Test Time Cost Research Articles

Prediction of time-dependent concrete mechanical properties based on advanced deep learning models considering complex variables

Evaluating the mechanical properties such as compressive strength, tensile strength, and modulus of elasticity (MOE) of concrete is crucial for the design, construction, and quality control of engineering projects. Developing a comprehensive theoretical-based numerical model to predict the concrete mechanical properties is considered challenging, owing to the diversity of admixtures and curing environment affecting the performance of modern concrete. The deep learning (DL) model gives a feasible approach as data-driven methods for accurate prediction of various material properties these years. However, predicting time-dependent mechanical properties of modern concrete with complex mix constituents by DL algorithms is still confined so far. In this article, artificial neuron network (ANN) and long short-term memory (LSTM) based DL models were established to predict concrete compressive strength, tensile strength, and MOE at different ages, considering the mix design, curing condition, curing time and tension testing method as input variables. LSTM outperformed ANN in terms of the statistical indicators for accuracy evaluation with high training and testing time costs. Bidirectional modification and multi-head attention mechanism were implemented to further improve the prediction accuracy of LSTM. It is shown that the multi-head attention bidirectional LSTM had more precise predicting results (R2=0.9355 for compressive strength, R2=0.8930 for tensile strength, R2=0.9584 for MOE) and fitting of the mechanical property-age curves. The LSTM-based models provided a promising prediction tool for time-dependent concrete mechanical properties with complex input parameters.

Segmentation-free Recognition Algorithm Based on Deep Learning for Handwritten Text Image

Segmentation-based offline handwritten character recognition algorithms suffered from the segmenting difficulty of interleaving and touching in handwritten manuscripts. To tackle the problem, a segmentation-free recognition algorithm based on deep learning network is proposed in this paper. The network consists of four neural layers, including input layer for image preprocessing, CNNs layer for feature extraction, BDLSTM layer for sequence prediction, and connectionist temporal classification layer for text sequence alignment and classification. Besides, a novel data processing method is performed for data length equalization. Based on this, groups of experiments, based on six typical databases, involved in evaluation indicators of character correct rate, training time cost, storage space cost and testing time cost are carried out. The experimental results show that the proposed algorithm has better performances in accuracy and efficiency than other classical algorithms.

Prevention of PID Phenomenon for Solar Panel Based on Mathematical Data Analysis Models

In recent years, the problem of potential-induced degradation (PID) phenomenon has been deeply associated with solar power issues because it causes serious power attenuation of solar panels and results in lowering its power generation efficiency. Thus, effectively identifying the PID problem from insights of industry data analysis to reduce production costs and increase the performance of power generation is an interesting and important subject for the solar power industry. Moreover, by the traditional standard rule (IEC62804) and the condition of a 96 h testing time, the costs of testing time and assembling materials against PID are very high and must be improved. Given the above reasons, this study proposes a hybrid procedure to organizes four mathematical methods: the mini-module testing, solar cell testing, a settling time, and a neural network, which are named as Method-1–Method-4, respectively, to efficiently solve the PID problem. Consequently, there are four key outcomes from the empirical results for solar power application: (1) In Method-1 with a 96 h testing time, it was found that the large module with higher costs and the mini module with lower costs have a positive correlation; thus, we can replace the large-module testing by the effective mini module for lower cost on module materials. (2) In Method-2 with a 24 h testing time, it was also found that the mini module and the solar cell are positively correlated; this result provides evidence that we can conduct the PID test by the easier solar cell to lower the costs. (3) In Method-3, the settling time achieves an average accuracy of 94% for PID prediction with a 14 h testing time. (4) In Method-4, the experimental result provides an accuracy of 80% when identifying the PID problem with the mathematical neural network model and are obtained within a 2 h testing time. From the above results, these methods succeed in reducing cost of materials and testing time during the manufacturing process; thus, this study has an industrial application value. Concurrently, Method-3 and Method-4 are rarely seen in the limited literature review for identifying PID problem; therefore, this study also offers a novel contribution for technical application innovation.

Optimal Usability Test Procedure Generation for Medical Devices.

Medical device usability testing offers many benefits, including finding medical device usage errors and providing safety to users. As usability testing becomes mandatory for medical devices, manufacturers are increasing the cost burden. In order to perform a high-quality usability test, it is important to implement a usability test procedure, but guidelines for this are lacking. In this paper, we propose a method to systematically design and implement a usability test procedure. We propose methods to reduce test time-costs and apply them to implement the final procedure. Next, by applying the proposed method to sinus surgical navigation system, it is shown that the total time was reduced by 21% compared to the usability summative test procedure previously used in the same system.

Breast Cancer Histopathological Image Recognition Based on Pyramid Gray Level Co-Occurrence Matrix and Incremental Broad Learning

In order to recognize breast cancer histopathological images, this article proposed a combined model consisting of a pyramid gray level co-occurrence matrix (PGLCM) feature extraction model and an incremental broad learning (IBL) classification model. The PGLCM model is designed to extract the fusion features of breast cancer histopathological images, which can reflect the multiresolution useful information of the images and facilitate the improvement of the classification effect in the later stage. The IBL model is used to improve the classification accuracy by increasing the number of network enhancement nodes horizontally. Unlike deep neural networks, the IBL model compresses the training and testing time cost greatly by making full use of its single-hidden-layer structure. To our knowledge, it is the first attempt for the IBL model to be introduced into the breast cancer histopathological image recognition task. The experimental results in four magnifications of the BreaKHis dataset show that the accuracy of binary classification and eight-class classification outperforms the existing algorithms. The accuracy of binary classification reaches 91.45%, 90.17%, 90.90% and 90.73%, indicating the effectiveness of the established combined model and demonstrating the advantages in breast cancer histopathological image recognition.

Gap between Prediction and Truth: A Case Study of False-Positives in Leakage Detection

Since leakage detection was introduced as a popular side-channel security assessment, it has been plagued by false-positives (a.k.a. type I errors). To fix this error, the previous solutions set detection thresholds based on an assumption-based prediction of false-positive rate (FPR). However, this study points out that such a prediction (of FPR) may be inaccurate. We notice that the prediction in EuroCrypt2016 is much smaller than (approximately 1 / 779 times) the true FPR. The gap between prediction and truth, called underpredicted false-positives (UFP), leads to severe false-positives in leakage detection. Then, we check the statistical distribution of test statistics to analyze the cause of UFP. Our analysis indicates that the overlap between cross-validation (CV) blocks gives rise to an assumption error in the distribution of the CV-based estimates of ρ -statistics, which is the root cause of UFP. Therefore, we tackle the UFP by eliminating the overlap between blocks. Specifically, we propose a profiling-shared validation (PSV) and utilize this validation to improve the detection of any-variate any-order leakages. Our experiments show that the PSV solves the UFP and saves more than 75% of the test time costs. In summary, this article reports a potential flaw in leakage detection and provides a complete analysis of the flaw for the first time.

Similarity mapping for robust face recognition via a single training sample per person

Face recognition using a single training sample per person (STSPP) is very challenging due to significant variations in the query sample. Although most of sparse representation based methods eliminate the variations to obtain discriminative power, their recognition performance usually degrades as the number of training samples per person decreases. To improve recognition performance on the STSPP problem, in this paper, we propose a novel face recognition method: generating new training samples by preserving the variations of each query sample onto training samples instead of eliminating the variations, and then identifying the query one via the most similar generated training sample. Specifically, we formulate face recognition as an patch-based sparse representation problem, and then efficiently solve it by dividing the optimization procedure into three individual sub-problems: selecting training patches, weighting selected patches and calculating residuals. Meanwhile, we interpret the proposed algorithm from a probability modeling viewpoint. Experiments on multiple benchmark databases demonstrate its superior recognition performance to state-of-the-art methods on faces under illumination changes, occlusions and facial expressions, with lower test time costs.

Attribute Reduction in Time-cost-sensitive Decision Systems through Backtracking

Research in machine learning, statistics and related fields has produced a wide variety of algorithms for cost-sensitive learning. Some of these algorithms are devoted to minimal test cost reduction for money cost. In this paper, we consider attribute reduction based on time-cost-sensitive decision systems and propose a backtrack algorithm to find an optimal reduct. Time cost consists of testing time cost and waiting cost, where waiting cost can be overlapped with others. We prove that taking tests in non-ascending order of waiting time can minimize the total time cost. We also propose a backtrack algorithm with two pruning techniques to obtain an optimal reduct. Experimental results show that the pruning techniques are effective, and the algorithm is efficient on the Mushroom dataset.

Feature Selection with Time Cost Constraint

Feature selection is an important task in machine learning and data mining. In real world applications, time and money are required to obtain features of objects. Many existing works have been developed to preserve enough information of a decision system, and at the same time, minimize time cost or money cost. In this paper, we study feature selection with time cost constraint. Here time cost consists of testing time cost and waiting cost. The optimization objective is to obtain a feature subset with the lowest conditional informational entropy. Two algorithms are designed to obtain optimal solutions for the problem. We revise the first algorithm by utilizing the relationship of the conditional entropy between a set and its subsets, then obtain the second algorithm. Experimental results on four UCI datasets indicate that the efficiency improvement is significant.

Low-Cost EVM Test Methodology for Wireless Transmitters Applied to W-CDMA

In digital radio applications, the error vector magnitude (EVM) is a figure of merit that characterizes modulation accuracy. The EVM highlights modulation impairments caused by either the in-phase/quadrature (I/Q) baseband transmit chain, filters, modulator, or amplifier linearity. EVM testing in production implies the use of high-cost test instrumentation in automated test equipment (ATE), which requires a complex I/Q baseband modulator supporting digital modulation and a built-in high-performance receiver. In this paper, an alternate EVM measurement technique is proposed that not only eliminates digital signal processing but also relies on standard continuous-wave (CW) tone measurements to evaluate the EVM performance of the device under test (DUT). The presented method comes at no extra test time cost since the EVM is computed from a series of CW tests, which are traditionally tested to guarantee the RF modulator yield in mass production. Assuming each EVM contributor is uncorrelated, the method consists of using simple sine-wave/cosine-wave input I/Q stimuli applied to the DUT to quantify each impairment and then compute each EVM contribution to finally derive the resulting composite EVM. The presented ATE EVM computation results are in excellent agreement with the laboratory EVM measurements using ATE conformance test equipment. The technique is applied to a release'99 wideband code-division multiple-access (W-CDMA) band I [(Universal Mobile Telecommunications System (UMTS) 2100] direct-conversion RF integrated circuit (IC) modulator.

A robust multilingual portable phrase chunking system

Automatic text chunking aims to recognize grammatical phrase structures in natural language text. Text chunking provides downstream syntactic information for further analysis, which is also an important technology in the area of text mining (TM) and natural language processing (NLP). Existing chunking systems make use of external knowledge, e.g. grammar parsers, or integrate multiple learners to achieve higher performance. However, the external knowledge is almost unavailable in many domains and languages. Besides, employing multiple learners does not only complicate the system architecture, but also increase training and testing time costs. In this paper, we present a novel phrase chunking model based on the proposed mask method without employing external knowledge and multiple learners. The mask method could automatically derive more training examples from the original training data, which significantly improves system performance. We had evaluated our method in different chunking tasks and languages in comparison to previous studies. The experimental results show that our method achieves state of the art performance in chunking tasks. In two English chunking tasks, i.e., shallow parsing and base-chunking, our method achieves 94.22 and 93.23 in F(@b=1) rates. When porting to Chinese, the F(@b=1) rate is 92.30. Also, our chunker is quite efficient. The complete chunking time of a 50K-words is less than 10s.