Application of Artificial Intelligence in an Unsupervised Algorithm for Trajectory Segmentation Based on Multiple Motion Features

With the development of the wireless network, location-based services (e.g., the place of interest recommendation) play a crucial role in daily life. However, the data acquired is noisy, massive, it is difficult to mine it by artificial intelligence algorithm. One of the fundamental problems of trajectory knowledge discovery is trajectory segmentation. Reasonable segmentation can reduce computing resources and improvement of storage effectiveness. In this work, we propose an unsupervised algorithm for trajectory segmentation based on multiple motion features (TS-MF). The proposed algorithm consists of two steps: segmentation and mergence. The segmentation part uses the Pearson coefficient to measure the similarity of adjacent trajectory points and extract the segmentation points from a global perspective. The merging part optimizes the minimum description length (MDL) value by merging local sub-trajectories, which can avoid excessive segmentation and improve the accuracy of trajectory segmentation. To demonstrate the effectiveness of the proposed algorithm, experiments are conducted on two real datasets. Evaluations of the algorithm’s performance in comparison with the state-of-the-art indicate the proposed method achieves the highest harmonic average of purity and coverage.

Textual-based DSL for Conceptual Database Modeling: A Controlled Experiment

The variety of database system technologies that have become available in recent years makes it difficult to select tools for entity-relationship modeling (ER) in the teaching-learning context. This paper reports a replicated controlled experiment carried out with 33 subjects in order to compare effort spent (time) and quality, using the harmonic average between precision and recall, of the models produced with two different approaches. The models were produced in a proposed tool (ERtext) with a textual-based DSL and in another tool with a graphical approach (brModelo). Briefly, the data obtained indicate: i) both approaches present similar performance in relation to associated effort, and; ii) that there is a statistically significant difference in relation to the quality of the generated models, with a slightly advantage for the textual approach. Therefore, we conclude that the use of a textual-based DSL is feasible and our proposal is an acceptable solution in the context of conceptual database modeling.

The Possible use of Equivalent Homogeneous Subsoil Models for 1D Seismic Response Analyses in Seismic Microzonation Studies

The possibility is here explored to use an ‘equivalent’ homogeneous configuration to simulate 1D seismic response of heterogeneous engineering-geological bodies when relatively weak seismic impedance contrasts (150 m/s) only exist above the seismic bedrock. This equivalent configuration is obtained by considering an equivalent Vs value the harmonic average of the actual Vs values and a linear combination of G/G 0 and D curves relative to the lithotechnical components present in the actual configuration. To evaluate feasibility of this approach, a wide set of numerical simulations was carried out by randomly generating subsoil layering including sequences of alternating thin layers of geotechnical units ( e.g., sands and clays) each characterized by a characteristic nonlinear curve. Outcomes of these simulations are compared with those provided by considering a single homogeneous layer characterized by equivalent nonlinear curves obtained as a weighted average of the original curves. By comparing the heterogeneous and the homogeneous columns seismic response in terms of amplification factors and fundamental period, the results confirm the possibility to model a 1D column characterized by a generic lithostratigraphic succession with an equivalent one without introducing significative errors that, at least for the studied cases, do not exceed the 6%. This conclusion is substantially confirmed by extending the comparison to a real case, i.e. the 113 m-thick heterogeneous soil profile at Mirandola site (Norther Italy), presented in the last part.

Weight Feedback-Based Harmonic MDG-Ensemble Model for Prediction of Traffic Accident Severity

Traffic accidents are emerging as a serious social problem in modern society but if the severity of an accident is quickly grasped, countermeasures can be organized efficiently. To solve this problem, the method proposed in this paper derives the MDG (Mean Decrease Gini) coefficient between variables to assess the severity of traffic accidents. Single models are designed to use coefficient, independent variables to determine and predict accident severity. The generated single models are fused using a weighted-voting-based bagging method ensemble to consider various characteristics and avoid overfitting. The variables used for predicting accidents are classified as dependent or independent and the variables that affect the severity of traffic accidents are predicted using the characteristics of causal relationships. Independent variables are classified as categorical and numerical variables. For this reason, a problem arises when the variation among dependent variables is imbalanced. Therefore, a harmonic average is applied to the weights to maintain the variables’ balance and determine the average rate of change. Through this, it is possible to establish objective criteria for determining the severity of traffic accidents, thereby improving reliability.

Decision fusion method for fault diagnosis based on closeness and Dempster-Shafer theory

Decision fusion is an effective way to resolve the conflict of diagnosis results. Aiming at the problem that Dempster-Shafer (DS) theory deals with the high conflict of evidence and produces wrong results, a decision fusion algorithm for fault diagnosis based on closeness and DS theory is proposed. Firstly, the relevant concepts of DS theory are introduced, and the normal distribution membership function is used as the evidence closeness. Secondly, the harmonic average is introduced, and the weight of each evidence is established according to the product of closeness of each evidence and its harmonic average. Thirdly, the weight of conflicting evidence is regularized, and the final decision fusion result is obtained by using the Dempster’s rule. Lastly, the simulation and application examples are designed. Simulation and application results show that the method can effectively reduce the impact of diagnostic information conflicts and improve the accuracy of decision fusion. What’s more, the method considers the overall average distribution of evidence in the identification framework, it can reduce evidence conflicts while preserving important diagnostic information.

Ukuran Gejala Data Belum Dikelompokan (Studi Kasus : Jumlah Warga Yang Meninggal Akibat Terjangkit Virus Covid-19 di Kota Depok Per-oktober 2020)

The statistic is a method developed for analyzing, analyzing, and compiling sample data to get the right data. Also, observation is needed to get accurate and concrete data. Various kinds of methods can be used to obtain the data, one of which is the Symptom Symptoms Data Center is the symptom data which is divided into two, namely the symptom center symptom data grouped and the data center symptom grouped. This journal will explain in detail the size of Symptoms in unclassified data centers Symptom Measurement of Unclassified Data Centers or also Symptom Size Single grouped data centers are data that are not arranged in a frequency distribution, so there are no category intervals and category midpoints. Symptom measurement data centers have not been grouped namely the calculated average (mean), measuring / geometric mean, harmonic average, tertiary average, median, mode, and fractile (quartile, decile, percentile). Measurement can use Microsoft Excel and SPSS applications

Application of Machine Learning Techniques in Short-term Travel Time Prediction Using Multiple Data Sources

Having access to accurate travel time is of great importance for both highway network users and traffic engineers. The travel time which is currently reported on several highways is estimated by employing naïve methods and using limited sources of data. This results in unreliable and inaccurate travel time prediction and could impose delay on travelers. Therefore, the main objective of this study is short-term prediction of travel time for highways using multiple data sources including loop detectors, probe vehicles, weather condition, network, accidents, road works, and special events in order to consider the effect of different factors on travel time. To this end, two machine learning methods, K-Nearest Neighbors and Random Forest, are employed. After applying data cleaning process on datasets and combining them, the models are trained to predict and compare short-term harmonic average speed as a representative of travel time for 5-minute prediction horizons in one hour ahead. The travel time is calculated as the ratio of the length of each link and the harmonic average speed for all reporting vehicles. Hence, a model is trained for each technique to predict travel time 5 minutes ahead, 10 minutes ahead, and all the way down to 60 minutes ahead. The results confirm satisfying performance of both models in short-term travel time prediction with slightly outperformance of Random Forest model. A feature importance and sensitivity analysis also applied for the Random Forest model, and traffic variables are found as the most effective variables in predicting the travel time.

Fuzzy Information Retrieval Based on Continuous Bag-of-Words Model

In this paper, we study the feasibility of performing fuzzy information retrieval by word embedding. We propose a fuzzy information retrieval approach to capture the relationships between words and query language, which combines some techniques of deep learning and fuzzy set theory. We try to leverage large scale data and the continuous-bag-of words model to find the relevant feature of words and obtain word embedding. To enhance retrieval effectiveness, we measure the relativity among words by word embedding, with the property of symmetry. Experimental results show that the recall ratio, precision ratio, and harmonic average of two ratios of the proposed method outperforms the ones of the traditional methods.

Каскадная генерация второй гармоники с половинным порядком периодической ориентации

We theoretically demonstrate cascaded fourth and second harmonic generation in a periodically-poled nonlinear crystal with a half-order of phase-matching period. We consider a cascaded process in which four photons of the fundamental harmonic firstly convert into an intermediate photon of the fourth harmonic, which parametrically decays into two photons of the second harmonic at the second stage. Phase-matching for this cascaded nonlinear conversion is provided by using an asymmetric periodical poling. We use the quantum spatial Heisenberg equations to describe light propagation and conversion inside the nonlinear crystal. With this approach, we calculate second and fourth harmonic average number of photons as a function of the nonlinear crystal length.

Decoding team and individual impact in science and invention

Scientists and inventors increasingly work in teams, raising fundamental questions about the nature of team production and making individual assessment increasingly difficult. Here we present a method for describing individual and team citation impact that both is computationally feasible and can be applied in standard, wide-scale databases. We track individuals across collaboration networks to define an individual citation index and examine outcomes when each individual works alone or in teams. Studying 24 million research articles and 3.9 million US patents, we find a substantial impact advantage of teamwork over solo work. However, this advantage declines as differences between the team members’ individual citation indices grow. Team impact is predicted more by the lower-citation rather than the higher-citation team members, typically centering near the harmonic average of the individual citation indices. Consistent with this finding, teams tend to assemble among individuals with similar citation impact in all fields of science and patenting. In assessing individuals, our index, which accounts for each coauthor, is shown to have substantial advantages over existing measures. First, it more accurately predicts out-of-sample paper and patent outcomes. Second, it more accurately characterizes which scholars are elected to the National Academy of Sciences. Overall, the methodology uncovers universal regularities that inform team organization while also providing a tool for individual evaluation in the team production era.