Prediction-based Approach Research Articles

High dynamic performance based by sliding mod control of quadrotor MAV

The paper focuses on strong control of high dynamic performance based on the sliding mode in order to control the quadrotor MAV. The model used in the simulation is the Newton-Euler model, and also choose the Mambo Parrot quadrotor parameters, to improve the latter's performance in terms of accuracy and robustness in case the impedance control chosen by the manufacturer needs to be changed. The results of which gave effectiveness and efficiency compared to classical controls PID and sliding mode. High dynamic performance control (HDP) is a type of control system designed to provide the highest level of performance over a wide range of operating conditions. This control is not a regulator but an organizing technique. It uses a prediction-based approach to anticipate rapid variations in system states and disturbances and to generate real-time commands to correct tracking errors and maintain system stability. This approach allows HDP control to achieve high performance while maintaining system stability, even under harsh operating conditions.

A dynamic electric fence planning framework for dockless bike-sharing systems based on inventory prediction

While dockless bike-sharing systems are growing in popularity due to their convenience, indiscriminate parking creates disorder in cities. To address this problem, this paper proposes a novel prediction-based approach for planning dynamic electric fences. These fences can adapt to dynamic usage patterns and efficiently guide parking behavior. First, graded clustering and spatial point integration algorithms are used to determine the locations of electric fence candidates. Second, initial parking and scheduling simulations are developed to calculate the inventory of each candidate. Finally, a spatiotemporal graph neural network is utilized to predict inventory and generate real-time deployment plans for electric fences. Case studies are conducted in two regions in Shanghai, China. Compared to a non-electric fence scenario, extensive experiments show that our framework can satisfy 98.6% of the parking demand and reduce spatial entropy by 15% within a reasonable walking distance. The results contribute to improving urban orderliness and promoting the sustainability of bike-sharing systems.

Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction model

One of the preconditions for efficient cloud computing services is the continuous availability of services to clients. However, there are various reasons for temporary service unavailability due to routine maintenance, load balancing, cyber-attacks, power management, fault tolerance, emergency incident response, and resource usage. Live Virtual Machine Migration (LVM) is an option to address service unavailability by moving virtual machines between hosts without disrupting running services. Pre-copy memory migration is a common LVM approach used in cloud systems, but it faces challenges due to the high rate of frequently updated memory pages known as dirty pages. Transferring these dirty pages during pre-copy migration prolongs the overall migration time. If there are large numbers of remaining memory pages after a predefined iteration of page transfer, the stop-and-copy phase is initiated, which significantly increases downtime and negatively impacts service availability. To mitigate this issue, we introduce a prediction-based approach that optimizes the migration process by dynamically halting the iteration phase when the predicted downtime falls below a predefined threshold. Our proposed machine learning method was rigorously evaluated through experiments conducted on a dedicated testbed using KVM/QEMU technology, involving different VM sizes and memory-intensive workloads. A comparative analysis against proposed pre-copy methods and default migration approach reveals a remarkable improvement, with an average 64.91% reduction in downtime for different RAM configurations in high-write-intensive workloads, along with an average reduction in total migration time of approximately 85.81%. These findings underscore the practical advantages of our method in reducing service disruptions during live virtual machine migration in cloud systems.

Shortest or locally quickest? A prediction-based approach for evacuation choice simulation between multiple staircases

Staircase choice is one of the most critical factors leading to the difference in pedestrian flow and evacuation routes in buildings with multiple staircases. Neither the shortest path to the building exit nor the locally quickest path to the nearest staircase can represent the natural mode of evacuation path choices for an authentic evacuation simulation. Thus, a prediction-based approach is established to predict and simulate evacuation choices, which helps to address three key issues: (1) extracting evacuation data through a controlled experiment; (2) establishing a Logit model for staircase choice prediction based on experimental data; (3) developing a prediction-based cellular automaton model. The proposed approach has achieved the coupling between choice prediction and evacuation simulation. A comparison with Pathfinder software is conducted to reveal the superiority of the prediction-based CA model for simulating staircase choice.

Overcoming denominator problems in refugee settings with fragmented electronic records for health and immigration data: a prediction-based approach.

Epidemiological studies in refugee settings are often challenged by the denominator problem, i.e. lack of population at risk data. We develop an empirical approach to address this problem by assessing relationships between occupancy data in refugee centres, number of refugee patients in walk-in clinics, and diseases of the digestive system. Individual-level patient data from a primary care surveillance system (PriCarenet) was matched with occupancy data retrieved from immigration authorities. The three relationships were analysed using regression models, considering age, sex, and type of centre. Then predictions for the respective data category not available in each of the relationships were made. Twenty-one German on-site health care facilities in state-level registration and reception centres participated in the study, covering the time period from November 2017 to July 2021. 445 observations ("centre-months") for patient data from electronic health records (EHR, 230 mean walk-in clinics visiting refugee patients per month and centre; standard deviation sd: 202) of a total of 47.617 refugee patients were available, 215 for occupancy data (OCC, mean occupancy of 348 residents, sd: 287), 147 for both (matched), leaving 270 observations without occupancy (EHR-unmatched) and 40 without patient data (OCC-unmatched). The incidence of diseases of the digestive system, using patients as denominators in the different sub-data sets were 9.2% (sd: 5.9) in EHR, 8.8% (sd: 5.1) when matched, 9.6% (sd: 6.4) in EHR- and 12% (sd 2.9) in OCC-unmatched. Using the available or predicted occupancy as denominator yielded average incidence estimates (per centre and month) of 4.7% (sd: 3.2) in matched data, 4.8% (sd: 3.3) in EHR- and 7.4% (sd: 2.7) in OCC-unmatched. By modelling the ratio between patient and occupancy numbers in refugee centres depending on sex and age, as well as on the total number of patients or occupancy, the denominator problem in health monitoring systems could be mitigated. The approach helped to estimate the missing component of the denominator, and to compare disease frequency across time and refugee centres more accurately using an empirically grounded prediction of disease frequency based on demographic and centre typology. This avoided over-estimation of disease frequency as opposed to the use of patients as denominators.

Prediction-Based Power Consumption Monitoring of Industrial Equipment Using Interpretable Data-Driven Models

Efficient energy optimization and scheduling in industrial factories depend on accurate, reasonable, and real-time monitoring of equipment power consumption. However, the power prediction of industrial equipment requires a large number of process data, which could be inevitably contaminated by some imperfect data, due to the harsh environments. Monitoring or predicting equipment power consumption is usually not feasible using data-driven black-box models with imperfect data. This paper proposes a prediction-based approach for power consumption monitoring using an interpretable data-driven model. First, a data preprocessing method is used to remove outliers and fill in missing values. Then, a Volterra polynomial basis function (VPBF) model is built to predict equipment power consumption. This model decomposes power values into a series of basis functions consisting of input parameters. Moreover, to compensate for data dropouts during the power consumption monitoring process, a networked predictive monitoring system is also proposed. Finally, this paper presents two case studies based on actual production equipment in an industrial manufacturing factory. The results demonstrate that the proposed approach can achieve satisfactory monitoring accuracy and adaptation ability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the problem of power consumption monitoring of industrial equipment in harsh production environments. A conventional solution is to predict the power consumption using data-driven black-box models, with limited feasibility and interpretability. This paper proposes a new power consumption monitoring approach, utilizing an interpretable data-driven model and a networked predictive method. This approach accurately reveals the transparent relationships between the output and input parameters. The power prediction result is consequently interpretable and more reasonable. Meanwhile, this approach actively compensates for data dropouts in the network, which can help operators real-time grasp the power consumption of equipment. Furthermore, this approach can be integrated into energy optimization systems as a basis of optimization and decision-making. Practical applications in an aluminum manufacturing company located in Guangdong Province demonstrate that this approach is feasible and applicable. Future research is to expand this approach further for energy optimization and scheduling.

A Prediction-Based Approach Realizing Finite-Time Convergence of Networked Control Systems

This paper presents a networked model predictive control (MPC) strategy for the finite-time convergence of networked control systems with two-channel random communication constraints. Network-induced delays and packet dropouts in the backward and forward channels are transformed into the equivalent delays in each channel, which are actively compensated in the controller and actuator, respectively. Future control inputs are obtained by a finite-time MPC strategy, and the upper bound of the convergence time is given by theoretical analysis. Finally, the proposed scheme is verified by the numerical simulation of a mass-spring-damper system.

A Prediction-Based Approach for Online Dynamic Appointment Scheduling: A Case Study in Radiotherapy Treatment

Patient scheduling is a difficult task involving stochastic factors, such as the unknown arrival times of patients. Similarly, the scheduling of radiotherapy for cancer treatments needs to handle patients with different urgency levels when allocating resources. High-priority patients may arrive at any time, and there must be resources available to accommodate them. A common solution is to reserve a flat percentage of treatment capacity for emergency patients. However, this solution can result in overdue treatments for urgent patients, a failure to fully exploit treatment capacity, and delayed treatments for low-priority patients. This problem is especially severe in large and crowded hospitals. In this paper, we propose a prediction-based approach for online dynamic radiotherapy scheduling that dynamically adapts the present scheduling decision based on each incoming patient and the current allocation of resources. Our approach is based on a regression model trained to recognize the links between patients’ arrival patterns and their ideal waiting time in optimal off-line solutions when all future arrivals are known in advance. When our prediction-based approach is compared with flat-reservation policies, it does a better job of preventing overdue treatments for emergency patients and also maintains comparable waiting times for the other patients. We also demonstrate how our proposed approach supports explainability and interpretability in scheduling decisions using Shapley additive explanation values. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search &amp; Approximation Algorithms. Funding: Mitacs Accélération IT26995 and Canada Research Chair in Analytics and Logistics in Healthcare (HANALOG). Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1289 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0342 ) at ( http://dx.doi.org/10.5281/zenodo.7579533 ).

Prediction-based approach for quantifying phenological mismatch across landscapes under climate change

Prediction-based approach for quantifying phenological mismatch across landscapes under climate change

A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system

Forecasting the short-term future energy demand in buildings and districts is a vital component towards the optimization of energy use and consequently the reduction in greenhouse gas emissions. This paper explores artificial intelligence approaches applied to estimate the future heating load in a district heating system. A distinction is made within thisd work between a prediction and forecasting based approach; a comparison is then accomplished by applying each method with prominent Machine Learning and Deep Learning based algorithms to estimate the future heating demand over 6 h and 24 h ahead. This analysis used available data from a Canadian district heating system in Quebec and actual weather forecasts obtained from Canadian meteorological services. All models within this work applied a grid search in order to calibrate their respective hyperparameters. Results of this work indicated that the prediction-based approach (with forecasted inputs) obtained a higher accuracy than the forecasting approach. All the machine learning models obtained good accuracy with errors not exceeding 16% CV(RMSE) and closer to 10% CV(RMSE) for the top performing models. Furthermore, the LSTM and XGBoost were consistently among the top performing algorithms and provided good performance over a variety of hyperparameters. The biggest difference between the two algorithms was the computational times; it was observed that the XGBoost was significantly faster to train.

Hybrid Prediction-Based Approach for Traffic Signal Control Problem

Hybrid Prediction-Based Approach for Traffic Signal Control Problem

Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach.

Currently, the importance of autonomous operating devices is rising with the increasing number of applications that run on robotic platforms or self-driving cars. The context of social robotics assumes that robotic platforms operate autonomously in environments where people perform their daily activities. The ability to re-identify the same people through a sequence of images is a critical component for meaningful human-robot interactions. Considering the quick reactions required by a self-driving car for safety considerations, accurate real-time tracking and people trajectory prediction are mandatory. In this paper, we introduce a real-time people re-identification system based on a trajectory prediction method. We tackled the problem of trajectory prediction by introducing a system that combines semantic information from the environment with social influence from the other participants in the scene in order to predict the motion of each individual. We evaluated the system considering two possible case studies, social robotics and autonomous driving. In the context of social robotics, we integrated the proposed re-identification system as a module into the AMIRO framework that is designed for social robotic applications and assistive care scenarios. We performed multiple experiments in order to evaluate the performance of our proposed method, considering both the trajectory prediction component and the person re-identification system. We assessed the behaviour of our method on existing datasets and on real-time acquired data to obtain a quantitative evaluation of the system and a qualitative analysis. We report an improvement of over 5% for the MOTA metric when comparing our re-identification system with the existing module, on both evaluation scenarios, social robotics and autonomous driving.

An IoT Method for Telemedicine: Lossless Medical Image Compression Using Local Adaptive Blocks

Image compression gains prominence in picture archiving and communication systems for storage and transmission of data. The advancements in technology enable the usage of computer-assisted algorithms for disease diagnosis and therapeutic planning. In this research work, a prediction-based compression algorithm was proposed to compress DICOM images. The local adaptive block-based predictor compression technique utilizes neighborhood pixel gray values for prediction and the Huffman coder was employed for the encoding of predicted coefficients. The compression results were considered effective, in contrast with the JPEG lossless, Kernel Least Mean Square, Context Adaptive Block-Based Prediction, and Least Mean square Prediction approaches. The performance validation by metrics reveals the effectiveness of the proposed local adaptive block prediction-based approach. The proposed compression algorithm was implemented in Raspberry Pi B+ embedded processor as IoT application. The compressed images are transferred through the cloud to other nodes in the network, thus facilitating the teleradiology for disease diagnosis by physicians.

Improving Patient Comfort in MRI with Predictive Acoustic Noise Cancelling.

With sound pressure levels reaching up to 130 dB, acoustic noise in Magnetic Resonance Imaging (MRI) is one of the main sources of patient discomfort in otherwise one of the safest medical imaging modalities. In this work, a noise prediction-based approach, termed predictive noise cancelling (PNC), is applied, for the first time, to suppress noise in MRI. In PN C the noise from the scanner gradient coils is predicted based on linear time-invariant models, which relate the individual gradient coil (X, Y and Z) input to the acoustic noise output. A model setup was constructed of a custom speaker box and MRI -compatible microphone to demonstrate live noise reduction. Additional tuning steps, including output channel equalization and clock mismatch correction, were performed to maximize noise reduction. A calibration sequence was designed to determine the model and tuning parameters. Analysis of actual scanner noise shows an upper limit of 21 dB noise reduction with the proposed linear model. For the components of a clinical example sequence, the setup demonstrated in-bore live noise reduction of up to 10 dB (7.01 ± 0.31 dB, 6.42 ± 2.04 dB and 9.28 ± 0.26 dB for X, Y and Z gradient coils respectively) in the presence of system imperfections. Clinical relevance - The results indicate promising noise attenuation without the need to modify scanner hardware or compromises in acquisition speed or quality. This has potential to substantially and cost effectively improve patient comfort in clinical MRI.

A dual synchronization prediction-based data aggregation model for an event monitoring IoT network

The abrupt changes in the sensor measurements indicating the occurrence of an event are the major factors in some monitoring applications of IoT networks. The prediction-based approach for data aggregation in wireless sensor networks plays a significant role in detecting such events. This paper introduces a prediction-based aggregation model for sensor selection named the Grey prediction model and the Kalman filter-based data aggregation model with rank-based mutual information (GMKFDA-MI) that has a dual synchronization mechanism for aggregating the data and selecting the nodes based on prediction and cumulative error thresholds. Furthermore, the nodes after deployment are clustered using K-medoids clustering along with the Salp swarm optimization algorithm to obtain an optimized aggregator position concerning the base station. An efficient clustering promises energy efficiency and better connectivity. The experiments are accomplished on real-time datasets of air pollution monitoring applications and the results for the proposed method are compared with other similar state-of-the-art techniques. The proposed method promises high prediction accuracy, low energy consumption and enhances the throughput of the network. The energy-saving is recorded to be more than 10 to 30% for the proposed model when compared with other similar approaches. Also, the proposed method achieves 97.8% accuracy as compared to other methods. The method proves its best working efficiency in the applications like event reporting, target detection, and event monitoring.

Temperature prediction-based realistic performance analysis of various electrical configurations of solar PV panels

In this paper, a temperature prediction-based approach for realistic performance analysis of the solar photovoltaic system has been investigated. Dissimilar to general methods, the change in module temperature, due to changing solar irradiance, has been considered for realistic and accurate performance evaluation results under mismatch conditions. The module temperature is predicted by a linear equation representing the effects of ambient temperature, wind speed, and irradiance. An optimized technique has been implemented for photovoltaic panel modeling. It provides an accurate estimation of the series and shunt resistances of the photovoltaic panel. The proposed modeling method provides an accuracy of less than 0.02% at the maximum power point. The temperature prediction-based approach has been applied to a solar photovoltaic system of 16 panels, modeled using the proposed technique. Further, five photovoltaic configurations were investigated: Series, Series-Parallel, Honey-Comb, Bridge-Linked, and Total-Cross-Tied. The performance evaluation of each configuration is based on the generated maximum power, fill factor, and mismatch loss. Under mismatch conditions, the Total-Cross-Tied configuration shows a good immunity against mismatch by minimizing losses in all the considered mismatch patterns. The proposed approach provides more accurate results than the available ones because it considers the temperature effect (due to variation in ambient temperature, solar radiation, and wind speed), accounting for up to 12% reduction in output power of photovoltaic modules.

CPMAN: Change Point Detection Approach in Time Series Based on the Prediction of Multi-stage Attention Networks

Time series change point detection can identify the locations of abrupt points in many dynamic processes. It can help us to find anomaly data in an early stage. At the same time, detecting change points for long, periodic, and multiple input series data has received a lot of attention recently, and is universally applicable in many fields including power, environment, finance, and medicine. However, the performance of classical methods typically scales poorly for such time series. In this paper, we propose CPMAN, a novel prediction-based change point detection approach via multi-stage attention networks. Our model consists of two key modules. First, in the time series prediction module, we employ the multi-stage attention-based networks and integrate the multi-series fusion mechanism. This module can adaptively extract features from the relevant input series and capture the long-term temporal dependencies. Secondly, in the change point detection module, we use the wavelet analysis-based algorithm to detect change points efficiently and identify the change points and outliers. Extensive experiments are conducted on various real-world datasets and synthetic datasets, proving the superiority and effectiveness of CPMAN. Our approach outperforms the state-of-the-art methods by up to 12.1% on the F1 Score.

Understanding providers\u2019 attitudes and key concerns toward incorporating CVD risk prediction into clinical practice: a qualitative study

BackgroundAlthough risk prediction has become an integral part of clinical practice guidelines for cardiovascular disease (CVD) prevention, multiple studies have shown that patients’ risk still plays almost no role in clinical decision-making. Because little is known about why this is so, we sought to understand providers’ views on the opportunities, barriers, and facilitators of incorporating risk prediction to guide their use of cardiovascular preventive medicines.MethodsWe conducted semi-structured interviews with primary care providers (n = 33) at VA facilities in the Midwest. Facilities were chosen using a maximum variation approach according to their geography, size, proportion of MD to non-MD providers, and percentage of full-time providers. Providers included MD/DO physicians, physician assistants, nurse practitioners, and clinical pharmacists. Providers were asked about their reaction to a hypothetical situation in which the VA would introduce a risk prediction-based approach to CVD treatment. We conducted matrix and content analysis to identify providers’ reactions to risk prediction, reasons for their reaction, and exemplar quotes.ResultsMost providers were classified as Enthusiastic (n = 14) or Cautious Adopters (n = 15), with only a few Non-Adopters (n = 4). Providers described four key concerns toward adopting risk prediction. Their primary concern was that risk prediction is not always compatible with a “whole patient” approach to patient care. Other concerns included questions about the validity of the proposed risk prediction model, potential workflow burdens, and whether risk prediction adds value to existing clinical practice. Enthusiastic, Cautious, and Non-Adopters all expressed both doubts about and support for risk prediction categorizable in the above four key areas of concern.ConclusionsProviders were generally supportive of adopting risk prediction into CVD prevention, but many had misgivings, which included concerns about impact on workflow, validity of predictive models, the value of making this change, and possible negative effects on providers’ ability to address the whole patient. These concerns have likely contributed to the slow introduction of risk prediction into clinical practice. These concerns will need to be addressed for risk prediction, and other approaches relying on “big data” including machine learning and artificial intelligence, to have a meaningful role in clinical practice.

Cybersecurity by Prediction of Time Synchronization Using Bayesian base Gradient Descent Approach

Time Commerce tends to be an effort that needs to be adapted to remove framework limitations and judicial intensifications to the inclusive digital economy in the form of a regional collaborative organisational system. "Date" and "Timestamp" reflect the root of the current term "Date Trade" in the cyber world The electricity grid shifts to the energy network to improve operating efficiency and reliability by developing advanced information and communication technology. However, the Internet also provides a range of entry points dependent on the internet, which produce additional vulnerabilities due to malicious cyber-attacks, thereby threatening nations' economic health. This paper proposes, a new mechanism to protect Critical Systems against malicious attacks, based on interval state predictors. In this paper using prediction-based approach for reducing the impact of cyber space attack. In prediction we use machine learning approach of Bayesian classifier by Bayesian approach forecast time synchronization according to universal time clock (UTC). In experiment analysis basic UTC, UTC and its likelihood and proposed approach on base of communication to improve substantially in proposed approach. Keywords Time Commerce, Timestamp, Cyber Security, Indian Standard Time, Time Dissemination, Cyber-Physical Systems, Big Data, Bayesian Prediction, Gradient Descent.

A prediction-based method to estimate student learning outcome: Impact of response rate and gender differences on evaluation results

Background Low response rates threaten the reliability and validity of student evaluations of teaching. Previous research has shown that asking students to predict how satisfied their fellow students were with a course produces reliable results at lower response rates. The aim of this study was to investigate whether this prediction-based method can also be used to evaluate student learning outcome. Methods Before and after a cardiorespiratory module, 128 fourth-year medical students provided self-assessments and predictions of performance on 27 specific learning objectives and took formative tests on the respective contents. Pre-post performance gain was compared across all three modalities. Results Formative exam results indicated a performance gain of 63.0%. Self-assessed and prediction-based performance gains were identical (67.8%) but both slightly overestimated actual performance gain. Irrespective of the method used, a response rate of 20% was sufficient to produce reliable results. Compared to male students, females greatly overestimated their peers’ performance which led to inflated performance gain values. Conclusions Student self-assessments and predictions are equally valid sources of learning outcome measures, and low response rates are sufficient to produce stable results. When using a prediction-based approach, a tendency to overestimate learning outcome in female students needs to be taken into account.