Time-consuming Preprocessing Research Articles

Two stage Ordered Escape Routing combined with LP and heuristic algorithm for large scaled PCB

The Ordered Escape Routing (OER) problem, which is an NP-hard problem, is critical to PCB design. Primary methods based on integer linear programming (ILP) work well on small-scale PCBs with fewer pins. However, when dealing with large-scale instances, traditional ILP strategies frequently cause time violations as the number of variables increases due to time-consuming preprocessing. In addition, heuristic algorithms have a time advantage when dealing with specific problems. In this paper, We propose an efficient two-stage escape routing method that employs LP for global routing and uses a heuristic algorithm to deal with the path intersection problem to minimize wiring length and runtime for large-scale PCBs. We first model the OER problem as a min-cost multi-commodity flow problem and use ILP to solve it. Then, we relax the non-crossing constraints and transform the ILP model into an LP model to reduce the runtime. we also construct a crossing graph according to the intersection of routing paths and propose a heuristic algorithm to locate congestion quickly. Finally, we reduce the local area capacity and allow global automatic congestion optimization. Compared with the state-of-the-art work, experimental results show that our method can reduce the routing time by 60% and handle larger-scale PCB escape routing problems.

Evaluation of an immersed boundary numerical framework to address the wind field in complex urban topographies

Estimating the wind velocity field in an urban area is important for pedestrian comfort and safety, as the local wind velocities dictate the transport of heat and air pollution in urban environments. Therefore, it is an essential requirement to assess wind patterns when designing urban areas. Computational Fluid Dynamics (CFD) numerical solvers are usually employed to estimate the wind comfort in an urban area under different wind intensities and directions. However, CFD simulations are expensive in terms of time, especially when many scenarios are addressed to ensure safety and comfort for multiple conditions. Here, a CFD framework based on an immersed boundary approach to discretize the urban topography is developed and validated against experimental data in a wind tunnel and two different standard body-fitted mesh codes. The new solver employs a structured cartesian octree grid automatically generated from Lidar data of urban topographies. The advantages are eliminating the complex and time-consuming pre-processing of urban topographies and making the framework accessible to urban planners without CFD expertise. Furthermore, the code is equipped with GPU parallelization that further reduces the computational time. Provided that the best practice guidelines for urban simulations are satisfied, in particular at least 10 cells between two buildings, the code shows very good agreement in all the tests comprising of a simplified and real urban neighborhood highlighting the importance of accurately solving the complex terrain topography of an urban region when non-negligible elevation changes of the order of 20/40 meters are present.

Simultaneous determination of multiple quality indices of dried shrimp (Parapenaeopsis hardwickii) during storage using Raman spectroscopy.

Dried shrimp is a high-value fishery product worldwide, but rapid and accurate assessment of its quality remains challenging. In the present study, a new method based on Raman spectroscopy was developed for assessing the quality changes in dried shrimp (Parapenaeopsis hardwickii) during storage. A high-quality Raman spectrum of astaxanthin (AST) was obtained from the third abdominal segment of dried shrimp. The intensity ratio (I1520/I1446) of the band from 1520 cm-1 to that at 1446 cm-1, which was ascribed to AST and protein/lipid, respectively, was calculated. I1520/I1446 can probe AST degradation in dried shrimp during storage at both 37 and 4 °C and further reflect quality changes of dried shrimp, as indicated by indices including total volatile basic nitrogen, pH and thiobarbituric acid reactive substances. Compared to conventional methods, the proposed method avoids complex and time-consuming preprocessing and provides significant advantages including cost-effectiveness and rapid detection. © 2024 Society of Chemical Industry.

A graph neural network framework for spatial geodemographic classification

Geodemographic classifications are exceptional tools for geographic analysis, business and policy-making, providing an overview of the socio-demographic structure of a region by creating an unsupervised, bottom-up classification of its areas based on a large set of variables. Classic approaches can require time-consuming preprocessing of input variables and are frequently a-spatial processes. In this study, we present a groundbreaking, systematic investigation of the use of graph neural networks for spatial geodemographic classification. Using Greater London as a case study, we compare a range of graph autoencoder designs with the official London Output Area Classification and baseline classifications developed using spatial fuzzy c-means. The results show that our framework based on a Node Attributes-focused Graph AutoEncoder (NAGAE) can perform similarly to classic approaches on class homogeneity metrics while providing higher spatial clustering. We conclude by discussing the current limitations of the proposed framework and its potential to develop into a new paradigm for creating a range of geodemographic classifications, from simple, local ones to complex classifications able to incorporate a range of spatial relationships into the process.

A simple and rapid DNA extraction method from meat by microneedle patch for detection of adulterated components

ABSTRACT The simple and rapid identification of meat via DNA-based methods is still subject to the conditions of complex and time-consuming preprocessing. It usually takes 3–4 h to obtain DNA sample using conventionally available DNA extraction kits. The existing approaches are cumbersome, require large equipment, and are not suitable for field operation. This study developed a swelling microneedle patch, constituted by micron-sized copolymers of methyl vinyl ether and maleic acid (PMVE/MA). The microneedle patch was applied to the meat sample for 1 min before being pulled out and rinsed with Tris-EDTA (TE) buffer to obtain DNA, which could be used for polymerase chain reaction (PCR) without purification. The DNA extracted by the microneedle patch could meet the needs for amplification and identification of meat samples. The developed approach which combines the patch-based extraction method and conventional PCR amplification is quick, simple, and reliable for DNA extraction and identification of meat samples.

Multilingual automation of transcript preprocessing in Alzheimer's disease detection.

IntroductionAnalyzing linguistic functions can improve early detection of Alzheimer's disease (AD). To date, no studies have focused on creating a universal pipeline for clinical transcript preprocessing.MethodsThis article presents a simple and efficient method for processing linguistic and phonetic data, sequencing subproblems of cleaning, normalization, and measure extraction tasks. Because some of these tasks are language‐ and context‐ dependent, they were designed to be easily configurable, thus increasing their scalability when dealing with new corpora.ResultsResults show improved performances over previous studies in this time‐consuming preprocessing task. Moreover, our findings showed that some discursive markers extracted from transcripts revealed a significant correlation (>0.5) with cognitive impairment severity.DiscussionThis article contributes to the literature on AD by presenting an efficient pipeline that allows speeding up the transcripts preprocessing task. We further invite other researchers to contribute to this work to help improve the quality of this pipeline (https://github.com/LiNCS-lab/usAge).

Fast convolution sparse filtering and its application on gearbox fault diagnosis

Transmission, as a critical part of vehicles, is the hub of power transmission and the core of controlling speed change. Condition monitoring and diagnosis of transmissions have become an effective tool to ensure vehicle safety travelling. The intelligent fault diagnosis strategy using artificial intelligent methods has been studied and applied for gearbox fault diagnosis. However, most algorithms cannot guarantee both accuracy and training efficiency. In this paper, fast convolutional sparse filtering based on convolutional activation and feature normalization is proposed for gearbox fault diagnosis without any time-consuming preprocessing. In fast convolutional sparse filtering, the features of samples are optimized instead of local features, which could obviously reduce the dimension and construction time of the Hessian matrix. In addition, the output features are equally active to guarantee that all features have similar contributions. The l2-norm of the training features is recorded and used for pseudo-normalization of the test features. The proposed fast convolutional sparse filtering is validated by a bearing fault dataset and a planetary gear fault dataset. Verification results confirm that fast convolutional sparse filtering is a promising tool for fault diagnosis, which has obviously improved the diagnosis accuracy, training efficiency, and robustness and provides the greater advantage of handling large-scale datasets.

Prediction of pedestrian brain injury due to vehicle impact using computational biomechanics models: Are head-only models sufficient?

Objectives: Accident reconstruction using computational biomechanics models plays an important role in research and prevention of human brain injury caused by car-to-pedestrian impacts. Finite element (FE) “head-only” models (that represent only the pedestrian head and brain) used in such reconstruction do not account for the influence of the rest of the pedestrian body on the head kinematics due to the accident and, consequently, on the brain injury risk prediction. Application of full-scale FE pedestrian models, on the other hand, is limited by their high computational cost and, more importantly, by the time-consuming preprocessing when repositioning the model to represent the pedestrian posture and location in relation to the impacting car. The objective of this study is to propose a computational biomechanics modeling approach to overcome these challenges.Methods: First, we couple a validated commercial FE head-neck complex model and a multibody (MB) pedestrian model. This coupled FE-MB model is evaluated through application in reconstruction of a real-world car-to-pedestrian impact accident and comparison of the pedestrian kinematics predicted using this model with the results obtained from the established full-scale MB pedestrian model. Finally, we compare the results obtained using the coupled FE-MB model proposed in this study and FE head-only model in terms of both the head kinematics and brain injury risk predicted using the two models.Results: The results of analysis of head injury criterion (HIC15) and brain deformation-based injury criteria (instantaneous value of cumulative strain damage measure iCSDM and maximum shear strain of the brain tissue) indicate substantial differences in the head kinematics and brain injury risk predicted using the two models. The coupled FE-MB model predicts a high risk of the brain injury which is consistent with the database record of the analyzed accident, in particular for the impact between the pedestrian head and road surface. In contrast, the head-only model did not predict that such impact can occur. The FE head head-only model with rigid skull and prescribed acceleration–time history of the head center of gravity determined from accident reconstruction using the purely MB pedestrian model, predicted appreciably lower iCSDM than the coupled FE-MB model that accounts for skull deformations using the linear elastic-plastic material model.Conclusions: This study suggests that the FE head-only models may be deficient for car-to-pedestrian impact accident reconstruction and estimation of risk of the pedestrian brain injury. In particular, this applies to the models that simplify the pedestrian skull as a rigid body.

Influential spreaders identification in complex networks with improved k-shell hybrid method

Identifying influential spreaders in a complex network has practical and theoretical significance. In applications such as disease spreading, virus infection in computer networks, viral marketing, immunization, rumor containment, among others, the main strategy is to identify the influential nodes in the network. Hence many different centrality measures evolved to identify central nodes in a complex network. The degree centrality is the most simple and easy to compute whereas closeness and betweenness centrality are complex and more time-consuming. The k-shell centrality has the problem of placing too many nodes in a single shell. Over the time many improvements over k-shell have been proposed with pros and cons. The k-shell hybrid (ksh) method has been recently proposed with promising results but with a free parameter that is set empirically which may cause some constraints to the performance of the method. This paper presents an improvement of the ksh method by providing a mathematical model for the free parameter based on standard network parameters. Experiments on real and artificially generated networks show that the proposed method outperforms the ksh method and most of the state-of-the-art node indexing methods. It has a better performance in terms of ranking performance as measured by the Kendall’s rank correlation, and in terms of ranking efficiency as measured by the monotonicity value. Due to the absence of any empirically set free parameter, no time-consuming preprocessing is required for optimal parameter value selection prior to actual ranking of nodes in a large network.

Direct Discrimination of Edible Oil Type, Oxidation, and Adulteration by Liquid Interfacial Surface-Enhanced Raman Spectroscopy.

The quality and safety of edible oils is a momentous but formidable challenge, especially regarding identification of oil type, oxidation, and adulteration. Most conventional analytical methods have bottlenecks in sensitivity, specificity, accessibility, or reliability. Surface-enhanced Raman spectroscopy (SERS) is promising as an unlabeled and ultrasensitive technique but limited by modification of inducers or surfactants on metal surfaces for oil analysis. Here, we develop a quantitative SERS analyzer on two-liquid interfacial plasmonic arrays for direct quality classification of edible oils by a portable Raman device. The interfacial plasmonic array is self-assembled through mixing the gold nanoparticle (GNP) sols and oil sample dissolved in chloroform without any surfactants or pretreatments. Different kinds of edible oils dissolved in chloroform directly participate in self-assembly of plasmonic arrays that finally localizes onto a three-dimensional (3D) oil/water interface. The 3D plasmonic array is self-healing, shape adaptive, and can be transferred to any glass containers as a substrate-free SERS analyzer for direct Raman measurements. It produces sensitive responses of SERS on different kinds of edible oils. By virtue of principal component analysis (PCA), this analyzer is able to quickly distinguish six edible oils, oxidized oils, and adulterated oils. Moreover, the solvent chloroform generates unique and stable SERS bands that can utilized as an inherent internal standard (IIS) to calibrate SERS fluctuation and greatly improve quantitation accuracy. Compared to conventional lab methods, this analyzer avoids complex and time-consuming preprocessing and provides significant advantages in cost, speed, and utility. Our study illuminates a facile way to determine edible oil quality and promises great potential in food quality and safety analysis.

Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input

Periodic vibration signals captured by the accelerometers carry rich information for bearing fault diagnosis. Existing methods mostly rely on hand-crafted time-consuming preprocessing of data to acquire suitable features. In this paper, we use an easy and effective method to transform the 1-D temporal vibration signal into a 2-D image. With the signal image, convolutional Neural Network (CNN) is used to train the raw vibration data. As powerful feature extractor and classifier for image recognition, CNN can learn to acquire features most suitable for the classification task by being trained. With the image format of vibration signals, the neuron in fully-connected layer of CNN can see farther and capture the periodic feature of signals. According to the results of the experiments, when fed in enough training samples, the proposed method outperforms other common methods. The proposed method can also be applied to solve intelligent diagnosis problems of other machine systems.

Spectrum sensing of trace C(2)H(2) detection in differential optical absorption spectroscopy technique.

An improved algorithm for trace C(2)H(2) detection is presented in this paper. The trace concentration is accurately calculated by focusing on the absorption spectrum from the frequency domain perspective. The advantage of the absorption spectroscopy frequency domain algorithm is its anti-interference capability. First, the influence of the background noise on the minimum detectable concentration is greatly reduced. Second, the time-consuming preprocess of spectra calibration in the differential optical absorption spectroscopy technique is skipped. Experimental results showed the detection limit of 50 ppm is achieved at a lightpath length of 0.2 m. This algorithm can be used in real-time spectrum analysis with high accuracy.

Web Clustering Based On Tag Set Similarity

Tagging is a service that allows users to associate a set of freely determined tags with web content. Clustering web documents with tag sets can eliminate the time-consuming preprocess of word stemming. This paper proposes a novel method to compute the similarity between tag sets and use it as the distance measure to cluster web documents into groups. Major steps in this method include computing a tag similarity matrix with set-based vector space model, smoothing the similarity matrix to obtain a set of linearly independent vectors and compute the tag set similarity based on these vectors. The experimental results show that the proposed tag set similarity measures surpasses other common similarity measures not only in the reliable derivation of clustering results, but also in clustering accuracies and efficiencies.

A Communication System Supporting Simultaneous Planning and Execution in Cranio-Maxillo-Facial Surgery

This paper describes the development of a system for simultaneous planning and execution of surgical operations in the cranio-maxillo-facial area. Simultaneous planning and execution is the process of taking an implicit task description, planning a sequence of explicit execution commands (e.g. for robots) and monitoring their execution. As the execution planning process is run completely on-line, during the execution of the assembly task, the planning process is highly reactive, based on sensor information about the robot's present environment. In order to meet the problem that medical data are usually complex and need time-consuming preprocessing, an appropriate architecture for evaluating sensor data has been developed. In this paper, a detailed presentation of the phases of execution planning and sensor data evaluation is given. As an example, the execution of a LeFort I osteotomy is presented.

A communication system supporting simultaneous planning and execution in cranio-maxillo-facial surgery.

This paper describes the development of a system for simultaneous planning and execution of surgical operations in the cranio-maxillo-facial area. Simultaneous planning and execution is the process of taking an implicit task description, planning a sequence of explicit execution commands (e.g. for robots) and monitoring their execution. As the execution planning process is run completely on-line, during the execution of the assembly task, the planning process is highly reactive, based on sensor information about the robot's present environment. In order to meet the problem that medical data are usually complex and need time-consuming preprocessing, an appropriate architecture for evaluating sensor data has been developed. In this paper, a detailed presentation of the phases of execution planning and sensor data evaluation is given. As an example, the execution of a LeFort I osteotomy is presented.