TP Model Research Articles

부산국제주류 및 와인전시박람회 참관객의 수요 결정요인 분석

Purpose - The purpose of this study is to provide the characteristics of exhibition fair consumers, and to provide practical measures to establish appropriate marketing strategies. Design/Methodology/Approach - This study applied the service quality of the exhibition fair as an independent variable for visitors of the 2023 Busan International Liquor & Wine Expo held in BEXCO, Busan, and analyzed the TP model as a research method. In addition, the amount of annual participation was estimated and analyzed as a dependent variable. Findings - Looking at the results of the study, as a result of the analysis of the attribute factors of service quality selection at the exhibition, it was found that it had a positive effect on diversity and comfort, accessibility and convenience facilities, quality and fidelity, and the benefit and value of information. In addition, it can be seen that out of all the socioeconomic characteristics variables, job factor is the only one that had a positive effect. Research Implications - The results of the study hoped to provide the characteristics of consumers by analyzing demand determinants using the service quality selection attribute of the exhibition, and at the same time provide practical measures to establish appropriate marketing strategies.

Research of Park and Ride Parking Spaces Tiered Pricing Methodology Based on Subway Ride Distance

Park and Ride (P&R) as a demand management tool has the effect of reducing traffic congestion in urban centers, saving energy and reducing pollutant emissions. Since 2000, many cities in China have been constructing P&R facilities, which have partially alleviated urban traffic congestion and provided a time-reliable mode of travel for commuters heading to urban centers. However, in recent years, due to the pricing policy of the P&R facility, there has been an insufficient supply of P&R facilities in many places. In fact, the P&R system prefers to welcome travelers who make long-distance subway rides and does not want those who make short-distance subway rides to occupy more parking spaces. To address this, this paper proposes a tiered pricing strategy that considers charging parking fees based on the distance traveled by commuters after switching to public transportation, to improve the utilization of P&R. That is, charge less for parking for long-distance subway riders and more for short-distance subway riders. Firstly, based on questionnaire data from SP surveys, a fixed pricing mixed logit model (FP model) and a tiered pricing mixed logit model (TP model) for P&R facilities are constructed. Utilizing two models, we explored the mechanisms underpinning traveler’s mode choice influenced by daily habits and travel considerations through the comparison of the two models to validate the effectiveness of the tiered pricing for P&R facilities. The study found that the implementation of a tiered pricing method for P&R facilities increases its attractiveness to long-distance subway ride travelers, resulting in a higher proportion of long-distance subway riders among P&R commuters. In the study’s last section, a marginal effect analysis was conducted on the per-kilometer cost (Pkm) within the P&R model. This analysis determined the optimal Pkm for three subway travel distances within the P&R model. Consequently, it calculated the corresponding P&R parking fees for these three subway travel distances. Additionally, we have predicted the implementation effects of the tiered pricing scheme.

Age in addition to RETTS triage priority substantially improves 3-day mortality prediction in emergency department patients: a multi-center cohort study

BackgroundPrevious studies have shown varying results on the validity of the rapid emergency triage and treatment system (RETTS), but have concluded that patient age is not adequately considered as a risk factor for short term mortality. Little is known about the RETTS system’s performance between different chief complaints and on short term mortality. We therefore aimed to evaluate how well a model including both RETTS triage priority and patient age (TP and age model) predicts 3-day mortality compared to a univariate RETTS triage priority model (TP model). Secondarily, we aimed to evaluate the TP model compared to a univariate age model (age model) and whether these three models’ predictive performance regarding 3-day mortality varies between patients with different chief complaints in an unsorted emergency department patient population.MethodsThis study was a prospective historic observational cohort study, using logistic regression on a cohort of patients seeking emergency department care in Stockholm during 2012–2016. Patient visits were stratified into the 10 chief complaint categories (CCC) with the highest number of deceased patients within 3 days of arrival, and to “other chief complaints”. Patients with priority 1 were excluded.ResultsThe studied cohort contained 1,690,981 visits by 788,046 different individuals. The TP and age model predicted 3-day mortality significantly and substantially better than both univariate models in the total population and in each studied CCC. The age model predicted 3-day mortality significantly and substantially better than the TP model in the total population and for all but three CCCs and was not inferior in any CCC. There were substantial differences between the studied CCCs in the predictive ability of each of the three models.ConclusionsAdding patient age to the RETTS triage priority system significantly and substantially improves 3-day mortality prediction compared to RETTS priority alone. Age alone is a non-inferior predictor of 3-day mortality compared to RETTS priority. The impact on 3-day mortality prediction of adding patient age to RETTS priority varies between CCCs but is substantial for all CCCs and for the total population. Including age as a variable in future revisions of RETTS could substantially improve patient safety.

Temperature Field Modelas a Factor to Improve a Horizontal Ground Heat Exchanger of a Heat Pump System: Review of Scientific Approaches and the Content of the Analytical Model

The article is devoted to the research of issues related to the organization of control systems for heat pump installations (HPS). HPS is used to use low-potential energy of the environment in the heat supply of buildings. HPS with horizontal ground heat exchangers (HGHE) are of great importance for improving energy efficiency. The efficiency and reliability of HPS with HGHE strongly depends on the state of the soil temperature field. A large number of scientific publications are devoted to modeling soil temperature fields forHGHEparameterdetermination; a significant part of them is devoted to the study of vertical ground heat exchangers. Scientific developments presented in foreign publications on modeling the temperature field of the HGHE location environment are focused on the use of HPS in the cold supply mode and/or in the heat supply mode under conditions of a slight decrease in temperature, which excludes soil freezing. The aim of the study was to apply the existing methods of the analytical theory of thermal conductivity to develop a soil-environment TP model for the placement of a HGHE HPS; the developed model is intended for engineering calculations of the main design HGHEparameters; the model is also applicable to determine the annual specific heat flow as an important parameter in the optimal control system of the HPI. The development of the model is based on the condition of a two-element structure of the soil massif: under the HGHE and above the HGHE; the target parameter is the temperature of the soil mass, depending on the coordinate and time, disclosed through the indicator 'relative temperature rise'; in accordance with the provisions of the thermal conductivitytheory, the relative excess temperature is given for the cases of a semi-infinite body (soil mass under the HGHE) and a slab (soil mass above the HGHE). The proposed model is implemented on the example of two constituent entities of the Russian Federation. The results obtained made it possible to determine the minimum guaranteed possibility of using HPS for heat supply in these climatic conditions. The model presented in the article can be used as a context for further studies of the main parameters of HGHE HPS operation and optimization of HPS operation modes.

Unveiling the Transparency of Prediction Models for Spatial PM2.5 over Singapore: Comparison of Different Machine Learning Approaches with eXplainable Artificial Intelligence

Aerosols play a crucial role in the climate system due to direct and indirect effects, such as scattering and absorbing radiant energy. They also have adverse effects on visibility and human health. Humans are exposed to fine PM2.5, which has adverse health impacts related to cardiovascular and respiratory-related diseases. Long-term trends in PM concentrations are influenced by emissions and meteorological variations, while meteorological factors primarily drive short-term variations. Factors such as vegetation cover, relative humidity, temperature, and wind speed impact the divergence in the PM2.5 concentrations on the surface. Machine learning proved to be a good predictor of air quality. This study focuses on predicting PM2.5 with these parameters as input for spatial and temporal information. The work analyzes the in situ observations for PM2.5 over Singapore for seven years (2014–2021) at five locations, and these datasets are used for spatial prediction of PM2.5. The study aims to provide a novel framework based on temporal-based prediction using Random Forest (RF), Gradient Boosting (GB) regression, and Tree-based Pipeline Optimization Tool (TP) Auto ML works based on meta-heuristic via genetic algorithm. TP produced reasonable Global Performance Index values; 7.4 was the highest GPI value in August 2016, and the lowest was −0.6 in June 2019. This indicates the positive performance of the TP model; even the negative values are less than other models, denoting less pessimistic predictions. The outcomes are explained with the eXplainable Artificial Intelligence (XAI) techniques which help to investigate the fidelity of feature importance of the machine learning models to extract information regarding the rhythmic shift of the PM2.5 pattern.

Extending the TPTool MATLAB toolbox with LMI based observer and disturbance rejection design

Present paper demonstrates the extensions of MATLAB TPtool toolbox. The toolbox does not include the LMI based observer and disturbance rejection design. Thus, the suggested extensions include the observer and H∞ based disturbance rejection LMI conditions for TP models. Furthermore this toolbox contains obsolete algorithms. Therefore, in this paper, the recommended algorithms are presented. Showing the proposed design method is based on a nonlinear dynamic system; modified TP model of the TORA example is used. Thus, the detailed observer and controller design, the disturbance rejection design are presented with LMI stability conditions through the HOSVD based method in the PDC framework.

베트남 의료관광 수요 결정요인에 관한 연구

Purpose – The purpose of this study is to identify the predictive factors of medical tourism in Vietnam by analyzing the variables that lead to the annual visitor number. Design/methodology/approach – This study collected data from Vietnamese with experience in domestic and international medical tourism, and focused on Hanoi, Vietnam. In this study, the variable of medical tourism preferences was applied independently. Factors that impacted the number of annual visitors were estimated by the truncated Poisson model. Findings – The results of the TP model analysis of demand for medical tourism are as follows. Among the variables related to socio-economic characteristics, age, marital status, and occupation had a significantly positive effect on the number of annual medical visits. These results imply that it is important to understand socio-economic characteristics to expand demand for medical tourism in Vietnam, which shows high growth potential. The top three variables that gave a significant positive effect on the number of annual medical visitors are the level of medical technology, waiting time, and price, as well as medical service experience. Research implications – The results indicate the need to develop medical tourism products tailored to consumer tastes by subdividing medical technology levels, waiting for time and price, medical experience services, and so on. Thus, the results presented by this study create a starting point for the development of demand forecasting models for the domestic, international, and Vietnamese medical tourism markets in the future.

A probabilistic forecasting approach for air quality spatio-temporal data based on kernel learning method

Reliable air quality index forecasting is an important guarantee for government to take early emergency measures to protect the ecological environment and public health. Nevertheless, providing accurate and reliable forecasting is still a difficult and challenging task, owing to the high nonlinearity and noise in the AQI (air quality index). This study develops a novel framework based on feature selection, the Error Correction Model, and the Residual estimation method with new structure fast kernels, to conveniently improve the prediction performance and efficiently quantify the uncertainty of the point prediction generated by any pre-trained model. This framework takes some representative ML (machine learning) tools as examples, with the significant input variables extracted by the two-stage feature selection method for accurate AQI point prediction, and models their residuals by the multi-output Gaussian process and the multi-output Student−t process, which rely on the new multi-output I/O kernels with chronological memory to embed the relevant information of the point prediction and the residuals as well as of different output for making predictions on residual distributions, thereby enhancing prediction performance and enriching uncertainty information. Besides, an acceleration method based on the Orthogonal Random Features method is also proposed to enable the framework to handle large-scale data at an acceptable time cost. The framework is validated by the AQI Spatio-temporal data of four observation points in Shijiazhuang, China, and the result reveals that the proposed framework provides valid prediction intervals and enhances the accuracy of the ML models’ point predictions. Specifically, the framework reduces the prediction error of almost all basic models, and provides shorter prediction intervals than other common methods. Especially, the multi-output GP model or multi-output TP model obtains the best improvement in point prediction and the shortest prediction intervals.

ASKERİ ALANLARDA KULLANILMAK ÜZERE İNSANSIZ HAVA ARACI (İHA) SİSTEMLERİ SEÇİMİNDE TOPSIS VE BULANIK TOPSIS YÖNTEMİNİN KULLANILMASI

Bu çalışma, askeri alanda kullanılan ve kullanılacak olan İnsansız Hava Aracı (İHA) Sistemlerinin, sahip
 olduğu teknik özelliklerinin karşılaştırılması ile tedarik karar aşamasında, en uygun kararın verilmesini
 amaçlamaktadır. Bu amaç doğrultusunda çalışmada, askeri alanda kullanıma uygun, üç yerli ve beş yabancı
 firmanın üretmiş ve üretim safhasında olduğu İHA Sistemleri’nin teknik verileri toplanmıştır. Toplanan bu
 veriler ortak olan başlıklar altında kriterlere [İlk olarak üç kriter (Havada Kalış Süresi, Maksimum İrtifa ve
 Faydalı Yük Kapasitesi) ile TOPSIS ve Bulanık TOPSIS analizi yapılmış ve sonrasında bu üç kritere iki kriter
 (Seyir Hızı ve Maksimum Hız) daha eklenerek sadece TOPSIS ile analiz yapılmıştır.] ayrılarak, TOPSIS ve
 Bulanık TOPSIS yöntemi ile sıralanmıştır. Elde edilen bulgular doğrultusunda, TOPSIS üç ve beş kriterli
 sıralamada ilk sırada sırasıyla, Yabhon United 40 ve Predator C Avenger modelleri yer alırken; Bulanık
 TOPSIS ile sıralamada ilk sırada, Heron TP modelinin yer aldığı tespit edilmiştir.

Random forest-based modeling of stream nutrients at national level in a data-scarce region

Nutrient runoff from agricultural production is one of the main causes of water quality deterioration in river systems and coastal waters. Water quality modeling can be used for gaining insight into water quality issues in order to implement effective mitigation efforts. Process-based nutrient models are very complex, requiring a lot of input parameters and computationally expensive calibration. Recently, ML approaches have shown to achieve an accuracy comparable to the process-based models and even outperform them when describing nonlinear relationships. We used observations from 242 Estonian catchments, amounting to 469 yearly TN and 470 TP measurements covering the period 2016–2020 to train random forest (RF) models for predicting annual N and P concentrations. We used a total of 82 predictor variables, including land cover, soil, climate and topography parameters and applied a feature selection strategy to reduce the number of dependent features in the models. The SHAP method was used for deriving the most relevant predictors. The performance of our models is comparable to previous process-based models used in the Baltic region with the TN and TP model having an R2 score of 0.83 and 0.52, respectively. However, as input data used in our models is easier to obtain, the models offer superior applicability in areas, where data availability is insufficient for process-based approaches. Therefore, the models enable to give a robust estimation for nutrient losses at national level and allows to capture the spatial variability of the nutrient runoff which in turn enables to provide decision-making support for regional water management plans.

Special Issue on “TP model transformation based control design theories and applications”

Special Issue on “TP model transformation based control design theories and applications”

Global Within-Season Yield Anomaly Prediction for Major Crops Derived Using Seasonal Forecasts of Large-Scale Climate Indices and Regional Temperature and Precipitation

AbstractWeather and climate variability associated with major climate modes is a main driver of interannual yield variability of commodity crops in global cropland areas. A global crop forecasting service that is currently in the test operation phase is based on temperature and precipitation forecasts, while recent literature suggests that crop forecasting services may benefit from the use of climate index forecasts. However, no consistent comparison is available on prediction skill between yield models relying on forecasts from temperature and precipitation and from climate indices. Here, we present a global assessment of 26-yr (1983–2008) within-season yield anomaly hindcasts for maize, rice, wheat, and soybean derived using different types of statistical yield models. One type of model utilizes temperature and precipitation for individual cropping areas (the TP model type) to represent the current service, whereas the other type relies on large-scale climate indices (the CI model). For the TP models, three specifications with different model complexities are compared. The results show that the CI model is characterized by a small reduction in the skillful area from the reanalysis model to the hindcast model and shows the largest skillful areas for rice and soybean. In the TP models, the skill of the simple model is comparable to that of the more complex models. Our findings suggest that the use of climate index forecasts for global crop forecasting services in addition to temperature and precipitation forecasts likely increases the total number of crops and countries where skillful yield anomaly prediction is feasible.

Special issue on “TP model transformation based control design theories and applications”

Special issue on “TP model transformation based control design theories and applications”

Special issue on “TP model transformation based control design theories and applications”

Special issue on “TP model transformation based control design theories and applications”

Diffusion Approximation for an Open Queueing Network with Limited Number of Customers and Time-Dependent Service

The object of this research is an open queueing network (QN) with a single class of customers, in which the total number of customers is limited. Service parameters are dependent on time, the routing of customers is determined by an arbitrary stochastic transition probability matrix, which is also depends on time. The service times of customers in each queue of the system is exponentially distributed with FIFO service, and it is assumed that a birth and death process generates and destroys the customers. The random vector, which determines the network state, forming a Markov random process is introduced. The purpose of the research is an asymptotic analysis of this Markov process, describing the queueing network state with a large number of customers, obtained from a system of differential equations, and used to find the mean relative number of customers in the network queues at any time. The results are illustrated with a specific example. This approach can be used tp model processes of customer service in the insurance companies, banks, logistics companies and other cyber-economical or service organizations.

Augmented TP model transformation‐based parallel distributed compensation control design

AbstractThe usual sampled hyper grid points of uniform sampling method are distributed equally in the TP model transformation, thus, the sampling results often omit the local extrema when the sampling step is not fine‐tuned. Then the resultant tensor which is used for controller design can not fully cover the state space, although the gain which is the feasible solution of the linear matrix inequalities. In this paper, we proposed a non‐uniform sampling method for tensor product model transformation, local extrema are considered in the sampling step, while the sampling step can vary dynamically for different function entries. In this paper, TP model transformation‐based parallel distributed compensation (PDC) controller is extended in three folds: (i) The existing TP model transformation‐based PDC controller with uniform sampling method is extended to TP model transformation‐based PDC tracking controller with the uniform sampling method and an extended signal. (ii) A new TP model transformation‐based PDC tracking controller is proposed based on a new sampling method, that is, the Hammersley sampling method. (iii) TP model transformation‐based PDC tracking controller is also proposed based on the non‐uniform sampling method. The proposed adaptive TP model transformation‐based PDC tracking controller is able to enhance the performance of the TP model transformation‐based PDC controller, and the adaptive TP model transformation‐based PDC controller obtains the best results due to the nearly exact sampling of the system.

Special issue on “TP model transformation based control design theories and applications”

Special issue on “TP model transformation based control design theories and applications”

Improved control performance of the 3‐DoF aeroelastic wing section: a TP model based 2D parametric control performance optimization

AbstractBased on the most recent Tensor Product model transformation solutions, the paper presents an improved control performance for the most recent version of the three Degree of Freedom aeroelastic wing section model including Stribeck friction, according to signals pitch, plunge, trailing edge and control value, based on practical engineering criteria such as overshoot, undershoot, signal end values and settling time. This is achieved through proposing a novel two dimensional parametric convex hull manipulation based method for Tensor Product model transformation based Control Design Frameworks. The approach provides two TP model representations for the different requirements of the controller and observer of a given model, opening the possibility to utilize the TP model transformation's convex hull manipulation potential in control performance optimization for a separate optimization of the two TP model representations. Numerical simulation results are provided to illustrate the control performance improvements of the aeroelastic wing section model through the proposed 2D parametric convex hull manipulation based design method.

Minimal Volume Simplex (MVS) Polytopic Model Generation and Manipulation Methodology for TP Model Transformation

AbstractThe vertices of a polytopic LPV/qLPV model determine the feasibility of any linear matrix inequality (LMI) based control design as well as the achievable control performance, since the polytope typically includes not only the actual system dynamics. This paper proposes a powerful method to determine suitable polytopic qLPV Tensor‐Product model forms that allows for fine tuning through constraints on the locations of the vertices. The paper shows that these tuning methods are capable of effectively influencing the closed‐loop control performance results from LMI‐based multi‐objective optimisation. The advantages of the proposed methods are their tractability, low computational complexity, as well as their determinism. The key idea behind the proposed methods is to determine vertices in each decoupled parameter‐dimensions of the TP model in such a way that they form a minimal volume enclosing simplex fulfilling further fine tuning conditions on its shape. This is achieved based on the extension of the Minimal Volume Simplex Analysis (MVSA) method, where the volume of the enclosing simplex polytope is minimized by applying Sequential Quadratic Programming with majorization minimization strategy extended with a set of additional constraints influencing the location of certain vertices according to physically established considerations. Besides the comprehensive presentation of the proposed design methodology an expressive numerical example is provided to demonstrate its effectiveness and usability.

Polytopic [formula omitted] filter design and relaxation for nonlinear systems via tensor product technique

This paper focuses on the intensive relaxation of the conservativeness inherently existing in the primitive stage of the polytopic H∞ filter design for general nonlinear systems. The considered conservativeness intrinsically stems from the polytopic representation of the nonlinear system. Therefore, it is difficult to obtain a suitable polytopic representation in the case of the special requirement on small enough conservativeness. In this paper, the vertices of the polytopic representation and representation error are especially incorporated into the filter solution together, such that the deeply-relaxed filter can be achieved with the trade-off between the tightness of the vertex polytope and the representation error. Concretely, based on the TP model transformation and an ad hoc rectification, a well-adjusted polytopic representation is acquired for the nonlinear system by two independent steps, where the first step guarantees the satisfactory representation error, while the second step further assures the tightness of the vertex polytope. Finally, numerical simulations are provided to demonstrate the effectiveness and feasibility of the method in terms of both the polytopic filter relaxation and the filter performance.