Traditional Search Methods Research Articles

Improved DFT Algorithm for 2D DOA Estimation Based on 1D Nested Array Motion

In this letter, two-dimensional (2D) direction of arrival (DOA) estimation using one-dimensional (1D) nested array motion is studied, and an improved Discrete Fourier transform (DFT) algorithm is proposed. Exploiting nested array to move vertically along its axis enables 2D DOA estimation with large-scale and hole-free difference co-array. DFT algorithm has lower complexity than multiple signal classifications(MUSIC) method, but traditional DFT algorithm still requires multiple extra searches to ensure estimation accuracy. We use DFT to obtain the initial angle estimations, and then compensate the estimation offsets via total least squares (TLS) constructed from Taylor expansion approximation. Compared with the traditional DFT search method, the proposed method has lower complexity and better performance. Multiple simulations are conducted to verify the effectiveness of our approach.

Optimizing Graphical Procedures for Multiplicity Control in a Confirmatory Clinical Trial via Deep Learning

In confirmatory clinical trials, it has been proposed to use a simple iterative graphical approach to construct and perform intersection hypotheses tests with a weighted Bonferroni-type procedure to control Type I errors in the strong sense. Given Phase II study results or other prior knowledge, it is usually of main interest to find the optimal graph that maximizes a certain objective function in a future Phase III study. In this article, we evaluate the performance of two existing derivative-free constrained methods, and further propose a deep learning enhanced optimization framework. Our method numerically approximates the objective function via feedforward neural networks (FNNs) and then performs optimization with available gradient information. It can be constrained so that some features of the testing procedure are held fixed while optimizing over other features. Simulation studies show that our FNN-based approach has a better balance between robustness and time efficiency than some existing derivative-free constrained optimization algorithms. Compared to the traditional stochastic search method, our optimizer has moderate multiplicity adjusted power gain when the number of hypotheses is relatively large. We further apply it to a case study to illustrate how to optimize a multiple testing procedure with respect to a specific study objective.

Hybrid Loss Minimization Control For Dual Three-phase Linear Induction Motor

Dual three-phase linear induction motor has harmonic losses that cannot be included in the loss-flux model in the z1-z2 subspace obtained by the generalized Clarke transform. The loss model of α-β subspace is not optimal for obtaining the reference flux. To solve this problem, losses in harmonic subspace was analyzed, and then a hybrid loss minimization control strategy was proposed. Based on the loss model that considers the end effect in the d-q subspace (transformed from α-β subspace), suboptimal flux linkage calculated by the model is set as the starting point of search controller, and optimal operating point could be obtained. Finally, the simulation results show that the strategy has better response performance than the traditional search method, and there is a certain loss reduction under various working conditions, especially at light load (0.2pu, 5 m/s), there is about 48.2% loss reduction.

Log-Based Anomaly Detection with the Improved K-Nearest Neighbor

Logs play an important role in the maintenance of large-scale systems. The number of logs which indicate normal (normal logs) differs greatly from the number of logs that indicate anomalies (abnormal logs), and the two types of logs have certain differences. To automatically obtain faults by K-Nearest Neighbor (KNN) algorithm, an outlier detection method with high accuracy, is an effective way to detect anomalies from logs. However, logs have the characteristics of large scale and very uneven samples, which will affect the results of KNN algorithm on log-based anomaly detection. Thus, we propose an improved KNN algorithm-based method which uses the existing mean-shift clustering algorithm to efficiently select the training set from massive logs. Then we assign different weights to samples with different distances, which reduces the negative effect of unbalanced distribution of the log samples on the accuracy of KNN algorithm. By comparing experiments on log sets from five supercomputers, the results show that the method we proposed can be effectively applied to log-based anomaly detection, and the accuracy, recall rate and F measure with our method are higher than those of traditional keyword search method.

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

A band selection approach based on Lévy sine cosine algorithm and alternative distribution for hyperspectral image

ABSTRACT Hyperspectral image (HSI) has become one of the most important remote sensing techniques for object interpretation by its abundant band information. As the data dimension increases, band selection technique is utilized to achieve the highest possible classification accuracy with fewer bands. Essentially, it is considered as an NP-hard problem, which is a non-deterministic problem within polynomial complexity and difficult to achieve a satisfactory solution using traditional search methods. Sine cosine algorithm (SCA) is a recently developed swarm intelligence algorithm based on the calculation of sine and cosine functions. To determine the parameters setting in SCA, Lévy flight technique is employed to improve the exploitation phase of the algorithm. In the paper, a new band selection method on the basis of SCA with a Lévy flight is proposed, and an alternative distribution is utilized to decrease the band dimension of HSI. In addition, crossover operation is conducted to enhance the optimal bits of each individual, and an evaluation criterion for assessing the performance of band selection is defined, allowing the classification accuracy and selected number of bands to be as balanced as possible. Experimental results demonstrate that the proposed band selection method is superior to other state-of-the-art approaches in terms of band subsets that achieve higher classification accuracy.

Predicting optimal parameters with random forest for quantum key distribution

For practical quantum key distribution (QKD) in finite-data case, full optimized parameters can greatly improve its key rate. To gain such optimal parameters, traditional search algorithms are performed quite frequently despite the high time and hardware overhead, which may be a severe challenge for real-time QKD systems and large-scale QKD networks. In this paper, instead of searching optimal parameters, we employ random forest to directly predict those parameters. Firstly, we illustrate the feasibility of this method with 3-intensity measurement-device-independent QKD (MDI-QKD). Later, we rebuild a versatile model applicable to MDI and BB84 protocol simultaneously. Both numerical simulations demonstrate our method enjoys a low time and hardware overhead compared with traditional search method and achieves over 99% of the optimal secure key rate as well, which is very promising in future QKD applications.

Study on Cascading Failures of Power Grid Information Physics Fusion System Based on Pattern Search

In a complex power grid, cascading failures that are triggered by some small disturbances can induce global catastrophic effects over the entire network. Based on the discussion of occurrence mechanism of power grid information physics fusion system cascading failures, this paper proposes an improved fault pattern search method. Compared with traditional search method, this method helps to improve the chain reaction set that causes the system failure, and makes it possible to make a comprehensive forecast search for grid link failures.

A Study of Optimizing Search Engine Results Through User Interaction

In this paper, we propose a method for optimizing search engine results based on user interaction. This method generates different search results mainly through the user’s operation on different search topics. There are two main differences between our method and the traditional personalized search method: personal privacy and storage space. First, traditional personal search methods need to record search and click records for individual users. However, these records have great personal privacy issues for users; especially in recent years, there have been personal privacy leaks that occurred in many large online companies (such as Facebook and Yahoo). Secondly, because different users need to record their own search records, the size of the storage space is closely related to the number of users and the amount of search records stored. However, the sum of individual users’ search and click records is a huge storage space in today’s Internet environment. To avoid these two issues for traditional personalized search methods, this study proposes a storage-free approach based on individual users operating on different search topics. In general, in addition to avoiding personal privacy and storage space issues, our method can also achieve optimal linear time in generating personalized search results.

An optimal mode selection algorithm for scalable video coding

Scalable video coding (SVC) is extended from its predecessor advanced video coding (AVC) because of its flexible transmission to all type of gadgets. However, SVC is more flexible and scalable than AVC, but it is more complex in determining the computations than AVC. The traditional full search method in the standard H.264 SVC consumes more encoding time for computation. This complexity in computation need to be reduced and many fast mode decision (FMD) algorithms were developed, but many fail to balance in all the three measures such as peak signal to noise ratio (PSNR), encoding time and bit rate. In this paper, the proposed optimal mode selection algorithm based on the orientation of pixels achieves better time saving, good PSNR and coding efficiency. The proposed algorithm is compared with the standard H.264 JSVM reference software and found to be 57.44% time saving, 0.43 dB increments in PSNR and 0.23% compression in bit rate.

An optimal mode selection algorithm for scalable video coding

Scalable video coding (SVC) is extended from its predecessor advanced video coding (AVC) because of its flexible transmission to all type of gadgets. However, SVC is more flexible and scalable than AVC, but it is more complex in determining the computations than AVC. The traditional full search method in the standard H.264 SVC consumes more encoding time for computation. This complexity in computation need to be reduced and many fast mode decision (FMD) algorithms were developed, but many fail to balance in all the three measures such as peak signal to noise ratio (PSNR), encoding time and bit rate. In this paper, the proposed optimal mode selection algorithm based on the orientation of pixels achieves better time saving, good PSNR and coding efficiency. The proposed algorithm is compared with the standard H.264 JSVM reference software and found to be 57.44% time saving, 0.43 dB increments in PSNR and 0.23% compression in bit rate.

Iterative Pointing Angle Calibration Method for the Spaceborne Photon-Counting Laser Altimeter Based on Small-Range Terrain Matching

The satellite, Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) has been equipped with a new type of spaceborne laser altimeter, which has the benefits of having small footprints and a high repetition rate, and it can produce dense footprints on the ground. Focusing on the pointing angle calibration of this new spaceborne laser altimeter, this paper proposes a fast pointing angle calibration method using only a small range of terrain surveyed by airborne lidar. Based on the matching criterion of least elevation difference, an iterative pointing angle calibration method was proposed. In the experiment, the simulated photon-counting laser altimeter data and the Ice, Cloud and Land Elevation Satellite-2 data were used to verify the algorithm. The results show that when 1 km and 2.5 km lengths of track were used, the pointing angle error after calibration could be reduced to about 0.3 arc-seconds and less than 0.1 arc-seconds, respectively. Meanwhile, compared with the traditional pyramid search method, the proposed iterative pointing angle calibration method does not require well-designed parameters, which are important in the pyramid search method to balance calculation time and calibration result, and the iterative pointing angle calibration method could significantly reduce the calibration time to only about one-fifth of that of the pyramid search method.

A Hybrid Memetic Algorithm for the Parallel Machine Scheduling Problem With Job Deteriorating Effects

This paper presents a hybrid memetic algorithm (HMA) for makespan minimization of parallel machine schedules with job deteriorating effects. The proposed HMA integrates two distinguished features - a novel compound neighborhood structure that extends the basic swap and insertion operators, as well as a divide-and-conquer based local search procedure. Unlike traditional local search methods, the divide-and-conquer based local search divides the original problem into subproblems so as to speed up the exploration of local optima. Experimental comparisons with the current state-of-the-art approaches show highly competitive performance of HMA in terms of both solution quality and computational efficiency. In particular, HMA obtains optimal solutions for all of the 900 small benchmark instances with an average computing time of 0.013 seconds and a success rate of 100%. Besides, it improves on the previous best-known results for 86 out of the 900 large benchmark instances, while matching the best-known results for the remaining cases. Additionally, we provide an analysis of the key algorithm features to identify its critical success factors.

Feature-Based Online Representation Algorithm for Streaming Time Series Similarity Search

With the rapid development of information technology, we have already access to the era of big data. Time series is a sequence of data points associated with numerical values and successive timestamps. Time series not only has the traditional big data features, but also can be continuously generated in a high speed. Therefore, it is very time- and resource-consuming to directly apply the traditional time series similarity search methods on the raw time series data. In this paper, we propose a novel online segmenting algorithm for streaming time series, which has a relatively high performance on feature representation and similarity search. Extensive experimental results on different typical time series datasets have demonstrated the superiority of our method.

Quantitative Analysis of Elements in Fertilizer Using Laser-Induced Breakdown Spectroscopy Coupled with Support Vector Regression Model.

The rapid detection of the elements nitrogen (N), phosphorus (P), and potassium (K) is beneficial to the control of the compound fertilizer production process, and it is of great significance in the fertilizer industry. The aim of this work was to compare the detection ability of laser-induced breakdown spectroscopy (LIBS) coupled with support vector regression (SVR) and obtain an accurate and reliable method for the rapid detection of all three elements. A total of 58 fertilizer samples were provided by Anhui Huilong Group. The collection of samples was divided into a calibration set (43 samples) and a prediction set (15 samples) by the Kennard–Stone (KS) method. Four different parameter optimization methods were used to construct the SVR calibration models by element concentration and the intensity of characteristic line variables, namely the traditional grid search method (GSM), genetic algorithm (GA), particle swarm optimization (PSO), and least squares (LS). The training time, determination coefficient, and the root-mean-square error for all parameter optimization methods were analyzed. The results indicated that the LIBS technique coupled with the least squares–support vector regression (LS-SVR) method could be a reliable and accurate method in the quantitative determination of N, P, and K elements in complex matrix like compound fertilizers.

A study on Web service supporting mobility of users using ICT-based autonomous feedback knowledge information

The 4th Industrial Revolution is influencing all the industries with the convergence and development of information communication technology. Particularly, the demand for “building a connected, more intelligent society” is amplified against the IoT (Internet of Things) and Web service area. The key of the 4th Industrial Revolution is to aim at optimized service by building the mutual communication system between the device and device and between the device and the person through the information and communication technology (ICT)-based IoT. However, since the existing IoT and Web service utilize only the limited form and amount of feedback knowledge information, it only could satisfy the individual service and could not provide integrated customized service demanded by the user. Therefore, in this study, the system that can support the customized mobile Web service utilizing autonomous feedback knowledge information with the existing service support methods is suggested. The information is generated in order to apply the Web service by collecting the data from the ICT environment devices, IoT devices, sensor, and wearable devices. The user can use the service suitable for diverse physical changes and spatial changes made during the movement using the information generated. The ICT-based autonomous feedback knowledge information suggested in this article is generated by collecting the changed data of the user. The generated information is identified through the analysis and the assessment as a stage to apply to the service level, and the identified service level gives feedback to the client by matching with the support service. If the autonomous feedback knowledge information mobile support Web service system suggested in this article is applied, the flexible service search is possible and the service by situation can be provided to the user. In addition, a customized mobile support service can be provided without increasing the complexity of the system design compared with the traditional service search method.

Bag of Patterns for Nearest Neighbour Neonatal EEG Recall.

Clinical neurophysiologists often find it difficult to recall rare EEG patterns despite the fact that this information could be diagnostic and help with treatment intervention. Traditional search methods may take time to retrieve the archived EEGs that could provide the meaning or cause of the specific pattern, which is undesirable as time can be critical for sick neonates. If neurophysiologists had the ability to quickly recall similar patterns, the prior occurrence of the pattern may help make an earlier diagnosis. This paper presents a system that may be used to assist a clinical neurophysiologist in the recall of neonatal EEG patterns. This paper compares two brute force approaches for the task of neonatal EEG recall and looks at the performance accuracy, speed and memory requirements. This system was tested on six different neonatal EEG pattern types with 430 events in total and the results are presented in this paper.

Global Evolution Commended by Localized Search for Unconstrained Single Objective Optimization

Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large steps while searching a local area. Thus, DE is not a good option for solving local optimization problems. On the other hand, there are traditional local search (LS) methods, such as Steepest Decent and Davidon–Fletcher–Powell (DFP) that are good at local searching, but poor in searching global regions. Hence, motivated by the short comings of existing search techniques, we propose a hybrid algorithm of a DE version, reflected adaptive differential evolution with two external archives (RJADE/TA) with DFP to benefit from both search techniques and to alleviate their search disadvantages. In the novel hybrid design, the initial population is explored by global optimizer, RJADE/TA, and then a few comparatively best solutions are shifted to the archive and refined there by DFP. Thus, both kinds of searches, global and local, are incorporated alternatively. Furthermore, a population minimization approach is also proposed. At each call of DFP, the population is decreased. The algorithm starts with a maximum population and ends up with a minimum. The proposed technique was tested on a test suite of 28 complex functions selected from literature to evaluate its merit. The results achieved demonstrate that DE complemented with LS can further enhance the performance of RJADE/TA.

Spread-It: A Strategic Game of Competitive Diffusion Through Social Networks

Diffusion of information is a key factor in many social and political situations. This paper presents a strategic game called Spread-It, which models the spread of information through social network structures. In the game, two competing parties must decide on the allocation and timing of their limited resources with the goal of increasing their influence in the network. Since present decisions affect the future level of network penetration, their effort allocation must be carefully planned. The work starts by defining the mathematical characteristics of the game, followed by analytical results derived by implementing several strategies for winning the game. Analyzing the experiments provides few observations regarding the role of influencers (“hubs”) under different game conditions, the superiority of Monte Carlo tree search strategy over traditional game-tree search methods, and the budget required to guarantee the game's finalization.

Enhancing evidence-based medicine skills in oncology training with cognitive technology.

10532 Background: Evidence-based medicine (EBM) requires applying literature evidence to inform practice. Students from Taipei Medical University Hospital, trained in EBM concepts, participated in a preliminary study using Watson for Oncology (WfO), an evidence-based decision-support system to enhance the EBM skills of medical students. Methods: A class of 50 medical students compared traditional search methods (TSM) and WfO in a workshop divided into 2 sequential sessions on colon and lung cancer, respectively. All students were trained on WfO, and 2 groups of 25 students each were randomly assigned to either TSM or WfO in the first session. Those groups were then assigned to the alternate approach in the second session. Students completed a profile that included their clinical experience with each cancer type. Students used either WfO or TSM to help answer a series of questions related to colon or lung cancer. Students then completed a survey of attitudes towards the technology, followed by a constructed-response learning assessment without the aid of TSM or WfO. Assessments were scored and results compared using a Mann-Whitney U Test; outcomes at two different experience levels, based on student profiles, were compared using a Kruskal-Wallis test. Results: In this preliminary study, more than 70% of students reported limited clinical experience with either cancer. On the colon cancer assessment, students in the WfO group performed significantly better than the TSM group ( p = 0.0001); there was no significant difference detected for lung cancer. Students with more clinical experience felt that TSM was easier to learn than WfO ( p= 0.005); students with less experience felt that WfO was clearer and more understandable than TSM ( p= 0.002). Conclusions: These preliminary results are consistent with better learning outcomes for students using WfO in the colon cancer module. Students with more clinic experience reported that TSM was easier to learn than WfO, however it is unknown if this might be due to a potentially greater familiarity with TSM in this more experienced group. More studies are needed to determine what features, if any, of WfO can facilitate EBM approaches in oncology education.