Error Model Research Articles

A maintainable and iterative development approach of critical systems with FoCaLiZe

The life-cycle development of critical systems follows mainly a V-cycle process overlapped with proofs and/or verification techniques. At the first development stage, a critical system must be, precisely, and as completely as possible described. Thus, it is recommended to use a graphical modeling language standard such as the Unified Modeling Language (UML) and the Object Constraint Language (OCL), to which both customers and developers could contribute. Then, the graphical design with its formal specification (requirements) is mapped into a complete formal development environment, using an MDE (Model-driven engineering) approach. The formal environment has to assess the development until the generation of a secure code. In this context, we propose a life-cycle development approach that combines UML/OCL and the FoCaLiZe formal environment for the secure development of critical systems. The proposed approach ensures essential development constraints such as strict boundaries and traceability between development phases. It also ensures a secure maintenance phase using partial transformation techniques from UML/OCL to FoCaLiZe. The latter enhances the transformation from UML/OCL into FoCaLiZe to support incremental development and assist developers in correcting modeling errors. The proposed approach is mostly dedicated to secure and maintainable lifecycle development processes of critical systems. The formal environment assesses the development process until the generation of a secure code. To illustrate the proposed approach, we present the stages of development of a constrained system controlling military objects. The development of this system uses the iterative, incremental, and maintainability stages as described by the development approach, until the generation of secure code.

CAN THE APPLICATION OF AGROFORESTRY AT MONOCULTURAL FOOD CROP AREAS SUPPORT MANUFACTURER CHEMICAL FERTILIZER REDUCTION PROGRAMS WITHOUT SACRIFICING FARMERS' INCOME?

In an agroforestry system, nutrients from the subsoil can be made available to food plants through the absorption process by trees with deep roots. However, shade from canopy tree plants can suppress photosynthesis and reduce food crop production and farmer income [INCM]. This antagonism needs to be evaluated when introducing agroforestry programs into monoculture food crop areas to reduce manufactured chemical fertilizers application. This research was carried out from June to August 2023 at Natar District, South Lampung Regency to determine the effect of tree strata (teak, acacia, and albizia), multi-proposed tree species or MPTS (Parkia speciosa, avocado, and jack fruit), shrubs (coffee and cacao), and ground cover (vegetables, peanut, and soybean) to [INCM]. Interviewing forty respondents who were still retaining one or more of these tree strata among food crops including upland rice, corn, and cassava. The OLS (ordinary least square) model was employed at 90% confidence level. To eliminate the model error, the variables ownership of productive assets (upland, cattle, sheep, and cock), fertilizer application (urea, ponska, KCl and liquid fertilizer), compost, and pesticide use were also incorporated in the model. The results suggest the significant effects: (1) that positive ones are acacia, teak, vegetables, peanuts, and, soybeans, respectively USD 86.4 (P=0.000); 88.3 (P=0.000); 109,5 (P=0.000); 114.6 (P =0.000); 145.1 (P=0.000); and (2) which negative were Albizia falcataria, Parkia speciosa, avocado, coffee, and cacao respectively of around USD 31,7 (P=0.002); 57.1 (P=0.000); 43.2 (P=0.003); 62,0 (P=0.000) and 96.37 (P=0.000).

Modified correlated measurement errors model for estimation of population mean utilizing auxiliary information

The existence of measurement errors cannot be avoided in practice. It is a prominent fact that the existence of measurement errors diminishes conventional properties of the estimators. A modified correlated measurement errors model has been proposed. Shalabh and Tsai (Commun Stat Simul Comput 46(7):5566–5593. 10.1080/03610918.2016.1165845, 2017) correlated measurement errors model is a particular member of the suggested modified model. In this article, we have tackled the estimation of population mean utilizing auxiliary information under modified correlated measurement errors model. We have developed ratio and product estimators and studied their properties in case of simple random sampling without replacement (SRSWOR) up to first order of approximation. It has been illustrated that suggested ratio and product estimators are more efficient than the conventional unbiased estimator as well as Shalabh and Tsai (Commun Stat Simul Comput 46(7):5566–5593. 10.1080/03610918.2016.1165845, 2017) ratio and product estimators under very realistic situations. An empirical study has also been performed to demonstrate the merits of the recommended estimators over other estimators.

Application of improved DBN and GRU based on intelligent optimization algorithm in power load identification and prediction

Non intrusive load monitoring belongs to the key technologies of intelligent power management systems, playing a crucial role in smart grids. To achieve accurate identification and prediction of electricity load, intelligent optimization algorithms are introduced into deep learning optimization for improvement. A load recognition model combining sparrow search algorithm and deep confidence network is designed, as well as a gated recurrent network prediction model on the grounds of particle swarm optimization. The relevant results showed that the sparrow search algorithm used in the study performed well on the solution performance evaluation metrics with a minimum value of 0.209 for the inverse generation distance and a maximum value of 0.814 for the hyper-volume. The accuracy and recall values of the optimized load identification model designed in the study were relatively high. When the accuracy was 0.9, the recall rate could reach 0.94. The recognition accuracy of the model on the basis of the test set could reach up to 0.924. The lowest classification error was only 0.05. The maximum F1 value of the harmonic evaluation index of the bidirectional gated recurrent network optimized by particle swarm optimization converged to 90.06%. The loss function had been optimized by particle swarm optimization, and both the convergence value and convergence speed had been markedly enhanced. The average absolute error and root mean square error of the prediction model were both below 0.3. Compared to the bidirectional gated recurrent model before optimization, the particle swarm optimization strategy had a significant improvement effect on prediction details. In addition, the research method had superior recognition response speed and adaptability in real application environments. This study helps to understand the load demand of the power system, optimize the operation of the power grid, and strengthen the reliability, efficiency, and sustainability of the power system.

Prediction of the effect of load resistance and heat input on the performance of thermoelectric generator using numerical and artificial neural network models

The load resistance in the thermoelectric generators (TEGs) is crucial for optimizing power output, and managing load resistances and operating conditions are integral elements of TEG system design. In this context, it is very important to predict the performance of TEGs at variable operating conditions. This research addresses an important problem in TEG by predicting the effects of load resistance and heat input on performance using both numerical and ANN models. The three-dimensional finite volume methods applied by employing ANSYS software, and the results were compared with experimental and ANN results in terms of voltage, current, power output and efficiency. In case of ambient temperature values of 17 °C, the average absolute errors of ANN and numerical model were calculated as 2.09 % and 4.22 % for voltage output, 0.79 % and 7.73 % for current output, 5.35 % and 12.09 % for power output, 4.14 % and 12.09 % for efficiency, respectively. As a result, it was observed that the ANN model gives better results compared to the numerical model. The highest power output was obtained at load resistance of 5.4 Ω and hot surface temperature of 150 °C, with the value of 0.827 W experimentally, 0.905 W numerically, and 0.824 W with ANN models. Besides, to evaluate the effect of ambient temperature, two additional temperatures (10 °C and 25 °C) were tested and it was found that decreasing the ambient temperature increased the TEG performance. The results showed that significant improvements on power and efficiency performance of the TEG can be achieved with optimised operating conditions.

Geometric Error-Based Multi-Source Error Identification and Compensation Strategy for Five-Axis Side Milling

Based on a multi-source error model, this paper discusses the principle of error element identification and uses the mirror bias method to compensate the geometric errors of a process system. Firstly, a nine-line measurement method to determine the geometric error of the linear feed axes of machine tools is introduced, and the geometric error identification model based on the “nine-line method” is established. Then, using a ballbar mounted in the axial, tangential, and radial directions of the machine, the geometric error elements of the rotation axis are identified by three simple measurements in each direction. Subsequently, for the more common flat vise clamping workpiece in actual production, the workpiece position error is identified by using the traditional process of dimensional chain, and the workpiece attitude error is identified by fitting the angle between the positioning plane and the horizontal plane by the least squares method. Finally, based on the tool position points and tool axis vectors obtained from the multi-source error model, the error compensation value is solved using inverse machine tool kinematics to offset the machining error by mirroring the error value of the same size, and based on the “S-shaped specimen” to compensate the processing experiments, after compensation, the processing error is reduced by 30~45%, verifying the effectiveness of the compensation method.

Optimal station placement based on grey wolf optimizer for regional target localization

The accuracy of target passive localization is influenced by the placement of signal receiving stations; therefore, many studies have been performed to optimize station placement. However, most of the present placement methods focus on the localization error of one target, and if the exact position of the target cannot be determined, but only the range of the target activity is known, how to study the localization station placement in a region is a problem that needs to be solved. This paper proposes a grey wolf optimization algorithm based on the regional target error model to solve the optimal station placement problem. Firstly, a regional target localization error model is established using the measured TDOA, and the overall error matrix within a region is derived. Then, by taking the trace of the error matrix as a criterion, the objective function is established to find the optimal location of the receiving station by grey wolf optimizer. The optimization parameters are also improved to increase the global search ability of the algorithm. Finally, the feasibility and reliability of the overall error model and the grey wolf algorithm proposed are verified by experiments from multiple perspectives. The station placement method proposed in this paper can effectively solve the localization problem of targets that are only known to be in a general activity region in advance, which is more realistic.

Subsetting reduces the error of MIR spectroscopy models for soil organic carbon prediction in the U.S. Great Plains

High demand for soil organic carbon data to support soil health and climate change mitigation efforts must be met with rapid, accurate, and inexpensive measurement methods. Mid-infrared spectroscopy shows promise as an alternative to conventional soil carbon analysis; however, its practicality depends on the construction and efficient use of soil spectral libraries. Subsetting, a calibration optimization technique, has potential to reduce model prediction errors. Nevertheless, the effectiveness of different subsetting criteria has yet to be well explored. This study assessed whether several subsetting criteria would result in calibration models with reduced error in the prediction of soil organic carbon content compared to models constructed from a full spectral dataset. A mid-infrared spectral library composed of Nebraska and Kansas soil samples was subset by (i) a nested wetland criterion, (ii) soilscapes, (iii) presence or absence of carbonates, and (iv) a combination of soilscape and carbonates. Partial least squares regression was used to construct all calibration models. Predictive performance of the subset models was compared to that of their corresponding full set model using several statistical metrics. Subsetting by wetlands reduced model error by 22 and 56 %. Subsetting by soilscape yielded a 13 to 55 % reduction in model error, while presence or absence of carbonates reduced model error by 21 and 46 %. Five of the eight combination soilscape and carbonate subset models reduced model error by 14 to 51 %. Overall, subsetting by soilscape or carbonate presence proved effective in improving model performance, with combination subsets proving beneficial under specific calibration set conditions.

The association of body mass index variability with cardiovascular disease and mortality: a mediation analysis of pooled cohorts.

We aimed to investigate the effect of BMI variability on CVD and mortality and to explore the mediation effects of the main cardiovascular risk factors contributing to this association. Participants aged 40-65 years were pooled from three cohort studies(ARIC [Atherosclerosis Risk in Communities], MESA [Multi-ethnic Study of Atherosclerosis], and TLGS [Tehran Lipid and Glucose Study]. We employed root mean squared error of the fractional mixed model to calculate BMI variability in the measurement period. In the event assessment period, the hazard ratios for CVD and mortality were estimated using Cox proportional hazard regression models. In the next step, the mediation and interaction effects of fasting plasma glucose, total cholesterol, and systolic blood pressure were determined. A total of 19073 participants were included in this pooled analysis. During a median of 20.7 years of follow-up, 3900 (20.44%) CVD and 6480 (33.97%) all-cause mortality events were recorded. After adjusting for potential confounders, BMI variability was linked to the 1.3 (1.2-1.4) and 1.7 (1.6-1.8) increased risk of CVD and mortality, respectively. Fasting plasma glucose mediated approximately 24% and 8% of the effect of BMI variability on CVD and mortality, respectively. However, systolic blood pressure and total cholesterol did not have mediation effects in this association. High BMI variability is independently associated with the development of CVD and mortality. This association is partly mediated through fasting plasma glucose. Modern cardiometabolic therapies that lower fasting glucose may reduce the risk of future CVD and mortality in individuals with high BMI variability.

Research on the income and expenditure forecast of long-term care insurance fund in Shihezi City based on community care.

This study forecasts the income and expenditures of the long-term care insurance fund, provides a basis for formulating the raising standard of the long-term care insurance fund, and explores the measures to improve the pilot work of long-term care insurance. By using the exponential smoothing and ARIMA models to forecast the income and expenditure of the old-age care insurance fund in 2022, the problems existing in the operation of the long-term care insurance fund are discussed. In 2022, the income of the old-age insurance fund was 28.8934 million yuan, and the fund compensation expenditure was 28.4070 million yuan, with a slight balance of the fund. The highest relative errors of income and expenditure forecast models are -2.03% and - 2.76%, respectively. According to the results of fund expenditure, the annual financing standard should be 132.93 yuan/person, and the individual financing standard should be 66.47 yuan/person. Through the integration of personal payment, welfare, sports lottery public welfare income, social donations, and other ways, we can gradually establish a multi-channel risk-sharing financing. We will appropriately raise the standard for individual financing and the annual contribution standard for individuals from 50 yuan to 66.47 yuan. This will promote sustainable development of long-term insurance system.

Office building energy consumption forecast: Adaptive long short term memory networks driven by improved beluga whale optimization algorithm

With the development of urbanization, buildings have become a major source of energy consumption. This research uses a data-driven approach to achieve accurate building energy consumption prediction by analyzing and modeling building energy consumption data. The model proposed in this paper uses improved beluga whale optimization algorithm (IBWO) to optimize long short-term memory networks (LSTM) for accurate energy consumption prediction. In order to enhance the ability of BWO in global search and local exploitation, a new method of dynamic adjustment of step factor as well as strategies such as nonlinear decreasing are introduced to improve BWO. For the first time, it is proposed to explore the accuracy of the number of hyper-parameters of LSTM on the prediction of energy consumption, and the improved beluga whale optimization algorithm is used to optimize the two, three, and four hyper-parameters of LSTM respectively. Then short-term prediction of historical energy consumption data of an office building in Xi'a is performed. Experiments show that the optimization of the four hyperparameters of LSTM using the IBWO of this paper can reduce the mean absolute error (MAE) of the pre-improvement model from 830.71 KW to 128.28 KW, the mean absolute percentage error (MAPE) from 12.32 % to 1.38 %, and the coefficient of variation (CV) from 7.5 % to 1.2 %.

A phase field approach to the fracture simulation of quasi-brittle structures with initial state

To solve the simulation problem of crack propagation in solid structures with initial stress, this paper proposes a phase field approach that considers the initial state of the quasi-brittle structure. By using the initial equilibrium iteration method, the estimated initial state of the structure, including initial stress, is transformed into internal stress loads of the structure. Thus, the problem of non-convergence caused by inaccurate initial state estimation of the structure in engineering calculations was solved, and an improved subproblem staggered iteration method was adopted to simulate crack propagation of the structure under external loads. The universality and computational accuracy of this method for different phase field models were verified by using pre made notched thin plates for the expansion of mode I and II cracks in different initial states. Among them, the calculation error using the PF_CZM model is within 1%, and the calculation error of the AT2 model increases with the initial stress, but is less than 7%. At the same time, the crack growth problem of cracked structures under different uniform initial stresses is accurately simulated, which reflects the applicability of this method to actual engineering. In addition, the study also verified the universal applicability of the adaptive refinement strategy proposed based on the AT2 model for other phase field models.

Two-step calibration of 6-DOF industrial robots by grouping kinematic parameters based on distance constraints

The positioning accuracy of industrial robots is critical for their applications, which can be improved through calibration. This paper proposes a two-step calibration method for six-degree-of-freedom (6-DOF) industrial robots to reduce couplings of their modified Denavit-Hartenberg (MD-H) model parameters during optimization processes of kinematic calibration. In this method, a distance error model is established based on the distance constraints, and the MD-H model parameters of the industrial robots are identified group by group using the Levenberg-Marquardt (LM) algorithm. To determine the optimal MD-H model parameter grouping, the different grouping strategies are simulated and analyzed. In the proposed method, the coordinate system transformation between the measurement frame and the robot base frame can be avoided based on the distance error model, thereby reducing the errors introduced by the coordinate system transformation during the calibration process. Finally, the proposed two-step calibration method is verified through an experiment. The experimental result shows that the distance/positioning accuracy on the robot end-effector is improved from 0.602 mm/2.7317 mm to 0.151 mm/0.717 mm after calibration. The presented study may be beneficial for improving performance of industrial robots.

Pooled Population Pharmacokinetic Analysis and Dose Recommendations for Ciprofloxacin in Intensive Care Unit Patients with Obesity.

Recent studies have explored the influence of obesity and critical illness on ciprofloxacin pharmacokinetics. However, variation across the subpopulation of individuals with obesity admitted to the intensive care unit (ICU) with varying renal function remains unexamined. This study aims to characterize ciprofloxacin pharmacokinetics in ICU patients with obesity and provide dose recommendations for this special population. Individual patient data of 34 ICU patients with obesity (BMI >30 kg/m2) from four studies evaluating ciprofloxacin pharmacokinetics in ICU patients were pooled and combined with data from a study involving 10 individuals with obesity undergoing bariatric surgery. All samples were collected after intravenous administration. Non-linear mixed effects modeling and simulation were used to develop a population pharmacokinetic model and describe ciprofloxacin exposure in plasma. Model-based dose evaluations were performed using a pharmacokinetic/pharmacodynamic target of AUC/MIC >125. The data from patients with BMI ranging from 30.2 to 58.1 were best described by a two-compartment model with first-order elimination and a proportional error model. The inclusion of Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) as a covariate on clearance reduced inter-individual variability from 57.3% to 38.5% (P <.001). Neither body weight nor ICU admission significantly influenced clearance or volume of distribution. Renal function is a viable predictor for ciprofloxacin clearance in ICU patients with obesity, while critical illness and body weight do not significantly alter clearance. As such, body weight and critical illness do not need to be accounted for when dosing ciprofloxacin in ICU patients with obesity. Individuals with CKD-EPI >60 mL/min/1.73 m2 may require higher dosages for the treatment of pathogens with minimal inhibitory concentration ≥0.25 mg/L.

How planning intervention changes the “price tags” of property rights? Towards a dynamic understanding of China's small property right housing

While studies of property rights have long gone beyond the legal-illegal dichotomy, how to quantify the varying degree of property rights remains a challenging task. Focusing on a special type of informal housing—Small Property Right Housing (SPRH) in China, this study examines how planning intervention affects the pricing mechanism of SPRH through the theoretical lens of graded property rights. A previous study has unravelled the pricing mechanism of SPRH by quantifying the impact of informal institutions inherent to the SPRH market that give different “price tags” to varying degree of property rights, while not being able to capture the impact of external interventions. Drawing on two SPRH datasets before and after a Master Plan of urban villages conservation announced in 2019, we employ difference in difference (DID) estimations to examine how planning intervention affects SPRH prices by way of altering the strength of informal property rights. We also develop spatial error models (SEM) to account for spatial autocorrelation. This research extends and enriches the robust conceptualisation of graded degree of informal property rights by capturing the changes caused by external intervention to offer a dynamic and more accurate understanding of SPRH pricing mechanism. It also demonstrates that planning can go beyond sanctioning and redlining to tolerate and steer informal development.

Study on flow pattern transition of cuttings transport in extended-reach drilling

Inadequate hole cleaning is one of the major challenges in extended-reach drilling, especially prominent in large-sized hole sections, such as 121/4-in. section. As the drilling parameters including flow rate, ROP and rotational speed change, the cuttings flow pattern converts between fully suspended flow, two-layer flow, and three-layer flow. However, most existing cuttings transport models only study the problem of cuttings transport under a certain flow pattern and do not consider the dynamic conversion of different cuttings flow patterns. Pipe rotation plays a key role in cuttings transport in rotary drilling, but the existing studies have not fully revealed the effect of pipe rotation on the conversion of cuttings transport patterns. In this paper, a stirring diffusion factor is presented to quantify the effect of pipe rotation on cuttings transport, and the mathematical expression of the stirring diffusion factor is obtained through numerical simulations and the nonlinear regression method. Secondly, the critical conditions for the conversion of cuttings flow patterns are obtained by combining mechanical analysis and numerical simulation results, clarifying the applicable scope of different cuttings flow patterns. According to the comparison with experimental results, the prediction error of the new model considering the conversion of cuttings flow patterns is not higher than 11.1%, and the minimum prediction error is only 3.07%. Thirdly, the flow pattern maps for cuttings transport under different drilling parameters are drawn to provide guidance for the identification of cuttings transport pattern and optimization of drilling parameters. Finally, the flow pattern maps are applied to a case study of an extended-reach drilling. The results show that there is a delay effect in the conversion of cuttings flow patterns due to the nonlinear characteristics of annular geometry. Furthermore, the effects of flow rate, ROP and rotational speed on cuttings transport patterns are not independent, but mutually reinforcing and constraining each other. Inappropriate selection of drilling parameters will result in three-layer flow of cuttings transport, and then pipe sticking is likely to occur. The cuttings transport pattern can be converted from three-layer flow to two-layer flow by optimizing drilling parameters, which can significantly improve the hole cleaning effect.

Landing cushioning analysis of new generation heavy airborne armored vehicles

PurposeThe purpose of this study is to ensure the successful implementation of a landing cushion for the new generation armored vehicles with significantly enhanced quality. Furthermore, to introduce a high-precision landing cushioning analysis model.Design/methodology/approachTo accurately analyze the cushioning performance of the new generation armored vehicles, a nonlinear finite element dynamics model considering the complex travel system was established. The model considered the influence of various nonlinear factors to measure the dynamic response difference between the proposed and traditional models. The cushioning performance of airbags under different landing conditions and their various influence factors were analyzed.FindingsThe travel system has a large influence on the key points of the vehicle, whose rear end of the upper deck has a larger acceleration fluctuation compared with the traditional model. The increase in the body material stiffness is helpful to reduce this fluctuation. The established nonlinear finite element model can effectively analyze the landing cushioning performance of airborne armored vehicles. The area of the external airbag vent has a large influence on the cushioning performance, and the cushioning system has excellent cushioning performance under various operating conditions.Practical implicationsThis study introduces the travel system, which is ignored by traditional analytical models. The interactions between various types of complex structures are included in the analysis process in its entirety, leading to valuable new conclusions. Quantitatively reveals the analytical errors of traditional simulation models in multiple dimensions and the reasons for their formation. Based on a high-precision simulation model, it is verified that the designed airbag cushioning system has an excellent cushioning effect for the new generation of heavy airborne armored vehicles.Originality/valueThe novelty of this work comes from the need for smooth landing with low overload for a new type of large-load airborne armored vehicle and provides a high-precision model that quantifies the traditional analytical modeling errors and error principle.

Analysis and diagnosis of abnormal SLR validation results for BeiDou-3 SECM-B MEO C225 and C226 satellite orbits

Satellite laser ranging (SLR) is the only stand-alone and external tool for verifying the accuracy of microwave-based GNSS orbits. On February 2, 2023, the International Laser Ranging Service initiated the SLR global tracking campaign for the complete BeiDou-3 medium Earth orbit (MEO) constellation. This initiative provides an opportunity to unveil the potential issue for BeiDou-3 MEO orbit modeling. An examination of the SLR validation results of precise BeiDou-3 MEO orbits across different IGS/Multi-GNSS Experiment (MGEX) analysis centres (ACs) reveals that the SLR residual standard deviation for BeiDou-3 SECM-B MEOs C225 and C226 orbits reaches 18 cm. This figure is approximately four to nine times those for other BeiDou-3 MEOs. This contradicts the fact that the orbit quality of C225 and C226 is comparable to other satellites. By analyzing the correlation between SLR residuals and de-trend clock residuals, detector-specific bias performance, and the distribution characteristics of SLR residuals relative to satellite azimuth and nadir angles, we diagnose that abnormal SLR validation results for C225 and C226 originate from a large deviation in the official horizontal offsets for the laser retroreflector arrays (LRAs) in the satellite reference frame. LRA horizontal offsets for C225 and C226 are estimated based on SLR residuals. With our adjusted LRA offset estimates, the SLR residual metrics for C225 and C226 orbits (across various IGS/MGEX ACs) are comparable to the results obtained for other BeiDou-3 MEOs. Additionally, our adjustments enable the identification of their orbit modeling errors across different IGS/MGEX ACs according to the pattern of SLR residual variation. From the study, spacecraft manufacturers should be encouraged to conduct a comprehensive review of ground calibrations for BeiDou LRA offsets. This is crucial for optimizing BeiDou orbit model and enhancing its applications in high-precision geosciences.

Estimation of wind load on supertall buildings using partial output measurements

Supertall buildings possess distinctive characteristics such as high flexibility, low damping, and susceptibility to wind. The impact of the wind load and wind-induced responses plays a critical role in ensuring the safety and functionality of these structures. Consequently, investigating the wind load and wind-induced responses of supertall buildings has immense practical significance and engineering value to guarantee the secure operation of such buildings under strong wind conditions. However, the direct measurement of wind loads and wind-induced responses across all building floors presents substantial challenges, including considerable engineering workload and implementation difficulties. This study proposes a novel approach based on Kalman filtering to estimate wind loads and wind-induced responses in supertall buildings. This approach involves fusing partial acceleration and displacement measurements to estimate wind load and wind-induced response accurately. Additionally, optimized sensor placement was considered based on the iterative process of the effective independence method. Wind-induced responses were obtained through numerical testing and subsequently utilized to estimate the wind loads on a supertall building. Various factors, such as measurement noise, modeling errors of structural dynamic properties, sensor placement, and response synchronization, were thoroughly discussed to assess their impact on the overall analysis.

Similarity Measures between Buying and Selling Prices of Various Exchange Rates with the inclusion of Multidimensionality and Multicollinearity

This paper introduces a new statistical model, the multidimensional measurement error model with multicollinearity, to study the relationship between buying and selling prices of foreign exchange rates of various currencies. Such a model is needed due to the possible rise in multidimensionality and multicollinearity issues that may occur due to the movement of the financial markets towards stock market integration of various countries since the occurrences of the financial crisis as well as the part result of globalization. As this integration involves the participation of various countries, it will affect the foreign exchange rate. Hence, the analyses are performed on seven currencies against the Malaysian Ringgit where four models’ performances are compared. From this research, it can be concluded that the proposed model comparatively performs better than the other models in representing the relationship of the stationary prices and performs as well as existing models towards non-stationary prices. It can also be seen that the Japanese Yen is the currency that has the strongest influence and closer similar trends towards other currencies while the Great British Pound showed otherwise.