the conventional estimation method and compares it with a new method that estimates the risk according to spill flow rate, meteorological conditions, and substrate type (solid or grated) on pool spreading and vapor cloud dispersion. Using six scenarios, simulations are used to demonstrate that the HYSYS-FLACS model calculates the equivalent stoichiometric clouds (Q9) and pooling areas as being much larger than those calculated by the conventional model. During an emergency, the HYSYS-FLACS model can be used to estimate the consequences of an unplanned LNG release.

A Fuzzy Selection Compressive Sampling Matching Pursuit Algorithm for its Practical Application

In compressive sampling matching pursuit algorithm, it requires that the sparsity information of original signal to control the size of the preliminary atomic set and the maximum number of the algorithm iteration. This weakens the reconstruction accuracy, increases the computation complexity and limits its practical application capacity. To overcome the problem, an improved method is proposed. The proposed method firstly sets a fixed step-size as the assumed sparsity to expand the preliminary atomic set at the initial stage when the sparsity information is unknown. Secondly, the proposed algorithm adopts the fuzzy threshold strategy to select the more relevant atoms from the preliminary atomic set to expand the candidate atomic set. Finally, the double threshold control method, multiply stages setting and variable step-size method are used to control the iteration stop condition and adjust the estimated sparsity. When the two threshold iteration stop conditions are simultaneously satisfied, the iteration stops, which shows that the reconstructed signal better approximated the original signal, and the reconstruction performance is the best. Otherwise, if only one of the conditions is satisfied, the size of the estimated sparsity is increased by the variable step size method to reduce the error between the reconstructed signal and original signal. In addition, we extended the proposed algorithm to the multiple measurement vectors scenario for joint sparse signal recovery. Simulation results indicate that the proposed algorithm is better than the other method in terms of the reconstruction performance in single measurement vector and multiple measurement vector cases.

A Novel Stacked Denoising Autoencoder-Based Reconstruction Framework for Cerenkov Luminescence Tomography

Cerenkov luminescence tomography (CLT) is a promising imaging modality in the field of optical molecular imaging (OMI), which successfully bridges the OMI and tradition nuclear medical imaging and provides the location and quantitative analysis of the distribution of radionuclide probes inside the biological objects. As the CLT is an inherent highly ill-posed inverse problem, it is still a challenge to obtain an accurate reconstruction result. Here, we proposed a novel reconstruction framework based on stacking denoising autoencoders (SDAE), which serve as one famous structure of the artificial neural network (ANN). In our framework, the initial permission region is the whole domain and then a traditional reconstruction algorithm is used to reconstruct each node’s energy. Then these nodes are clustered into two regions: permission region and non-permission region, and the permission region is used to start a new reconstruction loop where a new result can be obtained. The procedures above are repeated before the result meets the stop conditions. The numerical simulation experiments, physical phantom experiments and in vivo experiments are all carried out to validate the feasibility and potential of our framework. Results demonstrate that the proposed framework can indeed achieve a good performance in CLT reconstruction.

Dynamic tests of CO2-Based waste heat recovery system with preheating process

For engine waste heat recovery, CO2-based Transcritical Rankine Cycle (CTRC) system with preheating process has a strong capability of combining exhaust gas and engine coolant, thereby leading to improvement of output, efficiency improvement and the reduction in cooling load reduction. In previous researches, all the benefits were achieved and investigated mainly under ideal and stable engine conditions in previous researches. But in this study, various engine conditions were considered for a preheated CTRC test bench, namely starting and ‘warm up’ condition, stopping and idling condition, restarting condition and random condition. After system parameters (e.g. pressure, temperature) were measured, heat absorption quantity was calculated, and meanwhile net power output and thermal efficiency were estimated. The results show that the preheating process can improve the dynamic performance of the CTRC at various engine conditions. Firstly, the preheating process is beneficial for continuous output capacity and safe operation of CTRC. Besides, the preheating process also can avoid the sudden rise in the expansion inlet pressure under the stopping condition of engine, and meanwhile it plays an important energy supplement role in the random conditions of the engine, resulting in the cross change in the waste heat absorption from engine coolant and exhaust gas.

시내버스 운행차 공회전 제한장치 적용을 위한 제어기 개발

The Idle Stop and Go (ISG) system is a technology that turns the engine off when the vehicle stops and turns it on when the vehicle starts to reduce fuel consumption. City buses have longer idling time than other vehicles have because city buses stop at every bus station; for this reason, the ISG system is more effective for use in city buses to reduce fuel consumption. In addition to some hardware modifications, a controller is required for applying the ISG system to in-use city buses. This study focuses on developing an ISG control hardware and software. The controller communicates with the ECU (Engine Control Unit) and stops fuel injection when necessary. The engine stop conditions consider battery state of charge, road gradient and coolant temperature are investigated and introduced.

Early Stopping Criteria for Levenberg-Marquardt Based Neural Network Training Optimization

In this research we train a direct distributed neural network using Levenberg-Marquardt algorithm. In order to prevent overtraining, we proposed correctly recognized image percentage based on early stop condition and conduct the experiments with different stop thresholds for image classification problem. Experiment results show that the best early stop condition is 93% and other increase in stop threshold can lead to decrease in the quality of the neural network. The correct choice of early stop condition can prevent overtraining which led to the training of a neural network with considerable number of hidden neurons.