Effect Of Different Parameter Settings Research Articles

Integrated train timetabling and rolling stock rescheduling for a disturbed metro system: A hybrid deep reinforcement learning and adaptive large neighborhood search approach

For a metro line with high density and short trip time, the original train timetable and rolling stock circulation face infeasible risks under unexpected disturbances. This paper focuses on integrated train timetabling and rolling stock rescheduling for a disturbed metro system to reduce the negative impact. The problem is formulated as a multi-objective master model, in which required operational constraints and dispatching strategies are taken into account. To satisfy the real-time requirement, an innovative solution framework consisting of an independent rescheduling process and a cooperative rescheduling process is proposed. In the independent rescheduling process, the master model is decomposed into a series of submodels in the unit of train service. The submodel can be transformed into a Markov Decision Process (MDP) with well-defined fundamental elements (i.e., state, action, and reward function). Based on the MDP, a hierarchical policy is developed by introducing deep reinforcement learning to generate the initial solution, including the lower-level policy for train timetable and the higher-level policy for flexible rolling stock circulation. In the cooperative rescheduling process, the selfishness of agents that appears after the model decomposition is overcome by an adaptive large neighborhood search algorithm, which can improve the solution quality. Finally, two sets of numerical experiments are conducted to demonstrate the performance of the proposed solution framework. The experimental results show that a near-optimal solution can be obtained in a short time, which is better than the currently used practical rules in the automatic train supervision system, especially during peak hours. Furthermore, the effects of different parameter settings are analyzed.

Eruption Forecasting of Strokkur Geyser, Iceland, Using Permutation Entropy

AbstractA volcanic eruption is usually preceded by seismic precursors, but their interpretation and use for forecasting the eruption onset time remain a challenge. A part of the eruptive processes in open conduits of volcanoes may be similar to those encountered in geysers. Since geysers erupt more often, they are useful sites for testing new forecasting methods. We tested the application of Permutation Entropy (PE) as a robust method to assess the complexity in seismic recordings of the Strokkur geyser, Iceland. Strokkur features several minute‐long eruptive cycles, enabling us to verify in 63 recorded cycles whether PE behaves consistently from one eruption to the next one. We performed synthetic tests to understand the effect of different parameter settings in the PE calculation. Our application to Strokkur shows a distinct, repeating PE pattern consistent with previously identified phases in the eruptive cycle. We find a systematic increase in PE within the last 15 s before the eruption, indicating that an eruption will occur. We quantified the predictive power of PE, showing that PE performs better than seismic signal strength or quiescence when it comes to forecasting eruptions.

Effects of Different Parameter Settings for 3D Data Smoothing and Mesh Simplification on Near Real-Time 3D Reconstruction of High Resolution Bioceramic Bone Void Filling Medical Images.

Three-dimensional reconstruction plays a vital role in assisting doctors and surgeons in diagnosing the healing progress of bone defects. Common three-dimensional reconstruction methods include surface and volume rendering. As the focus is on the shape of the bone, this study omits the volume rendering methods. Many improvements have been made to surface rendering methods like Marching Cubes and Marching Tetrahedra, but not many on working towards real-time or near real-time surface rendering for large medical images and studying the effects of different parameter settings for the improvements. Hence, this study attempts near real-time surface rendering for large medical images. Different parameter values are experimented on to study their effect on reconstruction accuracy, reconstruction and rendering time, and the number of vertices and faces. The proposed improvement involving three-dimensional data smoothing with convolution kernel Gaussian size 5 and mesh simplification reduction factor of 0.1 is the best parameter value combination for achieving a good balance between high reconstruction accuracy, low total execution time, and a low number of vertices and faces. It has successfully increased reconstruction accuracy by 0.0235%, decreased the total execution time by 69.81%, and decreased the number of vertices and faces by 86.57% and 86.61%, respectively.

开沟参数对果园开沟施肥机作业质量的影响

This study aims to evaluate the effects of different parameter settings on the ditching performance using a ditching-fertilizer. We aimed to improve the performance of ditching-fertilizer machine performance in sustainable agriculture. With Box-Behnken experimental design method, taking forward speed, the rotation speed of the ditching cutter, and the deflection angle of ditching cutter as experimental factors, taking ditching depth stability and soil coverage rate as test indexes, the operation parameters of orchard ditching-fertilizer machine are studied. The regression model between test indexes and experimental factors is established, and the influence of each factor on the experimental indexes is analyzed. The test factors are comprehensively optimized. The results show that when the forward speed is 0.8km/h, the rotation speed of the ditching cutter is 348r/min, and the deflection angle of the ditching cutter is 32°, the ditching effect is the best. At this time, the stability coefficient of the ditching depth is 98.33%, and the soil coverage rate is 81.53%. As for the field test, which measured the stability coefficient of ditching depth, and the average soil cover rate is 96.24%, and 79.14%, respectively, and the relative errors from the optimized value are 2.17%, and 3.02%, respectively.

Improved detection of faint streaks based on a streak-like spatial filter

This paper presents an algorithm to detect very faint object streaks on CCD images acquired with an optical system. The proposed detection method uses image filters simulating the geometrical form and orientation of possible streaks on the CCD image. The method searches for a matching between streak and filter evaluating the convolutions of the image with all possible filters. Based on the statistics of the image background a threshold is applied in order to accept, respectively reject, the possible streak candidates. The detection probabilities and the effect of different parameter settings are estimated with tests on simulated images, while subframes of real images are used to evaluate the applicability of the algorithm to real cases. The detection probability of this method depends on the length and on the signal-to-noise ratio of the streak. For long streaks, a detection for signal-to-noise values around 0.5 is achieved. The further characterization of the detected streak (e.g. centroid and length), which is not performed in the current algorithm, and the reduction of the computation time, which is relatively high for full acquired frames, as well as possible improvements are briefly addressed.

Experimental Study of the Collective and Cyclic Pitch Propeller for an Underwater Vehicle

A series of experimental studies of the innovative propulsor named Collective and Cyclic Pitch Propeller (CCPP) applied to an underwater vehicle were designed and performed at the Australian Maritime College, University of Tasmania. The bollard pull and captive model tests were conducted to investigate the characteristics of CCPP and to examine the effect of different parameter settings to its performance. The results show that the CCPP is able to generate effective manoeuvring forces in various operational condition. In addition, the obtained results in the form of force coefficients provide a useful empirical model for the simulation and control of an underwater vehicle equipped with this propulsor.

Recommending property with short days-on-market for estate agency

Estates with short days-on-market (DOM), referring to the properties whose days on the active market are short, attract realtors to gain commission quickly in transactions. With the rise of the Internet, massive information of on-sale houses can be obtained online. It is a challenge for estate agencies to mine estates with short DOM from such massive information. In this paper, we proposed an estate with short DOM appraisal framework to automatically recommend those estates using transaction data and profile information crawled from websites. Motivated by the estimation process of domain experts, we first seek similar estates with the location, structure and market information of estates. Then, the listing prices of similar estates were used to calculate a price interval. To evaluate the proposed framework, we used two real-world datasets, which consist of 220,000 on-sale estates and 643 estates with manually evaluated values in Chongqing real estate market. The results show that the proposed framework can estimate accurately about 78% estates. Two agencies scored the list of recommendation, in which 88% estates are trustworthy. Compared to the classical hedonic model, our method can successfully deal with problems such as the negative effects of special estates and the lack of labels. Moreover, both the spatial and temporal characteristics of an estate are integrated into our framework. The effect of different parameter settings is also discussed.

A Risk-Based Approach to Multi-Stage Probabilistic Transmission Network Planning

As a result of the power industry restructuring and the increasing integration of large-scale renewable energy, power transmission expansion planning (TEP) has become a complicated decision-making process requiring risk analysis. This paper proposes a chance constrained approach to TEP. The probabilistic load curtailment degree is quantified by a capped load curtailment probability, which is incorporated into our multi-stage TEP model. Moreover, the system dynamic performance including security and stability is also realistically considered in our model through an iterative procedure. To enhance the computational efficiency, probabilistic optimal power flow (POPF) is adopted in conjunction with an evolutionary algorithm (EA). In case studies, different TEP approaches are compared, and effects of different parameter settings are also investigated. According to the simulation results, our model can not only provide information regarding risks, but also help network planners make trade-offs to select the most flexible and cost-effective planning schemes.

Online path planning in dynamic environments using the curve shortening flow method

Handling tasks with robots which are interacting in a shared workspace provide a high risk of collision. A new approach based on curve shortening flows enables collision-free path planning for robots within a varying environment on basis of a workspace model. Thereby a global path planning method based on geometrical curvature flow is combined with the locally and reactively acting potential field method. In the authors’ previous work this path was formulated as a non-linear partial differential equation and solved using explicit time-integration. This article presents a linearized model of this method which overcomes the numerical stability problems of the former formulation and allows a more efficient computation which is strongly required for the algorithm to be run on a real-time robot control. After a detailed explanation of the algorithm simulative and experimental results using the example of a handling task performed by two 5-axis manipulators is presented and effects of different parameter settings on the resulting path are discussed.

Hair growth-promotion effects of different alternating current parameter settings are mediated by the activation of Wnt/β-catenin and MAPK pathway.

Electrical stimulation is being used in variable skin therapeutic conditions. There have been clinical studies demonstrating the positive effect of electrical stimuli on hair regrowth. However, the underlying exact mechanism and optimal parameter settings are not clarified yet. To investigate the effects of different parameter settings of electrical stimuli on hair growth by examining changes in human dermal papilla cells (hDPCs) in vitro and by observing molecular changes in animal tissue. In vitro, cultured hDPCs were electrically stimulated with different parameter settings at alternating current (AC). Cell proliferation was measured by MTT assay. The Ki67 expression was measured by immunofluorescence. Hair growth-related gene expressions were measured by RT-PCR. In animal model, different parameter settings of AC were applied to the shaved dorsal skin of rabbit for 8 weeks. Expression of hair-related genes in the skin of rabbit was examined by RT-PCR. At low voltage power (3.5 V) and low frequency (1 or 2 MHz) with AC, in vitro proliferation of hDPCs was successfully induced. A significant increase in Wnt/β-catenin, Ki67, p-ERK and p-AKT expressions was observed under the aforementioned settings. In animal model, hair regrowth was observed in the entire stimulated areas under individual conditions. Expression of hair-related genes in the skin significantly increased on the 6th week of treatment. There are optimal conditions for electrical stimulated hair growth, and they might be different in the cells, animals and human tissues. Electrical stimuli induce mechanisms such as the activation of Wnt/β-catenin and MAPK pathway in hair follicles.

A MILP approach to the optimization of banner display strategy to tackle banner blindness

The paper addresses the issue of online advertising efficiency in the context of the “banner blindness” phenomenon. We extend the traditional exposure metric of banner efficiency by allowing the effect of a banner display (exposure effect, EE) to vary depending on the sequence of pages that lead to the display. Within a user session on the given website, we assume that EE differs for a banner carried over from the previous page (low EE), from the case where the banner suddenly appears in a previously unoccupied place (high EE). This assumption implies that a banner’s efficiency can be increased by optimising its placement throughout the website’s structure; to this end, we develop a simple model framework. First, we formalize the efficiency metric based on expected total EE for a representative user, using a Markov chain model estimable from the website’s clickstream data. Next, we formulate the selection of efficient banner placement as a mixed integer linear programming problem. We consider two scenarios: (i) a static one, where banner placement is fixed throughout the day, and (ii) a dynamic one, where banner placement is allowed to vary in different time intervals in order to respond to changes in browsing patterns. Finally, we demonstrate the use of our model on an empiric case study, and analyse the effect of different parameter settings.

Optimization of astigmatic particle tracking velocimeters

Astigmatic particle tracking velocimetry (APTV) has been developed in the last years to measure the three-dimensional displacement of tracer particles using a single-camera view. The measurement principle relies on an astigmatic optical system that provides aberrated particle images with a characteristic elliptical shape univocally related to the corresponding particle depth position. Because of the precision of this method, this concept is well established for measuring and controlling the distance between a CD/DVD and the reading head. The optical arrangement of an APTV system essentially consists of a primary stigmatic optics (e.g., a microscope, or a camera objective) and an astigmatic optics, typically a cylindrical lens placed in front of the camera sensor. This paper focuses on the uncertainty of APTV in the depth direction. First, an approximated analytical model is derived and experimentally validated. From the model, a set of three non-dimensional parameters that are the most significant in the optimization of the APTV performance are identified. Finally, the effect of different parameter settings and calibration approaches are studied systematically using numerical Monte Carlo simulations. The results allow for the derivation of general criteria to minimize the overall error in APTV measurements and provide the basis for reliable uncertainty estimation for a wide range of applications.

Efficient data use in incremental actor–critic algorithms

Actor–critic (AC) reinforcement learning methods are on-line approximations to policy iterations and have wide application in solving large-scale Markov decision and high-dimensional learning control problems. In order to overcome data inefficiency of incremental AC algorithms based on temporal difference (AC-TD), two new incremental AC algorithms (i.e., AC-RLSTD and AC-iLSTD) are proposed by applying a recursive least-squares TD (RLSTD(λ)) algorithm and an incremental least-squares TD (iLSTD(λ)) algorithm to the Critic evaluation, which can make more efficient use of data than TD. The Critic estimates a value-function using the RLSTD(λ) or iLSTD(λ) algorithm and the Actor updates the policy based on a regular gradient obtained by the TD error. The improvement in learning evaluation efficiency of the Critic will contribute to the improvement in policy learning performance of the Actor. Simulation results on the learning control of an inverted pendulum and a mountain-car problem illustrate the effectiveness of the two proposed AC algorithms in comparison to the AC-TD algorithm. In addition the AC-iLSTD, using a greedy selection mechanism, can perform much better than the AC-iLSTD using a random selection mechanism. In the simulation, the effect of different parameter settings of the eligibility trace on the learning performance of AC algorithms is analyzed. Furthermore, it is found that different initial values of the variance matrix in the AC-RLSTD algorithm should be chosen appropriately to obtain better performance for different learning problems.

Process Factors Influence on Cavity Pressure Behavior in Microinjection Moulding

Process monitoring of microinjection moulding (μIM) is of crucial importance when analysing the effect of different parameter settings on the process and then in assessing its quality. Quality factors related to cavity pressure can provide valuable information about the process dynamics and also about the filling behavior of different polymer melts. In this paper, a pressure sensor mounted inside a tool cavity was employed to analyse maximum cavity pressure, pressure increase rate during filling and pressure work. The influence of four μIM parameters, melt temperature, mould temperature, injection speed, and packing pressure on these three pressure-related process parameters was investigated. A design of experiment study was conducted by moulding a test part, a microfluidic component, in three different polymer materials, PP, ABS, and PC. The results show a similar process behavior for all three polymers, in particular a higher injection speed led to a reduction of the pressure work while a lower mould temperature reduces the pressure rate.

Cavity Air Flow Behavior During Filling in Microinjection Molding

Process monitoring of microinjection molding (μ-IM) is of crucial importance in understanding the effects of different parameter settings on the process, especially on its performance and consistency with regard to parts’ quality. Quality factors related to mold cavity air evacuation can provide valuable information about the process dynamics and also about the filling of a cavity by a polymer melt. In this paper, a novel experimental setup is proposed to monitor maximum air flow and air flow work as an integral of the air flow over time by employing a microelectromechanical system gas sensor mounted inside the mold. The influence of four μIM parameters, melt temperature, mold temperature, injection speed, and resistance to air evacuation, on two air flow-related output parameters is investigated by carrying out a design of experiment study. The results provide empirical evidences about the effects of process parameters on cavity air evacuation, and the influence of air evacuation on the part flow length.

Conformational Sampling with Stochastic Proximity Embedding and Self-Organizing Superimposition: Establishing Reasonable Parameters for Their Practical Use

Stochastic proximity embedding (SPE) and self-organizing superimposition (SOS) are two recently introduced methods for conformational sampling that have shown great promise in several application domains. Our previous validation studies aimed at exploring the limits of these methods and have involved rather exhaustive conformational searches producing a large number of conformations. However, from a practical point of view, such searches have become the exception rather than the norm. The increasing popularity of virtual screening has created a need for 3D conformational search methods that produce meaningful answers in a relatively short period of time and work effectively on a large scale. In this work, we examine the performance of these algorithms and the effects of different parameter settings at varying levels of sampling. Our goal is to identify search protocols that can produce a diverse set of chemically sensible conformations and have a reasonable probability of sampling biologically active space within a small number of trials. Our results suggest that both SPE and SOS are extremely competitive in this regard and produce very satisfactory results with as few as 500 conformations per molecule. The results improve even further when the raw conformations are minimized with a molecular mechanics force field to remove minor imperfections and any residual strain. These findings provide additional evidence that these methods are suitable for many everyday modeling tasks, both high- and low-throughput.

Optimization of multiplane μPIV for wall shear stress and wall topography characterization

Multiplane μPIV can be utilized to determine the wall shear stress and wall topology from the measured flow over a structured surface. A theoretical model was developed to predict the measurement error for the surface topography and shear stress, based on a theoretical analysis of the precision in PIV measurements. The main parameters that affect the accuracy of the measurement are identified. The effect of different parameter settings is studied by means of Monte Carlo simulations, and the results are compared with an experimental test case. The results are used to determine the recommended parameter settings for this measurement approach.

SU‐FF‐I‐105: Positional Accuracy of a B‐Spline Based Method for Image Registration in the H&N Area: Effect of Noise, Contrast and Registration Parameters

Purpose: Evaluation of the B‐Spline based Registration Method (BSRM) and the effect of contrast, signal to noise ratio and registration parameter settings on the accuracy of the restored voxel displacements in deformed H&N images. Method and Materials: CT images of a 8×15×25 cm3 phantom were constructed. The phantom contained spherical and elliptical structures simulating a tumor and several H&N organs. From this set, additional CT sets were constructed by: 1) affine transformation, 2) imposing shaped deformations up to 20 mm and 3) addition of noise of 10 HU up to 300 HU (1SD). The BSRM of the Insight Toolkit combined with Elastix was used to register the various distorted phantoms to the original phantom. The effect of different parameter settings of the registration has been investigated (number of iterations, spacing of the B‐ spline grid, number of resolution levels). Displacement vectors calculated in the deformable registration were compared with the imposed deformations. Average residual voxel displacement and other similarity measures were calculated. Results: Satisfactory convergence of the average residual displacement is reached after 64, 64 and 128 iterations for 3 levels of resolution. A grid spacing of 8–12 mm gives good results, although for sharp deformations a 6 mm grid is better in most cases. The increase in noise has a minor influence on the accuracy. An average tracking accuracy of better than 2 mm for parotid gland edges seems feasible. In some areas, however residual displacement errors up to 6–8 mm are observed. Conclusions: The BSRM seems to be suitable for intensity based deformable image registration of structures like the parotid and submandibular glands in the H&N region. Careful tuning of parameter values is important to obtain reliable results.

On the construction of a ground truth framework for evaluating voxel-based diffusion tensor MRI analysis methods

Although many studies are starting to use voxel-based analysis (VBA) methods to compare diffusion tensor images between healthy and diseased subjects, it has been demonstrated that VBA results depend heavily on parameter settings and implementation strategies, such as the applied coregistration technique, smoothing kernel width, statistical analysis, etc. In order to investigate the effect of different parameter settings and implementations on the accuracy and precision of the VBA results quantitatively, ground truth knowledge regarding the underlying microstructural alterations is required. To address the lack of such a gold standard, simulated diffusion tensor data sets are developed, which can model an array of anomalies in the diffusion properties of a predefined location. These data sets can be employed to evaluate the numerous parameters that characterize the pipeline of a VBA algorithm and to compare the accuracy, precision, and reproducibility of different post-processing approaches quantitatively. We are convinced that the use of these simulated data sets can improve the understanding of how different diffusion tensor image post-processing techniques affect the outcome of VBA. In turn, this may possibly lead to a more standardized and reliable evaluation of diffusion tensor data sets of large study groups with a wide range of white matter altering pathologies. The simulated DTI data sets will be made available online (http://www.dti.ua.ac.be).

A low cost solution for post-biopsy complications using available RFA generator and coaxial core biopsy needle

Percutaneous image-guided needle biopsy is typically performed in highly vascular organs or in tumours with rich macroscopic and microscopic blood supply. The main risks related to this procedure are haemorrhage and implantation of tumour cells in the needle tract after the biopsy needle is withdrawn. From numerous conducted studies, it was found that heating the needle tract using alternating current in radiofrequency (RF) range has a potential to minimize these effects. However, this solution requires the use of specially designed needles, which would make the procedure relatively expensive and complicated. Thus, we propose a simple solution by using readily available coaxial core biopsy needles connected to a radiofrequency ablation (RFA) generator. In order to do so, we have designed and developed an adapter to interface between these two devices. For evaluation purpose, we used a bovine liver as a sample tissue. The experimental procedure was done to study the effect of different parameter settings on the size of coagulation necrosis caused by the RF current heating on the subject. The delivery of the RF energy was varied by changing the values for delivered power, power delivery duration, and insertion depth. The results showed that the size of the coagulation necrosis is affected by all of the parameters tested. In general, the size of the region is enlarged with higher delivery of RF power, longer duration of power delivery, and shallower needle insertion and become relatively constant after a certain value. We also found that the solution proposed provides a low cost and practical way to minimizes unwanted post-biopsy effects.