Similarity Measure Method Research Articles

Evidence-based data mining method to reveal similarities between materials based on physical mechanisms

Measuring the similarity between materials is essential for estimating their properties and revealing the associated physical mechanisms. However, current methods for measuring the similarity between materials rely on theoretically derived descriptors and parameters fitted from experimental or computational data, which are often insufficient and biased. Furthermore, outliers and data generated by multiple mechanisms are usually included in the dataset, making the data-driven approach challenging and mathematically complicated. To overcome such issues, we apply the Dempster–Shafer theory to develop an evidential regression-based similarity measurement (eRSM) method, which can rationally transform data into evidence. It then combines such evidence to conclude the similarities between materials, considering their physical properties. To evaluate the eRSM, we used two material datasets, including 3d transition metal–4f rare-earth binary and quaternary high-entropy alloys with target properties, Curie temperature, and magnetization. Based on the information obtained on the similarities between the materials, a clustering technique is applied to learn the cluster structures of the materials that facilitate the interpretation of the mechanism. The unsupervised learning experiments demonstrate that the obtained similarities are applicable to detect anomalies and appropriately identify groups of materials whose properties correlate differently with their compositions. Furthermore, significant improvements in the accuracies of the predictions for the Curie temperature and magnetization of the quaternary alloys are obtained by introducing the similarities, with the reduction in mean absolute errors of 36% and 18%, respectively. The results show that the eRSM can adequately measure the similarities and dissimilarities between materials in these datasets with respect to mechanisms of the target properties.

A Similarity-based Time Series Source Dataset Selection Method for Transfer Learning

Source datasets are selected with high similarity to target datasets to improve the effect of transfer learning. The low similarity between the source and target datasets may lead to negative transfer. This paper proposes a similarity-based time series source dataset selection method for transfer learning. First, to reduce the complexity of the operation, we use Dynamic time-warped barycentric averaging to obtain the prototype signals of each dataset and convert the dataset similarity calculation into the similarity calculation between the prototype signals. The dynamic time warping (DTW) algorithm, commonly used to calculate time-series signal similarity, does not consider the effect of signal complexity and the amount of data the dataset contains on the signal similarity calculation. This article makes improvements to the above issues. Experiments are carried out on 20-time series datasets archived by the University of California Riverside. Experimental results show that the average transfer effect of source datasets selected by the proposed method is better than the commonly used dataset similarity measurement method based on DTW.

A novel system for discovery and reuse of typical process route based on information entropy and PSO-Kmeans clustering algorithm

Manufacturing enterprises will accumulate a large number of manufacturing instances as they run and develop. Being able to excavate and reuse the instance resources reasonably is one of the most effective ways to improve manufacturing and support innovation. To determine the reuse object scientifically and raise the reuse flexibility, a novel system for discovery and reuse of typical process route based on the information entropy and PSO-Kmeans clustering algorithm is proposed in this paper. In this system, a similarity measurement method of machining process routes based on the information entropy of multistage longest common subsequence is developed. Then a discovery method of typical process route based on the spectral clustering idea and PSO-Kmeans clustering algorithm is invented, and the two reuse approaches based on the typical process route are analyzed and discussed. Finally, the three case studies are rendered and the results reveal that the proposed system can provide better support for manufacture instance reuse.

RETRACTED: An unsupervised anomaly detection approach for pre-seismic ionospheric total electron content

This paper focuses on the anomaly detection for ionospheric total electron content (TEC) before earthquakes. In this paper, a novel unsupervised approach is proposed. First, interval-based method is employed to granulate the TEC series. Justifiable granularity principle is utilized to construct interval information granules (IGs) for representing TEC series. Second, high-order difference method is introduced to construct rectangle IGs and cube IGs for obtaining the new representation of TEC. Third, corresponding similarity measurement method is designed to calculate the anomaly score of each IG, which is the evaluation criterion for detecting the anomalies. Finally, experimental results using real TEC datasets validate the effectiveness of the proposed approach. Compared with the existing major approaches, because the proposed approach can capture more morphological details and variation trend of TEC series, it can achieve a higher detection accuracy.

A transfer learning framework for well placement optimization based on denoising autoencoder

Well placement optimization is directly related to the recovery factor of reservoir development, and at present, the mainstream solution is an evolutionary algorithm. However, time-consuming numerical simulators need to be called to evaluate each alternative well placement scheme. Since the rules of well placement problems are universal, similar reservoirs will have similar well locations. Thus, knowledge transfer across similar well placement optimization tasks can expedite searching effectively. To this end, this paper proposes a novel transfer learning framework for well placement optimization to extract the potential well placement rules based on the feature extraction capability of a single-layer denoising autoencoder. The reconstruction mapping between the previous and present tasks is established to make the randomly generated well locations inherit the knowledge from the optimal well locations of the previous task, which helps the search direction of the evolutionary algorithm quickly bias to the optimal solution, thus, the solving of present task can be accelerated. The simplified denoising autoencoder holds a closed-form solution after derivation of the loss function, and the corresponding reuse of knowledge will not bring much additional computational burden on the evolutionary search. In addition, a similarity measure method between well placement optimization tasks is proposed to avoid a negative transfer. At last, comprehensive experiments on benchmark functions and well placement optimization instances are presented to evaluate the effectiveness of the proposed framework.

Non-Co-Occurrence Enhanced Multi-Label Cross-Modal Hashing Retrieval Based on Graph Convolutional Network

Supervised cross-modal retrieval has significant advantages in retrieval efficiency and storage cost. In the field of hashing retrieval, existing supervised methods are divided into single-label and multi-label methods. For the single-label method, simply using a single label to measure the semantic relevance between instances will cause an error in supervision information. However, the existing multi-label hashing methods also have some problems. For example, only considering the co-occurrence of multiple labels among instances may not accurately reflect their similarity. At the same time, in the previous methods, the text modality processing did not reach the fine level of image modality, resulting in insufficient use of text information. We proposed Non-co-occurrence enhanced Multi-label cross-modal hashing retrieval based on Graph Convolutional Network (MHGCN) to address these issues. Firstly, we introduced a multi-label non-co-occurrence similarity measurement method, which adds multi-label non-co-occurrence information among instances in the multi-label similarity measurement to measure the differences between instances; Secondly, we used Graph Convolutional Networks (GCNS) to process the information on text modality; Thirdly, we introduced the memory mechanism to restrict the difference of hash code learning. Many experiments on three commonly used benchmark datasets show that the proposed Non-co-occurrence enhanced Multi-label cross-modal hashing retrieval based on Graph Convolutional Network (MHGCN) method has excellent performance.

Remaining Useful Life Prediction for Circuit Breaker Based on SM-CFE and SA-BiLSTM

Targeting at the uncertainty of the degradation of conventional circuit breakers (CCBs) and the perfect mechanical degradation characterization by vibration signals, a remaining useful life (RUL) prediction method based on similarity measure (SM), comprehensive features extraction (CFE) and self-attention bidirectional long short-term memory network (SA-BiLSTM) is proposed in this paper. Firstly, the degradation-sensitive intrinsic mode functions (IMFs) of the vibration signals are separated by the SM method, and the reconstructed signals are divided into intervals based on the working process of CCBs to obtain the vibration signals for life prediction. Secondly, the explicit parametric features (EPFs) are obtained based on the vibration events and waveform features, and the implicit parametric features (IPFs) are extracted using the multi-scale convolutional auto-encoder (MSCAE). Next, the comprehensive features are obtained by combination of the EPFs and IPFs. On this basis, the degradation mode judgment method is formed by using the least square method (LSM) linear fitting of the EPFs. Finally, a quantitative life prediction model based on SA-BiLSTM is constructed, and greater weights are assigned to the important time steps. The proposed method is proved to show high prediction accuracy and good stability, which is more advantageous compared with other hybrid prediction models.

Neuromorphic Similarity Measurement of Tactile Stimuli in Human–Machine Interface

The interpretation of tactile stimuli empowers humans to identify substances, distinguish materials, and engage in tactile communication. For stimulus design in human-computer interaction, objective similarity measures improve efficiency and save costs. Inspired by the fact that biological systems are robust in recognizing multimedia stimuli, we propose a neuromorphic method for similarity measurement. The method is divided into two steps. First, tactile information is translated into biological representations by mimicking a low-threshold mechanoreceptor through a physiological neuronal model. Then, three measures are nominated to assess the similarity of neural spike trains from the following perspectives: interval spike counting, temporal matching, and vector space embedding. Regression analysis showed that the linearity of these measures was significant, indicating that the filtering ability of the physiological neuron model is robust. One of the measures is selected for comparison with the signal-to-noise ratio, structural similarity, and hybrid metric. The results suggest that the correlation between the predictions of our method and the subjective evaluation is stable, above 0.9 for each experimental stimulus. We achieve a mutual interpretation between quantitative measures of vibrotactile similarity and subjective cognitive outcomes. Furthermore, the feasibility of this method in material classification has been substantiated through an exploratory experiment.

A New Ion Concentration Detection Method Based on Spectral Matching and Fusion for Zinc Leaching Liquid

The suspended micro particles (SMP) in the zinc leaching liquid will cause the scattering of detection light, which will make the spectral data of the liquid rise nonlinearly, leading to the difficulty of accurate detection of ion concentration by ultraviolet-visible spectroscopy (UV-Vis). Therefore, based on UV-Vis, a new ion concentration detection method based on spectral matching and fusion is proposed to weaken the scattering interference of SMP from the perspective of the data model. The method includes the improvement of spectrometer structure, spectral matching, spectral fusion, and prediction of the calibration model. By improving the structure of the spectrometer, the spectral curves of different positions of the leaching liquid were obtained. To accurately discriminate and match multiple spectral curves, a similarity measurement method integrating the differences in amplitude, shape, and information between spectra was proposed, named spectral amplitude, shape, and information differences measure method (SASIM). To effectively use the spectral bands’ information screened by SASIM, a spectral data level fusion method was proposed. Finally, a partial least square regression model was established to predict the cobalt ion concentration of the zinc-leaching liquid. The experimental results show that SASIM has stronger spectral discrimination power (40.59%) and smaller spectral discrimination entropy (3.426). The proposed detection method obtains better root mean square error (RMSE) (0.563) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> (0.858), which proves this method can improve the detection accuracy of ion concentration in the leaching liquid. This also provides a new idea for the direct detection of water quality parameters.

Secure Content Based Image Retrieval Scheme Based on Deep Hashing and Searchable Encryption

To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security, retrieval efficiency, and retrieval accuracy. This research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure, searchable encryption scheme. First, a deep learning framework based on residual network and transfer learning model is designed to extract more representative image deep features. Secondly, the central similarity is used to quantify and construct the deep hash sequence of features. The Paillier homomorphic encryption encrypts the deep hash sequence to build a high-security and low-complexity searchable index. Finally, according to the additive homomorphic property of Paillier homomorphic encryption, a similarity measurement method suitable for computing in the retrieval system’s security is ensured by the encrypted domain. The experimental results, which were obtained on Web Image Database from the National University of Singapore (NUS-WIDE), Microsoft Common Objects in Context (MS COCO), and ImageNet data sets, demonstrate the system’s robust security and precise retrieval, the proposed scheme can achieve efficient image retrieval without revealing user privacy. The retrieval accuracy is improved by at least 37% compared to traditional hashing schemes. At the same time, the retrieval time is saved by at least 9.7% compared to the latest deep hashing schemes.

Development of data-driven performance benchmarking methodology for a large number of bus air conditioners

Bus air conditioners (ACs) are responsible for providing a comfortable cabin environment for passengers. Identifying the bus ACs with degraded performance from a large number of city buses is a critical and challenging task in the development of smart cities. This study developed a data-driven benchmarking methodology to detect anomalous operations with degraded energy performance from a large number of bus ACs. For each target AC to be benchmarked, its similar operation data in other ACs, termed comparable peer samples, are first identified by a Long-Short-Term-Memory (LSTM) autoencoder-based similarity measurement method. The comparable peer samples are then used to develop a LSTM network-based reference model for predicting the power consumption of the target AC. A key energy performance indicator termed power consumption ratio (PCR) is defined for the target AC as the ratio of its measured power to the predicted power. Statistical analysis-based trend and change detection algorithms are designed to identify a trend or change of PCR over a few days for anomalous detection. To validate the benchmarking methodology, two fault experiments were conducted in field-operating bus ACs, and the results show encouraging potentials of the proposed methodology for health monitoring of a large number of ACs serving the city bus fleet.

A pairwise functional connectivity similarity measure method based on few-shot learning for early MCI detection.

Alzheimer's disease is an irreversible neurological disease, therefore prompt diagnosis during its early stage, i.e., early mild cognitive impairment (MCI), is crucial for effective treatment. In this paper, we propose an automatic diagnosis method, a few-shot learning-based pairwise functional connectivity (FC) similarity measure method, to detect early MCI. We first employ a sliding window strategy to generate a dynamic functional connectivity network (FCN) using each subject's rs-fMRI data. Then, normal controls (NCs) and early MCI patients are distinguished by measuring the similarity between the dynamic FC series of corresponding brain regions of interest (ROIs) pairs in different subjects. However, previous studies have shown that FC patterns in different ROI-pairs contribute differently to disease classification. To enable the FCs of different ROI-pairs to make corresponding contributions to disease classification, we adopt a self-attention mechanism to weight the FC features. We evaluated the suggested strategy using rs-fMRI data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the results point to the viability of our approach for detecting MCI at an early stage.

INVESTIGATING DIFFERENT SIMILARITY METRICS USED IN VARIOUS RECOMMENDER SYSTEMS TYPES: SCENARIO CASES

Abstract. A recommendation system represents a very efficient way to propose solutions adapted to customers needs. It allows users to discover interesting items from a large amount of data according to their preferences. To do this, it uses a similarity metric, which determines how similar two users or products are. In the case of recommender systems, similarity computation is a practical step. The calculation of similarity may be used for both items and users. Following the similarity calculation, a user or item with a comparable computation value can be recommended together with the goods to a user with similar preferences. The user’s requirements influence the choice of similarity metric. This paper explores various similarity measurement methods employed in recommender systems. We compare correlation and distance techniques to determine the capabilities of different similitude calculation algorithms and synthesize which similarity measure is adapted for which type of recommendation.

A Multi-Channel Descriptor for LiDAR-Based Loop Closure Detection and Its Application

Simultaneous localization and mapping (SLAM) algorithm is a prerequisite for unmanned ground vehicle (UGV) localization, path planning, and navigation, which includes two essential components: frontend odometry and backend optimization. Frontend odometry tends to amplify the cumulative error continuously, leading to ghosting and drifting on the mapping results. However, loop closure detection (LCD) can be used to address this technical issue by significantly eliminating the cumulative error. The existing LCD methods decide whether a loop exists by constructing local or global descriptors and calculating the similarity between descriptors, which attaches great importance to the design of discriminative descriptors and effective similarity measurement mechanisms. In this paper, we first propose novel multi-channel descriptors (CMCD) to alleviate the lack of point cloud single information in the discriminative power of scene description. The distance, height, and intensity information of the point cloud is encoded into three independent channels of the shadow-casting region (bin) and then compressed it into a two-dimensional global descriptor. Next, an ORB-based dynamic threshold feature extraction algorithm (DTORB) is designed using objective 2D descriptors to describe the distributions of global and local point clouds. Then, a DTORB-based similarity measurement method is designed using the rotation-invariance and visualization characteristic of descriptor features to overcome the subjective tendency of the constant threshold ORB algorithm in descriptor feature extraction. Finally, verification is performed over KITTI odometry sequences and the campus datasets of Jilin University collected by us. The experimental results demonstrate the superior performance of our method to the state-of-the-art approaches.

Correlation measures under single-valued neutrosophic hesitant fuzzy sets environment for multi-criteria decision-making problems

We examine correlation coefficients for single-valued neutrosophic hesitant fuzzy sets (SVNHFSs) to point out their questionable results for the ideal alternative. Then, we propose three similarity measure methods to solve multi-criteria decision-making (MCDM) problems. Three applications, namely, ranking of alternatives, dysfunctional comments of turbine engine generators, and disease diagnoses for patients, illustrate the stability and effectivity of our new similarity. Our findings will help researchers deal with similarity measures in the future.

Exploring the spatiotemporal characteristics of intracity travels by time series clustering: A case study of Beijing

AbstractExploring spatiotemporal characteristics of residents' travel is a fundamental task in the field of urban studies. Time series clustering is one of the useful methods to distinguish those characteristics of intracity travel. However, previous studies focused more on exploring the spatiotemporal characteristics in urban centers. For understanding the spatiotemporal characteristics in suburbs, we combined the Shape Based Distance (SBD) based on the Discrete Fourier Transformation (DFT) similarity measurement method and the Affinity Propagation (AP) clustering method to cluster time series of each Traffic Analyze Zone (TAZ). The method performed well both in urban centers and suburbs. There are nine clusters on weekdays and seven clusters on weekends. These clusters reflect different travel characteristics. To further understand the difference between these travel characteristics, Points of Interest (POI) data were employed to analyze attractors of travel. Work‐oriented, Residence‐oriented and Attraction‐oriented are three main attractors on weekdays. Meanwhile, there are four attractors on weekends which are Recreation‐oriented, Residence‐oriented, Attraction‐oriented and Work‐oriented. This research has a certain theoretical and practical value for understanding urban formation evolution and urban planning.

An Efficient Service Recommendation Algorithm for Cyber-Physical-Social Systems

Cyber-physical-social System (CPSS) technology closely integrates and coordinates computing resources, physical resources, and social information to provide humans with efficient and convenient services. Service recommendation technology satisfies the individual needs of users by collecting, processing, and analyzing user characteristic data. In recent years, service recommendation algorithms based on social networks and collaborative filtering have been widely used in CPSS. However, this type of service recommendation algorithm cannot effectively use the aliasing and auxiliary information in the service, nor can it generate different service recommendation schemes according to different scenarios. We propose a collaborative filtering service recommendation algorithm that combines heterogeneous information networks and topic models for CPSS. The proposed algorithm designs a more effective functional similarity measurement method through the word vectors learned by Word2vec. Then, we use the topic model to cluster the scenario-based service (SBS), thereby greatly reducing the time required for service recommendation. Experimental analysis results show that we finally determine the best combination of Mashup similarity between the two scenarios with better recommendation efficiency and accuracy. Thus, the proposed recommendation algorithm can effectively improve the quality of service of CPSS, and provide a basis for the personalized service of CPSS.

A New Feature-Based Method for Similarity Measurement under the Linux Operating System

This paper presents a new algorithm in an important research field which is the semantic word similarity estimation. A new feature-based algorithm is proposed for measuring the word semantic similarity for the Arabic language. It is a highly systematic language where its words exhibit elegant and rigorous logic. The score of sematic similarity between two Arabic words is calculated as a function of their common and total taxonomical features. An Arabic knowledge source is employed for extracting the taxonomical features as a set of all concepts that subsumed the concepts containing the compared words. The previously developed Arabic word benchmark datasets are used for optimizing and evaluating the proposed algorithm. In this paper, the performance of the new feature-based algorithm is compared against the performance of seven ontology-based algorithms adapted to Arabic. The results of the evaluation and comparison experiments show that the new proposed algorithm outperforms the adapted word similarity algorithms on the Arabic word benchmark dataset. The proposed algorithm will be included in the AWN-similarity which is free open-source software for Arabic.

Information Granulation With Rectangular Information Granules and Its Application in Time-Series Similarity Measurement

Information granules can discover interpretable and meaningful relationships offering a full description for time series. This article presents an information granulation method with rectangular information granules and applies it to time-series similarity measurement. First, the fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> -means clustering algorithm transforms the time series and its first-order difference time series to data clusters. With the maximum volume of rectangular information granules viewed as the criterion, the optimal rectangular information granules are formed using the data cluster by the principle of justifiable granularity and the gravitational search algorithm. The time-series similarity is measured by calculating the similarity between the upper and lower bounds of the optimal rectangular information granules built from the time series. Finally, an experiment is performed on a public dataset to verify the feasibility of the proposed method. The result shows that the rectangular information granulation method can capture the change characteristics of time series. The similarity measurement method can effectively evaluate the similarity of the time series belonging to different classes.

Fuzzy multi-attribute decision making method based on new similarity measure under interval-valued neutrosophic sets

Interval-valued neutrosophic set (IVNS) plays an important role in dealing with imprecise judgment information. For a multi-attribute decision making problem, the information of alternatives under different attributes is given in the form of interval valued neutrosophic number(IVNN). The objective of the presented paper is to develop a multiple-attribute decision making (MADM) method under interval-valued neutrosophic sets(IVNSs) using the new similarity measurement. The similarity measurement of IVNSs has always been a research hotspot. A new similarity measurement of IVNSs is first proposed in this paper based on Chebyshev distance. The proposed method enriches the existing similarity measurement methods. It can be applied to not only IVNSs, but also single-valued neutrosophic sets(SVNSs). The influence of each attribute on the decision-making result can be described by the weight. How to formulate the weight scientifically is vital as well. In this paper, the objective weight is calculated by normalizing the grey correlation coefficient obtained by a score function which can be applied to IVNSs. The objective weight is then combined with the subjective one by considering an adjustment factor with the weighted summation method. The adjustment factor is determined by the importance of subjective weight. Finally, an example is used to illustrate the comparison results of the proposed algorithm and other three ones. The comparison shows that the proposed algorithm is effective and can identify the optimal scheme quickly.