Intelligent Search Method Research Articles

Prediction of stable structure and unique charge transfer in Li–Pt intermetallic compounds under pressure

The exploration of new intermetallic compounds is of great significance for basic research and practical application. There is a huge electronegativity difference between Li and Pt, and the metals exhibit interesting and diverse properties; however, the structural behavior of Li–Pt intermetallics with different ratios under high pressure has not been systematically studied. In this study, an intelligent structure search method based on a particle swarm optimization algorithm combined with first-principles calculations was used to extensively explore the stable structures and unique conditions governing charge transfer in Li–Pt intermetallic compounds under different pressures. In addition to reproducing the known LiPt (P6‾m2) and Li2Pt (P6/mmm), the simulation was consistent with the experimental results. New phases were also found by calculation: LiPt3(Cmmm), Li2Pt (P3‾m1), and Li4Pt (I4/m) at ambient pressure; Li3Pt (Fm3‾m), Li4Pt (R3‾m), and Li5Pt (P6/mmm) at 10 GPa; and Li5Pt (P3‾m1) at 20 GPa. Bader charge analysis and electron localization function (ELF) mapping showed that the transition metal (Pt) atoms exhibit unusual oxidation states both under ambient and high pressure, and more electrons were localized on Pt as the Li content increased. The highest negative valence state was approximately −4. Intermetallic compounds LiPt3 (Cmmm) and Li2Pt (P6/mmm) were prepared successfully by arc melting furnace. The reliability of structure prediction method and pseudo potential selection was verified. This work demonstrates that tuning the pressure and stoichiometry is an effective means of forming novel, stable intermetallic compounds.

A cross-view intelligent person search method based on multi-feature constraints

ABSTRACT Person searches aim to simultaneously locate and identify persons to be queried from different scenes, which is crucial for disaster emergency management and public safety. However, the high variability of environmental features in different video scenes, along with the susceptibility of people searching for occlusions or dense populations, results in existing methods suffering from inefficiency and poor accuracy in searching for cross-view persons. Therefore, we propose a cross-view intelligent person search method based on multifeature constraints. First, we establish the global-local context-aware (GLCA) module, which fully extracts the differential personnel features. Second, we construct the semantic complementarity and feature aggregation (SCFA) module for personnel-scale feature constraints in different contexts. Third, we constrain the method in terms of person spatial, person identity, and detection confidence features to improve person search accuracy. Finally, we construct a case experiment dataset, select two public benchmark datasets, and conduct a detailed experimental analysis based on them. The results show that our method can be applied to personnel search tasks in complex scenarios well, and the search results outperform those of 25 other state-of-the-art algorithms, with mAPs improved by 0.41%−19.71%. This approach effectively enhances the informatization level of disaster emergency rescue and public safety management.

An approach for low-power heterogeneous parallel implementation of ALC-PSO algorithm using OmpSs and CUDA

PSO (particle swarm optimization), is an intelligent search method for finding the best solution according to population state. Various parallel implementations of this algorithm have been presented for intensive-computing applications. The ALC-PSO algorithm (PSO with an aging leader and challengers) is an improved population-based procedure that increases convergence rapidity, compared to the traditional PSO. In this paper, we propose a low-power heterogeneous parallel implementation of ALC-PSO algorithm using OmpSs and CUDA, for execution on both CPU and GPU cores. This is the first effort to heterogeneous parallel implementing ALC-PSO algorithm with combination of OmpSs and CUDA. This hybrid parallel programming approach increases the performance and efficiency of the intensive-computing applications. The proposed approach of this article is also useful and applicable for heterogeneous parallel execution of the other improved versions of PSO algorithm, on both CPUs and GPUs. The results demonstrate that the proposed approach provides higher performance, in terms of delay and power consumption, than the existence implementations of ALC-PSO algorithm.

Application of Big Data Technology in the Study of “UGS” Collaborative Training Mechanism for Musicology Majors in Colleges and Universities in the Context of Professional Accreditation

Abstract The traditional education model gradually reveals the corresponding problems and deficiencies in the face of the update of the concept of education frontier and the change of talent cultivation mode. In this paper, we first sorted out the relevant features of the big data and data mining process, mined out the specific needs and realistic development dilemmas of musicology professional certification, and explored the appropriate mechanisms and strategies of UGS collaborative education and training. Using big data Hadoop technology as the infrastructure, a platform for music discipline services is designed to support interoperability and sharing of music education resources among universities, governments, and primary and secondary schools. Specifically, semantic similarity calculation is performed with music resource domain ontology to improve the intelligent retrieval ability, and the recommendation method of user dynamic preference is introduced to provide more accurate music teaching resources for platform users. The effect of cultivating musicology majors in colleges and universities is analyzed in terms of emotional attitudes, knowledge, and skills through the collaborative cultivation mechanism. The accuracy of the semantic similarity intelligent search method based on domain ontology is above 50%, and the mean value of students’ satisfaction with the intelligently recommended music learning resources is 4.3 points. 83.42% of the teaching carried out based on the subject service platform indicated that it met their learning needs, and the overall performance of the students who carried out the platform teaching was 16 points higher than that of the traditional classes. By using big data technology, the UGS collaborative cultivation mechanism can be utilized as a tool to enhance the quality of talent cultivation for musicology majors.

Strength and manufacturability enhancement of a composite automotive component via an integrated finite element/artificial neural network multi-objective optimization approach

This study addresses the enhancement of an injection-molded fiber-reinforced plastic / metal hybrid automotive structure and its plastic injection molding process through the integration of the finite element method, artificial intelligence and evolutionary search methods. Experiments are conducted to validate the finite element models. The orthogonal array and Latin hypercube methods are employed to generate a database via finite element analysis. The database is then used to train artificial neural networks that accurately evaluate component distortion, manufacturing time, and structural strength. A genetic optimization algorithm is applied to identify optimal process parameters. The procedure was demonstrated to simultaneously reduce product warpage and manufacturing time by 10 and 62 %, respectively, when compared with the reference manufacturing process while strength is kept above the required levels with a reduced number of required data points. A more in-depth investigation into the causes of strength variation and deformation is also provided. The results contribute to the advance of robust composite automotive structures with superior quality, manufactured through efficient processes.

Fault diagnosis method for hydro-power plants with Bi-LSTM knowledge graph aided by attention scheme

In hydro-power systems, the fault of equipment is an important potential threat for the safe production of electricity. Therefore, the automation and intelligence of fault diagnosis becomes the popular issue in the research on hydro-power system. In this paper, a knowledge graph-based method is put forth to diagnose faults occurred in hydro-power systems, since the knowledge graph can store structured and unstructured data for better fault diagnosis and intelligently search the reasons of the faults. First, we model the knowledge graph for hydro-power plants, where the rational path for the fault reason is formulated. Then, the bi-directional long short-term memory (Bi-LSTM) with conditional random field (CRF) is used to extract the entities and relations to the given documents, which record the phenomenon and reasons for the occurred faults. Moreover, the attention scheme is employed in the Bi-LSTM to weigh the closer relationships to improve the diagnosis accuracy. An automatic diagnosis algorithm is developed to improve the diagnosing efficiency by constructing rational paths, with which directive and in-directive factors for occurring faults can be traced. Simulation results reveal that the intelligent search method with a knowledge graph can effectively find the reason, locate the position, and provide useful suggestions for the occurred faults.

Collection and classification of influence parameters for safety effectiveness of ADAS

Virtual scenario-based testing has become an acceptable method for evaluating safety effectiveness of advanced driver assistance systems (ADAS). Due to the complexity of the ADAS operating environment, the scenarios that an ADAS could face are almost infinite. Therefore, it is crucial to find critical scenarios to improve the efficiency of testing without compromising credibility. One popular method is to explore the parameterized scenario space using various intelligent search methods. Selecting parameters to parameterize the scenario space is particularly important to achieve good coverage and high efficiency. However, an extensive collection of (relevant) influence parameters is missing, which allows a thorough consideration when selecting parameters regarding specific scenarios. In addition, the general importance definition for individual influence parameters is not provided, regarding the potential influence of their variations on the safety effectiveness of ADAS, which can also be used as a reference while selecting parameters. Combining knowledge from different sources (the published literature, standardized test scenarios, accident analysis, autonomous vehicle disengagement, accident reports, and specific online surveys), this paper has summarized, in total, 94 influence parameters, given the general definitions of importance for 77 influence parameters based on cluster analysis algorithms. The list of influence parameters provides researchers and system developers a comprehensive basis for pre-selecting influence parameters for evaluating the safety effectiveness of ADAS by virtual scenario-based testing and helps check whether certain influence parameters can be a meaningful extension for the evaluation.

Active control of structures using genetic algorithm with dynamic weighting factors using in the constrained objective function

Active control method by improving specification of well-known intelligent numerical search method i.e. genetic algorithm is developed here. This method reduces displacement of the structure by optimizing the control forces at each time step. The efficiency of the genetic algorithm as a part of nature-inspired metaheuristic methods is highly dependent on the constrained objective function. The constrained objective function is achieved by combining the constraints of the optimization problem. There are several methods to numerically combine these constraints. Using appropriate weighting factors to generate this function has been suggested by many researchers. In previous studies, the selection of these factors has been based on experimental or try and error methods and were constant throughout the control period. Proper selection of weighting factors increases the efficiency of the control method. Presenting a new genetic algorithm method in a way selecting weighting factors dynamically over the structural control period is the aim of this paper. Here, weighting factors are non-static in nature and are dynamically selected at each time step according to the memory of the previous step. Numerical results clearly prove the accuracy and efficiency of the proposed control process in comparison with Constant weighting factors methods.

Leveraging genetic algorithm to address multi-failure localization in optical networks

Fault management has long been an indispensable component for controlling and managing telecommunication networks. To prevent huge data losses, it is necessary to develop a fast and efficient fault localization mechanism. In this work, we study the problem of multi-failure localization in transparent optical networks. In this context, a correlation-based approach is introduced to exploit the quality of transmission of acquired lightpaths to localize the faulty links. The proposed search-based framework can be implemented by leveraging any search algorithm. One may utilize the exhaustive search method to localize faulty links more accurately, but at the cost of taking more time. On the other hand, one may utilize intelligent search methods with the aim of reducing the required time for localization at the expense of accuracy. However, we propose to use both of the search approaches together. In this way, faulty links are first localized by the intelligent search methods to reroute and restore the failed traffic as fast as possible to prevent further loss of data. To this aim, a genetic algorithm (GA) is proposed to search among the suspected links. Subsequently, exhaustive search method can be utilized to localize failures more accurately without time constraint and then send technicians to the right site to recover the faulty links. The obtained results reveal that the proposed GA approach achieves overall high localization accuracy (98.6%–100%) that is insignificantly affected as the traffic load decreases. Dual and triple-failure incidents are localized within 42–80 ms and 596–2180 ms, respectively. It is shown that the mean time required for localizing failures using the GA search algorithm is significantly lower than exhaustive search approach by several orders of magnitude. Hence, the proposed GA-based fault localization algorithm can reduce the average time required to restore the traffic in case of failures, applicable for the restoration applications.

The Application of AI-Based Technology in Computer Network Operation and Maintenance

As an emerging IT technology, artificial intelligence has unique advantages in data extraction and data processing. In this paper, an artificial intelligence search method based on the K-means algorithm is proposed. According to the similarity between data samples, the samples are divided into many different categories. The ARIMA algorithm is used to perform regression analysis on the traffic data, and a Boosting model is established to improve the time series prediction accuracy. And, we use the relative distance between elements to represent the degree of dissimilarity of different types of variables. It can process massive data in a short time and obtain user behavior information. A large number of experiments show that the method proposed in this paper can complete information search more effectively and complete information classification quickly.

Artificial Electric Field Algorithm-Pattern Search for Many-Criteria Networks Reconfiguration Considering Power Quality and Energy Not Supplied

Considering different objectives and using powerful optimization methods in the distribution networks reconfiguration by accurately achieving the best network configuration can further improve network performance. In this paper, reconfiguration of radial distribution networks is performed to minimize the power loss, voltage sag, voltage unbalance, and energy not supplied (ENS) of customers using a new intelligent artificial electric field algorithm-pattern search (AEFAPS) method based on the many-criteria optimization approach. The voltage sag and voltage unbalance are defined as power quality indices and the ENS is the reliability index. In this study, the pattern search (PS) algorithm enhances the artificial electric field algorithm’s (AEFA) flexibility search both globally and locally. AEFAPS is applied to determine the decision variables as open switches of the networks considering the objective function and operational constraints. The proposed methodology based on AEFAPS is performed on an unbalanced 33-bus IEEE standard network and a real unbalanced 13-bus network. The reconfiguration problem is implemented in single-criterion and many-criteria optimization approaches to evaluate the proposed methodology’s effectiveness using different algorithms. The single-criterion results demonstrated that some power quality indices might be out of range, while all indices are within the permitted range in the many-criteria optimization approach, proving the effectiveness of the proposed many-criteria reconfiguration with logical compromise between different objectives. The results show that AEFAPS identified the network configuration optimally and different objectives are improved considerably compared to the base network. The results confirmed the superior capability of AEFAPS to obtain better objective values and lower values of losses, voltage sag, voltage unbalance, and ENS compared with conventional AEFA, particle swarm optimization (PSO), and grey wolf optimizer (GWO). Moreover, the better performance of AEFAPS is proved in solving the reconfiguration problem compared with previous studies.

Intelligent train operation based on deep learning from excellent driver manipulation patterns

In the application of deep learning to realize intelligent train operation, there are some problems, such as the single learning task. Especially when using the gradient descent approach to optimize the structure, weight and threshold of a deep network, it is easy in this task to fall into a local optimum. This leads to excessive reliance on manual tuning experience. Aiming at the above issues, this paper proposes a new approach of train manipulation and prediction based on a long short-term memory (LSTM) deep network. From the perspective of automatic hyper-parameter optimization, the gradient-free intelligent search method is principally chosen to optimize the architecture and parameters of a LSTM deep network, so as to improve the manipulation accuracy based on learning from excellent drivers. This method first selects excellent driver data through the Pareto dominance principle and crowding distance calculation; on this basis, a step-by-step method is used to optimize the structure, weight and threshold of the LSTM network. Particularly, in the first step, we adopt a genetic algorithm to search for the optimal deep network structure, which overcomes the problem that the structure is difficult to determine. In the second step, we optimize the parameters of the deep network, a process that is divided into two stages of ‘rough learning’ and ‘precise learning’. In the ‘rough learning’ stage, we use the multi-population chained multi-agent (MPCMA) algorithm to preliminarily optimize the LSTM network parameters. In the ‘precise learning’ stage, the Adam algorithm is applied to further finely optimize the network parameters. Finally, through simulation experiments, it is verified that the proposed method improves the accuracy of train manipulation and prediction, and shows strong robustness in situations of multiple manipulation sequences and different temporary speed limits.

Reachability checking in complex and concurrent software systems using intelligent search methods

Reachability checking in complex and concurrent software systems using intelligent search methods

Adequacy Evaluation of Composite Power Systems Using an Evolutionary Swarm Algorithm

The generation and transmission capacities of many power systems in the world are significantly increasing due to the escalating global electricity demand. Therefore, the adequacy evaluation of power systems has become a computationally challenging and time-consuming task. Recently, population-based intelligent search methods such as Genetic Algorithms (GAs) and Binary Particle Swarm Optimization (BPSO) have been successfully employed for evaluating the adequacy of power generation systems. In this work, the authors propose a novel Evolutionary Swarm Algorithm (ESA) for the adequacy evaluation of composite generation and transmission systems. The random search guiding mechanism of the ESA is based on the underlying philosophies of GAs and BPSO. The main objective of the ESA is to find out the most probable system failure states that significantly affect the adequacy of composite systems. The identified system failure states can be directly used to estimate the system adequacy indices. The proposed ESA-based framework is used to evaluate the adequacy of the IEEE Reliability Test System (RTS). The estimated annualized and annual adequacy indices such as Probability of Load Curtailments (PLC), Expected Duration of Load Curtailments (EDLC), Expected Energy Not Supplied (EENS) and Expected Frequency of Load Curtailments (EFLC) are compared with those obtained using Sequential Monte Carlo Simulation (SMCS), GA and BPSO. The results show that the accuracy, computational efficiency, convergence characteristics, and precision of the ESA outperform those of GA and BPSO. Moreover, compared to SMCS, the ESA can estimate the adequacy indices in a more time-efficient manner.

Waveguide-integrated digital metamaterials for wavelength, mode and polarization demultiplexing

In this paper, we present a general design scheme for waveguide-integrated demultiplexing devices based on intelligent topology search method combining density model and gradient-based method of moving asymptotes. 2D finite element method is adopted to simulate and evaluate the performance of demultiplexers with quasi-3D configuration by setting equivalent refractive index of silicon. The obtained pixelated digital metamaterials individually fulfill functionalities including 1550nm/1310nm and 1500nm/1600nm wavelength demultiplexing, TE0/TE1 mode demultiplexing at 1550 nm, and TE0/TM0 polarization demultiplexing at 1550 nm. The footprint of the functional region is as small as 1.55 μm × 1.55 μm. To validate our designs with SOI configuration, 3D models are simulated by finite difference time domain. Comparable and satisfactory performances of the target functionalities are demonstrated. For all four demultiplexers, the worst transmission efficiency is 44.5% of TE0 in polarization demultiplexing, the worst contrast ratio is 14.1 dB of TE1 in mode demultiplexing and the worst crosstalk is −15.1 dB of TE0 in mode demultiplexing. Within an ultra-compact footprint, this design scheme can efficiently enable the device to separate or route photons based on specific dimension, such as wavelength, mode and polarization.

Effective rural electrification via optimal network: Optimal path-finding in highly anisotropic search space using multiplier-accelerated A* algorithm

United Nations’ 7th Sustainable Development Goal envisions the availability of modern energy for everyone by 2030. While the progress has been satisfactory in the last few years, further rural electrification is increasingly challenging. The current mainstream approach of electrifying villages individually is becoming cost-ineffective due to uncertainties in both resource availability and energy demand for small, difficult-to-reach, residences. A networked rural electrification model, i.e. a cost-optimized network connecting villages and generation facilities, could improve resources utilization, reliability and flexibility. However, determining optimal paths with common search algorithms is extremely inefficient due to complex topographic features of rural areas. This work develops and applies an artificial intelligence search method to efficiently route inter-village power connections in the common rural electrification situation where substantial topological variations exist. The method is evolved from the canonical A* algorithm. Results compare favorably with optimal A* results, at significantly reduced computational effort. Furthermore, users can adaptively trade-off between computation speed and optimality and hence quickly evaluate sites and configurations at reasonable accuracy, which is impossible with classical methods.

New adaptive intelligent grey wolf optimizer based multi-objective quantitative classification rules mining approaches

The classification rule mining problem is one of the most important tasks of data mining. A constructed classification model is needed to have high accuracy, comprehensiveness, interestingness, etc. Furthermore, when the data composed of quantitative, numerical, or mixed data types, automatically discovering the appropriate intervals at the time of the mining process is a hard problem. The standard classification algorithms in the literature do not find the intervals of the quantitative attributes in the rules and this is performed a priori that causes the modification of datasets. Automatically constructing a successful classification model consisting of an explainable rule set without changing or modifying the data in artificial intelligence and machine learning is a very hot topic. Due to the philosophy of constantly researching to discover more accurate, surprising, and comprehensible rule sets and the absence of the most effective algorithm for all kinds of data sets, new methods or new versions of existing methods are proposed. In this study, automatic mining of high-quality rule set is handled as a multi-objective optimization problem due to the nature of the necessities and novel adaptive multi-objective intelligent search and optimization algorithms based on Grey Wolf Optimizer are proposed for this task. The datasets are considered as search spaces and the proposed adaptive Pareto based multi-objective Grey Wolf Optimizer algorithms are designed and applied as search methods for automatically discovering the high-quality rules. The proposed intelligent methods and successful classification algorithms such as naive Bayes, k-NN, support vector machines, decision trees, and RIPPER are tested in five real-world data sets with different characteristics. The obtained results show the efficiency of the proposed intelligent search and optimization methods.

Study on intelligent traffic search method based on driver facial feature analysis

With the rapid development of Internet technology and biometrics technology in China, artificial intelligence has gradually entered into every aspect of people's life. Big data is used to upgrade intelligent traffic search for people, which improves the efficiency and accuracy of people search for people. Intelligent traffic search is a hotspot in the field of biometric identification and plays an important role in social stability. As an important feature, driver's face image can not only provide great help to the detection of illegal vehicles, but also help to carry out the tracing of missing people, so as to maintain social harmony and stability. Therefore, the intelligent traffic search method based on the analysis of driver's facial features has a broad application prospect and research value. This paper investigates the current international top facial recognition algorithm technology level, and proposes a face image illumination invariant feature extraction algorithm and face feature detection ASM algorithm. The experimental results show that the intelligent traffic search method in this paper has a good recognition rate, and the study in this paper also has a certain guiding significance for the application of image processing in the field of intelligent traffic.

Study on intelligent traffic search method based on driver facial feature analysis

With the rapid development of Internet technology and biometrics technology in China, artificial intelligence has gradually entered into every aspect of people's life. Big data is used to upgrade intelligent traffic search for people, which improves the efficiency and accuracy of people search for people. Intelligent traffic search is a hotspot in the field of biometric identification and plays an important role in social stability. As an important feature, driver's face image can not only provide great help to the detection of illegal vehicles, but also help to carry out the tracing of missing people, so as to maintain social harmony and stability. Therefore, the intelligent traffic search method based on the analysis of driver's facial features has a broad application prospect and research value. This paper investigates the current international top facial recognition algorithm technology level, and proposes a face image illumination invariant feature extraction algorithm and face feature detection ASM algorithm. The experimental results show that the intelligent traffic search method in this paper has a good recognition rate, and the study in this paper also has a certain guiding significance for the application of image processing in the field of intelligent traffic.

Improved gradient descent algorithms for time-delay rational state-space systems: intelligent search method and momentum method

This study proposes two improved gradient descent parameter estimation algorithms for rational state-space models with time-delay. These two algorithms, based on intelligent search method and momentum method, can simultaneously estimate the time-delay and parameters without the matrix eigenvalue calculation in each iteration. Compared with the traditional gradient descent algorithm, the improved algorithms come with two advantages: having quicker convergence rates and less computational efforts, particularly meaningful for those large-scale systems. A simulated example is selected to illustrate the efficiency of the proposed algorithms.