Movement Prediction Research Articles

Failure Detector Based on Vehicle Movement Prediction in Vehicular Ad-Hoc Networks

Vehicular ad hoc network (VANETs) improves road safety and efficiency by organizing vehicles and infrastructure to provide a platform for application deployment. The availability of vehicles and infrastructure is critical to the operation of applications. Accurate failure detector (FD) has been one of the fundamental components for maintaining high availability in VANETs. However, it is hard to find the vehicle failure accurately and timely due to the dynamic nature of VANETs caused by the high mobility of vehicles and communications link failures. Therefore, it is important to achieve an accurate FD which can cope with the high mobility of VANETs. In this paper, we propose a dead reckoning based FD, called DR-FD. It can predict the mobility of vehicle accurately and avoid the impact of link failures on the detection results by the cooperation between vehicles. Experimental results are provided to confirm that the proposed DR-FD method can achieve at most 20% reduction in detection time, 30% improvement in mistake rate and 20% improvement in overhead.

IPH2O: Island Parallel-Harris Hawks Optimizer-Based CLSTM for Stock Price Movement Prediction

IPH2O: Island Parallel-Harris Hawks Optimizer-Based CLSTM for Stock Price Movement Prediction

Stock Price Movement Prediction based on Relation Type guided Graph Convolutional Network

Stock Price Movement Prediction (SPMP) task tends to predict the future price fluctuation of some stocks, which is challenging because of the volatile nature of financial markets. Recent researches widely introduce Graph Convolutional Network (GCN) and achieve some competitive performances for SPMP. However, their works usually only extract the signal representations of individual stock in the phase of feature extraction whereas they ignore the real-time interactions among companies along the signal channels. In addition, when the financial relations among different entities (i.e., companies and executives) participate in the aggregation of GCN during feature encoding, the recent advances fail to consider the dynamics of the relation type representations and the iterative update between entities and relations. To address this problem, we propose a novel method consisting of an External Attention (EA) module and a Relation Type guided Graph Convolutional Network (RTGCN) for SPMP. In detail, to achieve the real-time interactions among companies along the signal channels, we introduce an external attention mechanism to share the company memory of the price and sentiment signals. Moreover, the proposed RTGCN considers the dynamics of the relation type embeddings and achieves iterative update between entity nodes and relation edges in the graph. The experiments on two open-source datasets (i.e., CSI100E and CSI300E) demonstrate that the proposed SPMP method can achieve state-of-the-art performances and can assuredly bring benefits for the performance improvement of SPMP.

Prediction of Ground Movements and Impacts on Adjacent Buildings Due to Inclined–Vertical Framed Retaining Wall-Retained Excavations

Inclined–vertical framed retaining walls were recently developed and successfully adopted in engineering practice. However, relevant empirical methods for the evaluation of ground movements due to excavation have not been established. This study aimed to establish a calculation method for assessing ground movement due to excavation and to evaluate the damage probability of nearby buildings. A series of numerical models were carried out, and a database was established with their results. In this paper, simplified equations are proposed for predicting the profiles of subsurface and surface settlements, as well as lateral movements, utilizing the database. The accuracy of the simplified equations was validated by monitoring data from three case histories of the inclined–vertical framed retaining wall. Finally, the damage potential index was used to obtain the probability of damage to buildings located outside the excavations, while considering different positions.

How to set safety boundary in virtual reality: A dynamic approach based on user motion prediction

AbstractVirtual reality (VR) interaction safety is a prerequisite for all user activities in the virtual environment. While seeking a deep sense of immersion with little concern about surrounding obstacles, users may have limited ability to perceive the real‐world space, resulting in possible collisions with real‐world objects. Nowadays, recent works and rendering techniques such as the Chaperone can provide safety boundaries to users but confines them in a small static space and lack of immediacy. To solve this problem, we propose a dynamic approach based on user motion prediction named SCARF, which uses Spearman's correlation analysis, rule learning, and few‐shot learning to achieve prediction of user movements in specific VR tasks. Specifically, we study the relationship between user characteristics, human motion, and categories of VR tasks and provides an approach that uses biomechanical analysis to define the interaction space in VR dynamically.We report on a user study with 58 volunteers and establish a three dimensional kinematic dataset from a VR game. The experiments validate that our few‐shot learning model is effective and can improve the performance of motion prediction. Finally, we implement SCARF in VR environment for dynamic safety boundary adjustment.

Modified surgery-first approach for management of severe class III malocclusion with maxillary skeletal midline discrepancy

This case report describes orthosurgical case that used the recently introduced surgery-first approach to correct a severe skeletal class III malocclusion with severe maxillary midline discrepancy. A 23-year-old adult male presented with severe mandibular prognathism and midline discrepancy associated with upper jaw. Firstly partial orthodontic movement were facilitated to decrowd the lower teeth in order to avoid any hindrance while anterior tooth advancement. After which prediction of the postsurgical tooth movement and surgical simulation was done by using Nemoceph software than 2-jaw surgery that included maxillary advancement differentially initiated to correct midline followed by mandibular setback was performed using a surgery-first approach. Immediate facial improvement was achieved and postsurgical orthodontic treatment was efficiently carried out. The total treatment time was 11 months. The patient's facial appearance improved significantly and a stable surgical orthodontic outcome was obtained even after patient was followed for six years.

A fast matching method for skip displacement recognition of hoisting system

Determining if there is any coal left in the coal mine skip unloading is a challenging problem. We propose a new object tracking method to provide new ideas to solve the problem of whether there is residual coal in the skip unloading. In this study, a fast vision-based measurement framework about new diamond search normalized cross-correlation (NDS-NCC), including a movement prediction part and a template tracking part, is used. Firstly, a diamond search method with fast adaptive coarse localization is proposed to overcome the time-consuming problem of NCC template matching process. Secondly, we incorporate a template updating strategies that allows targets to be tracked consistently. The performance of the NDS-NCC algorithm was tested on the bench and at the coal mine site for balancing cylinders and wire rope tracking. The results show that the NDS-NCC algorithm has better efficiency (7 ms/frame) and accuracy (0.4512 px) in the coal mine field application.

AE-ACG: A novel deep learning-based method for stock price movement prediction

This paper proposes a method named AE-ACG for stock price movement prediction. In AE-ACG, the convolutional neural network (CNN) and gated recurrent unit (GRU) are combined to design a base layer, which is embedded in the autoencoder (AE) framework, to efficiently extract features from financial time series data. Furthermore, skip connection links encoding and decoding to leverage hierarchical features. Attention mechanism (AM) also distinguishes the importance of historical data across periods. Extensive experiments demonstrated that the proposed model is effective in predicting price movements, showing advantages over some mainstream methods.

SEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters.

For assistive devices such as active orthoses, exoskeletons or other close-to-body robotic-systems, the immediate prediction of biological limb movements based on biosignals in the respective control system can be used to enable intuitive operation also by untrained users e.g. in healthcare, rehabilitation or industrial scenarios. Surface electromyography (sEMG) signals from the muscles that drive the limbs can be measured before the actual movement occurs and, hence, can be used as source for predicting limb movements. The aim of this work was to create a model that can be adapted to a new user or movement scenario with little measurement and computing effort. Therefore, a biomechanical model is presented that predicts limb movements of the human forearm based on easy to measure sEMG signals of the main muscles involved in forearm actuation (lateral and long head of triceps and short and long head of biceps). The model has 42 internal parameters of which 37 were attributed to 8 individually measured physiological measures (location of acromion at the shoulder, medial/lateral epicondyles as well as olecranon at the elbow, and styloid processes of radius/ulna at the wrist; maximum muscle forces of biceps and triceps). The remaining 5 parameters are adapted to specific movement conditions in an optimization process. The model was tested in an experimental study with 31 subjects in which the prediction quality of the model was assessed. The quality of the movement prediction was evaluated by using the normalized mean absolute error (nMAE) for two arm postures (lower, upper), two load conditions (2 kg, 4 kg) and two movement velocities (slow, fast). For the resulting 8 experimental combinations the nMAE varied between nMAE = 0.16 and nMAE = 0.21 (lower numbers better). An additional quality score (QS) was introduced that allows direct comparison between different movements. This score ranged from QS = 0.25 to QS = 0.40 (higher numbers better) for the experimental combinations. The above formulated aim was achieved with good prediction quality by using only 8 individual measurements (easy to collect body dimensions) and the subsequent optimization of only 5 parameters. At the same time, just easily accessible sEMG measurement locations are used to enable simple integration, e.g. in exoskeletons. This biomechanical model does not compete with models that measure all sEMG signals of the muscle heads involved in order to achieve the highest possible prediction quality.

The influence of stereopsis on visual saliency in a proto-object based model of selective attention

Some animals including humans use stereoscopic vision which reconstructs spatial information about the environment from the disparity between images captured by eyes in two separate adjacent locations. Like other sensory information, such stereoscopic information is expected to influence attentional selection. We develop a biologically plausible model of binocular vision to study its effect on bottom-up visual attention, i.e., visual saliency. In our model, the scene is organized in terms of proto-objects on which attention acts, rather than on unbound sets of elementary features. We show that taking into account the stereoscopic information improves the performance of the model in the prediction of human eye movements with statistically significant differences.

Associating deep learning and the news headlines sentiment for Bursa stock price prediction

Accurate stock price prediction is appealing to academics, economists, and financial analysts for its potential to increase profits. Although remarkable progress has been made in stock prediction accuracy, studies to explore the relationship between public sentiments and the prediction of stock price movement based on online news portals in Malaysia context are limited. Therefore, this study aims to determine whether news sentiments influence the movement of the Bursa stock price. The stock prediction model was implemented using long short-term memory (LSTM), with stock data from Bursa Malaysia between January 2017 and April 2022, and the root mean squared error (RMSE) value was calculated. In addition, LSTM prediction model was compared to the decision tree algorithm, and LSTM performed significantly better than the decision tree, particularly when using the New York stock exchange (NYSE) dataset. Furthermore, sentiment analysis was carried out using a Malaysian online news portal's business and financial news. The findings showed that i) news has a significant impact on Malaysian stock market price movement; ii) the RMSE of the LSTM model was improved by adding a parameter (news polarity values); and iii) the RMSE value generated is less than one for every company stock and is influenced by stock price and price change magnitude.

Effects of excavation and construction sequence on behavior of existing pile groups

In congested urban areas, new structures are regularly constructed next to high-rise buildings. At different stages of excavation for the new structures, settlement and tilting of the adjacent piled buildings may be occurred. Thus, attention here will be focused on the interaction behavior between deep excavation and adjacent piled foundations. Accurate prediction of soil movement and pile response plays an important role in preventing damages to adjacent buildings. Finite-element models are employed to compute the pile group responses. The parametric study has been performed by varying pile group configurations, center-to-center pile spacing, and new building load. Two, four, and six capped head pile groups next to excavation sites have been examined. In this paper, discussion centers on settlement and tilting of the pile groups nearby an excavation in fully saturated sand. The influence of pile group configuration and new building loads are systematically investigated. New building can be constructed in the excavated area after completion of the excavation. The effect of new building construction sequences is also investigated. Results provide confirmatory evidence that, excavation depth has the most significant effect on pile settlement and pile cap tilting. High pile cap tilting and small pile group settlement are apparent in case of two piles near the excavation. For the four pile group, the induced tilting of pile cap decreases as spacing between the piles increases. Due to construction of the new building, the settlement is exacerbated but the tilting is moderated.

Stock Market Analysis and Prediction using Machine Learning Algorithm

Abstract: The prediction of a stock market direction serves as an unseasonable recommendation of system for shortterm investors and before time financial distress warning system for long-term shareholders. Forecasting accuracy is the most important part in selecting any forecasting technique. Research efforts in upgrading the accuracy of forecasting models are increasing from the last decade. The appropriate stock selections those are fit for investment is a challenging task. The key factor for each investor is to be paid maximum profits on their investments. In this paper Support Vector Machine Algorithm (SVM) is handed down. SVM is a very specific kind of learning algorithms characterized by the volume control of the decision function, the work of the kernel functions and the scarcity of the compound. In this paper, we investigate the predictability of economic movement with SVM

Orthodontic Practitioners’ Knowledge and Education Demand on Clear Aligner Therapy

ObjectivesClear aligner therapy (CAT) has been gaining popularity amongst the orthodontic community. No systematic course on CAT has been reported to date. The objectives of this study were to determine practitioners’ knowledge and to offer insights for future tailored courses on CAT. MethodsAn online questionnaire was distributed. The questionnaire comprised personal background information, predictability of tooth movement through CAT, and CAT knowledge that practitioners demanded to learn. Four senior expert orthodontists’ answers to the predictability of tooth movement through CAT were averaged to be a standard reference. Descriptive statistics, 1-way analysis of variance (ANOVA), principal component analysis, Student t test, and multivariate logistics regression analysis were performed with significance set at P < .05. ResultsIn total, 190 practitioners participated in this study. As compared to the standard reference, participants overestimated the predictability of difficult-to-be-achived tooth movements (eg, molar mesialisation; P < .0001). Strategy of managing troubleshooting cases and extraction cases ranked the highest CAT knowledge that participants requested to learn. Practice type, number of completed CAT cases, number of undergoing CAT cases, years of practice, education background, and time of using CAT were the influencing factors of the questions regarding the predictability of tooth movement through CAT and CAT knowledge that were demanded to be learned. ConclusionsThe predictability of difficult-to-be-achieved tooth movement through CAT is often overestimated by practitioners with limited clinical experience. Tailored education on CAT, especially managing troubleshooting cases and extraction cases, should be designed for all practitioners.

A simulation study on the insurance claims distribution using Weibull distribution

&lt;p style="text-align:justify"&gt;&lt;span style="font-size:10.5pt"&gt;&lt;span style="font-family:等线"&gt;&lt;span dir="ltr" lang="EN-US" style="font-family:&amp;quot;Cambria&amp;quot;,serif"&gt;&lt;span style="color:black"&gt;The Weibull distribution is extensively useful in the field of finance, insurance and natural disasters. Recently, It has been considered as one of the most frequently used statistical distributions in modelling and analyzing stock pricing movement and uncertain prediction in financial and investment data sets, such as insurance claims distribution. It is well known that the Bayes estimators of the two-parameter Weibull distribution do not have a compact form and the closed-form expression of the Bayes estimators cannot be obtained. In this paper and the Bayesian setting, it is assumed that the scale parameter of the Weibull model has a gamma prior under the assumption that its shape parameter is known. A simulation study is performed using random claims amount to compare the performance of the Bayesian approach with traditional maximum likelihood estimators in terms of Root Mean Square Errors (RMSE) and Mean Absolute Error (MAE) for different sample sizes, with specific values of the scale parameter and shape parameters. The results have been compared with the estimated result via the maximum likelihood method. The result revealed that the Bayesian approach behaves similarly to the maximum likelihood method when the sample size is small. Nevertheless, in all cases for both methods, the RMSE and MAE decrease as the sample size increases. Finally, applications of the proposed model to the insurance claim data set have been presented.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

Deep RNN Based Prediction of Driver’s Intended Movements at Intersection Using Cooperative Awareness Messages

This paper presents an early prediction framework to classify drivers’ intended intersection movements in a connected vehicle environment. Intersections are considered accident blackspots with major traffic violations that cause property damage, injuries and fatalities. An accurate perception of drivers’ intended movements at intersections is required for advanced red-light (ARLW) or turning warnings for vulnerable road users (TWVR). Early prediction of intersection movement and adequate warning assistance will ensure road users’ safety at the intersection. In this study, we adopted recurrent neural networks (RNN): long short-term memory (LSTM) and gated recurrent units (GRU) networks to predict driver intended movements at intersections using the vehicle kinematics extracted from the Cooperative Awareness Messages (CAMs). We used naturalistic driving data of the Ipswich Connected Vehicle Pilot (ICVP) project, Queensland, which was collected from 351 participants who drove their connected vehicles during the pilot period. The pilot study installed roadside equipment at 29 signalised intersections to enable the Cooperative Intelligent Transportation System (C-ITS) use cases. Vehicle speed, speed limit, longitudinal acceleration, lateral acceleration, and yaw rate are used as predictors and monitored in 100-millisecond intervals for 1s to 4s at different warning distances from the stop line. Separate prediction models are trained based on different monitoring windows. Furthermore, drivers’ intended intersection movements are predicted at two individual intersections to evaluate intersection-specific prediction performance and are found with improved prediction accuracy than overall prediction models trained with all 29 intersections data. Overall prediction models are useful for some intersections which lack available data for individual intersection-based prediction.

Predicting extreme events in the stock market using generative adversarial networks

Accurately predicting extreme stock market fluctuations at the right time will allow traders and investors to make better-informed investment decisions and practice more efficient financial risk management. However, extreme stock market events are particularly hard to model because of their scarce and erratic nature. Moreover, strong trading strategies, market stress tests, and portfolio optimization largely rely on sound data. While the application of generative adversarial networks (GANs) for stock forecasting has been an active area of research, there is still a gap in the literature on using GANs for extreme market movement prediction and simulation. In this study, we proposed a framework based on GANs to efficiently model stock prices’ extreme movements. By creating synthetic real-looking data, the framework simulated multiple possible market-evolution scenarios, which can be used to improve the forecasting quality of future market variations. The fidelity and predictive power of the generated data were tested by quantitative and qualitative metrics. Our experimental results on S&amp;P 500 and five emerging market stock data show that the proposed framework is capable of producing a realistic time series by recovering important properties from real data. The results presented in this work suggest that the underlying dynamics of extreme stock market variations can be captured efficiently by some state-of-the-art GAN architectures. This conclusion has great practical implications for investors, traders, and corporations willing to anticipate the future trends of their financial assets. The proposed framework can be used as a simulation tool to mimic stock market behaviors.

Prediction of stock price movement using an improved NSGA-II-RF algorithm with a three-stage feature engineering process.

Prediction of stock price has been a hot topic in artificial intelligence field. Computational intelligent methods such as machine learning or deep learning are explored in the prediction system in recent years. However, making accurate predictions of stock price direction is still a big challenge because stock prices are affected by nonlinear, nonstationary, and high dimensional features. In previous works, feature engineering was overlooked. How to select the optimal feature sets that affect stock price is a prominent solution. Hence, our motivation for this article is to propose an improved many-objective optimization algorithm integrating random forest (I-NSGA-II-RF) algorithm with a three-stage feature engineering process in order to decrease the computational complexity and improve the accuracy of prediction system. Maximizing accuracy and minimizing the optimal solution set are the optimization directions of the model in this study. The integrated information initialization population of two filtered feature selection methods is used to optimize the I-NSGA-II algorithm, using multiple chromosome hybrid coding to synchronously select features and optimize model parameters. Finally, the selected feature subset and parameters are input to the RF for training, prediction, and iterative optimization. Experimental results show that the I-NSGA-II-RF algorithm has the highest average accuracy, the smallest optimal solution set, and the shortest running time compared to the unmodified multi-objective feature selection algorithm and the single target feature selection algorithm. Compared to the deep learning model, this model has interpretability, higher accuracy, and less running time.

Long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention.

Propose: Contrast-enhanced ultrasound has shown great promises for diagnosis and monitoring in a wide range of clinical conditions. Meanwhile, to obtain accurate and effective location of lesion in contrast-enhanced ultrasound videos is the basis for subsequent diagnosis and qualitative treatment, which is a challenging task nowadays. Methods: We propose to upgrade a siamese architecture-based neural network for robust and accurate landmark tracking in contrast-enhanced ultrasound videos. Due to few researches on it, the general inherent assumptions of the constant position model and the missing motion model remain unaddressed limitations. In our proposed model, we overcome these limitations by introducing two modules into the original architecture. We use a temporal motion attention based on Lucas Kanade optic flow and Karman filter to model the regular movement and better instruct location prediction. Moreover, we design a pipeline of template update to ensure timely adaptation to feature changes. Results: Eventually, the whole framework was performed on our collected datasets. It has achieved the average mean IoU values of 86.43% on 33 labeled videos with a total of 37,549 frames. In terms of tracking stability, our model has smaller TE of 19.2 pixels and RMSE of 27.6 with the FPS of 8.36 ± 3.23 compared to other classical tracking models. Conclusion: We designed and implemented a pipeline for tracking focal areas in contrast-enhanced ultrasound videos, which takes the siamese network as the backbone and uses optical flow and Kalman filter algorithm to provide position prior information. It turns out that these two additional modules are helpful for the analysis of CEUS videos. We hope that our work can provide an idea for the analysis of CEUS videos.

GuideRender: large-scale scene navigation based on multi-modal view frustum movement prediction

GuideRender: large-scale scene navigation based on multi-modal view frustum movement prediction