Critical Performance Indicators Research Articles

The Effects of Imbalanced Datasets on Machine Learning Algorithms in Predicting Student Performance

Predictive analytics technologies are becoming increasingly popular in higher education institutions. Students' grades are one of the most critical performance indicators educators can use to predict their academic achievement. Academics have developed numerous techniques and machine-learning approaches for predicting student grades over the last several decades. Although much work has been done, a practical model is still lacking, mainly when dealing with imbalanced datasets. This study examines the impact of imbalanced datasets on machine learning models' accuracy and reliability in predicting student performance. This study compares the performance of two popular machine learning algorithms, Logistic Regression and Random Forest, in predicting student grades. Secondly, the study examines the impact of imbalanced datasets on these algorithms' performance metrics and generalization capabilities. Results indicate that the Random Forest (RF) algorithm, with an accuracy of 98%, outperforms Logistic Regression (LR), which achieved 91% accuracy. Furthermore, the performance of both models is significantly impacted by imbalanced datasets. In particular, LR struggles to accurately predict minor classes, while RF also faces difficulties, though to a lesser extent. Addressing class imbalance is crucial, notably affecting model bias and prediction accuracy. This is especially important for higher education institutes aiming to enhance the accuracy of student grade predictions, emphasizing the need for balanced datasets to achieve robust predictive models.

SBAC-SDN: A Scalable Blockchain-based Access Control in Northbound Interface for Multi-Controller SDN with Load Balancing Mechanism

Abstract The rapid adoption of Software-Defined Networking (SDN) has not only revolutionized network management but also presented challenges in scalability and security, particularly in multi-controller environments. This paper introduces a load balancing technique aimed at enhancing the scalability of the Northbound interface, a critical component often susceptible to performance bottlenecks. The experimental setup utilized Mininet and Ryu to create a simulated multi-controller SDN environment, where SBAC-SDN was thoroughly evaluated based on CPU and memory usage, response times, and error rates. The results showed substantial improvements in critical performance indicators, confirming the effectiveness of the load balancing technique in addressing scalability challenges. The findings indicate that the system can be scaled without excessive resource consumption, as CPU usage peaked at 60.3% and memory usage remained below 8 GB even with eight controllers. The stable average response times were consistently below 1 s, and the high throughput, ranging from 4.34 to 4.67 requests per second, further demonstrates the efficiency of the system. The error rate remained 0.0% across all configurations, highlighting the robustness of the implemented security measures. This study contributes to ongoing efforts to improve the scalability and overall robustness of SDNs, aligning with the broader goals of reliability and efficiency in network management.

Potential site evaluation of fifth-generation district heating and cooling networks under uncertain energy market conditions: A two-step robust design analysis approach

In a fifth-generation district heating and cooling (5GDHC) network, energy demands are satisfied without using fossil fuel-based sources, rather by sharing participants’ energy loads through decentralized consumption and production. However, finding such locations where the complementary energy demands of participants are matched is difficult. The problem becomes more complicated due to uncertain energy markets (equipment investment and operational costs). These uncertain market trends make it difficult to decide whether to build the network. Finding the feasible network temperatures for the 5GDHC network is challenging due to the conflicting participants, as at one-time instance some need cooling and others need heating. The sensitivity analysis using Sobol’ indices is performed to find the most influencing input parameters. Then, a techno-economical assessment of the considered location is performed against influencing inputs. Two different sites Diest and Alpha Cloud (Flanders, Belgium) are evaluated. The systematic techno-economic analysis under the influence of an uncertain energy market and network operating temperatures reveals that building a network might be not feasible due to the high payback period. The methodology offers greater flexibility in identifying key uncertain input parameters and evaluating the robustness of critical performance indicators, overcoming the limitations of current GIS-based approaches. This improved capability assists decision-makers in more accurately determining feasibility.

EXAMINING THE INTEGRATION OF INDUSTRY 4.0 TECHNOLOGIES IN MANUFACTURING

The integration of Industry 4.0 technologies is transforming manufacturing, enhancing efficiency, flexibility, and sustainability. This research paper explores the application of key technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data analytics in the sector. By analyzing a comprehensive dataset that includes supply chain metrics from major corporations like Apple, Amazon, and Google, the study examines the impact of Industry 4.0 practices on critical supply chain performance indicators, including inventory turnover ratio, lead time, and customer satisfaction. Industry 4.0 technologies are reshaping supply chain management (SCM) strategies such as lean manufacturing, agile SCM, and cross-docking. AI and block chain are shown to significantly enhance supply chain agility and resilience. Companies that integrate these technologies tend to have more streamlined operations, shorter lead times, and higher customer satisfaction rates. Environmental sustainability practices are becoming increasingly important as businesses strive for more eco-friendly manufacturing processes. The study finds that organizations employing agile supply chain practices and leveraging advanced technologies often demonstrate superior operational efficiency and financial performance. The integration of these technologies also introduces challenges, such as increased supply chain complexity and risk management concerns. This paper provides a comprehensive overview of how Industry 4.0 technologies are revolutionizing manufacturing and supply chain dynamics. It highlights the future trajectory of smart manufacturing and emphasizes the need for continued innovation to address emerging challenges. The research offers insights into how companies can adapt their supply chain strategies to thrive in the evolving landscape of digital transformation and automation.

A Novel Method for Monitoring Notified Bodies Designated under The European Union Medical Device Regulation

Notified bodies, which perform conformity assessments, play a crucial role in protecting patient health and providing access to safety products on the market. The EU 2017/745 Medical Device Regulation (MDR) brings stricter rules and responsibilities for notified bodies. Designating authorities (DAs), who are also responsible for monitoring notified bodies, have not been provided with any guidance documents or written procedures. In this study, for the first time, we proposed a methodology aided by a digital system to monitor notified bodies effectively. We conducted a need analysis based on the MDR requirements and the relevant guidance documents, and we introduced a six-component technique for monitoring of the medical device notified bodies. Then, we identified the subcriteria of each component and created business activity diagrams for the main processes to monitor the notified bodies. There are now forty-eight notified bodies available under the MDR. Our monitoring approach consists of six steps that cover all NB-related activities, such as review of technical documentation assessment, personnel authorization, and surveillance of the certified product on the market. The proposed system complies with the MDR requirements and handles all critical performance indicators of NBs. The new MDR requirements for NBs also require an advanced monitoring system for DAs. This study focused on the critical points for monitoring NBs. Member states should implement the proposed methodology and the activity diagrams to have an efficient monitoring system in accordance with MDR requirements. A similar system can be used for monitoring of the other conformity assessment bodies.

Predictive modeling of combined cycle power plant performance using a digital twin-based neural ODE approach

This study presents a novel digital twin-based predictive modeling approach to enhance the operation, maintenance, and management of combined cycle power plants. The research aimed to accurately forecast the combined cycle power (CCP) output, a critical performance indicator, to support intelligent decision-making for plant operators and engineers. The proposed framework leverages a neural ordinary differential equation (NODE) model integrated within a digital twin architecture to capture the complex, nonlinear dynamics of combined cycle gas turbine unit (CCGU) system. The predictive model was trained and validated using multivariate time-series data collected from a CCGU, achieving high accuracy with an R2_score of 0.993, a mean absolute percentage error of 0.325 %, and a root mean squared error of 1.518. Importantly, the NODE model demonstrated superior forecasting capabilities compared to traditional machine learning techniques. The digital twin (DT) concept enables seamless real-time data integration, physics-based models, and advanced data analytics to facilitate comprehensive CCGU lifecycle management. This novel approach provides operators with reliable, real-time insights to optimize plant performance, reduce maintenance costs, and improve overall sustainability. The generalization of the NODE model to an independent CCGU unit validated its applicability across diverse power plant configurations, highlighting the originality and practical value of the research. The key contribution of this work is the development of a digital twin-based predictive modeling framework centered on the innovative use of neural ordinary differential equations to enhance the operation and maintenance of critical energy infrastructure. This research advances the state-of-the-art in intelligent asset management for the built environment.

Bond performance and constitutive model of steel bar in G-UHPC under cyclic loading

The increasing embrace of the low-carbon and environmentally sustainable construction approach has opened exciting opportunities for using environmentally friendly geopolymer based ultra-high-performance concrete (G-UHPC) in engineering structures, drawing significant attention to its performance. Seismic performance is a crucial factor in structural design, and it is greatly affected by the bond behavior between steel bar and concrete. As a result, a thorough understanding of the bond behavior of steel bars to G-UHPC provides essentially theoretical support for the structural design and nonlinear finite element analysis of G-UHPC structures, which is vital for their widespread implementation in earthquake engineering. This study tested 54 specimens from 18 groups to examine the bond performance of G-UHPC and steel bars under cyclic loading. The influence of concrete and steel bar types, protective layer thickness, bond length, and steel fiber on critical bond performance indicators were evaluated, and the bond stress-slip hysteretic and skeleton curves were analyzed. Subsequently, based on the experimental data, a constitutive model was proposed to predict the bond stress-slip relationship of steel bars and G-UHPC under cyclic loading. The results indicated that the steel bars exhibited superior bond performance with G-UHPC as compared to NSC. The bond and residual strength between steel rebars and G-UHPC was approximately 1.73 times and 3 times greater, respectively, in comparison with that between NSC and steel rebars. Increasing the strength of steel bars only resulted in a limited 2 % improvement in bond strength. The steel bar diameter, protective layer thickness, bond length, and the dosage and length of steel fibers in G-UHPC significantly influenced the bond performance. The addition of irregular-shaped steel fibers to G-UHPC has demonstrated potential in improving energy consumption, with the most remarkable enhancement observed for the inclusion of twisted steel fibers. Specifically, the incorporation of twisted steel fibers signally enhanced the energy consumption to approximately 1.3 times the energy dissipation as compared to straight steel fibers with the same dosage and aspect ratio. In addition, the accuracy of the proposed constitutive model has been validated.

Pork meat production: Proposal for monitoring indicators based on life cycle assessment

This study carried out a Life Cycle Assessment (LCA) of the pork production process based on a detailed Life Cycle Inventory (LCI) of inputs and outputs of 3 years of the process, from the entrance of the live animal to the final packaged products. The Life Cycle Impact Analysis (LCIA) was carried out using the Environmental Footprint method to observe which impact categories are most relevant in environmental terms, excluding impacts related to production of the living animal. The results of the LCIA made it possible to identify the most relevant impact categories, such as Acidification, Climate change, Freshwater ecotoxicity, Freshwater and Marine eutrophication, Resource and Water use. The impacts detected supported the definition of critical Key Performance Indicators (KPI) and Process Performance Indicators (PPIs) in environmental aspects, establishing a cause-and-effect relationship with the organization's strategic objectives. A model for monitoring environmental impacts with established indicators was developed based on the LCA study of the pork processing. The proposed monitoring indicators were identified based on the environmental impacts and this industrial sector can utilize these findings to adopt a more sustainable process.

Analyzing Factors Influencing the Development of Railway Freight Transportation Service through the Fuzzy Analytic Hierarchy Process Technique

The main objective of this study is to apply the Fuzzy Analytic Hierarchy Process (FAHP) technique to assess factors influencing the development of railway freight transportation services. These factors include four critical performance indicators within the supply chain: responsiveness, cost, reliability, and agility. Moreover, the study aims to develop guidelines for enhancing the effectiveness of railway freight transportation in Chiang Rai province, particularly through the Den Chai–Chiang Rai–Chiang Khong railway route. Data were collected from 9 experts, including logistics service providers, users, and academics then, pairwise comparisons of factors were conducted. The results revealed that the cost factor holds the highest significance at 33.86%, followed by reliability, responsiveness, and agility factors at 28.51%, 26.14%, and 11.49%, respectively. Consistent with satisfaction scores obtained from logistics service users, the cost factor holds the highest satisfaction score at 4.78, followed by reliability, responsiveness, and agility factors with scores of 4.41, 4.16, and 4.01, respectively. The result above will bring towards preparation in railway freight transportation of Chiang Rai province and encouraging entrepreneurs to shift to railway transportation mode in the end.

Approach for predicting and adjusting the pointing accuracy of opto-mechanical systems considering multi-source uncertainty.

Pointing accuracy is a critical performance indicator of opto-mechanical systems, directly affecting the systems' efficiency and application range. This study introduces what we believe to be a novel approach for predicting pointing accuracy and adjusting processes in opto-mechanical systems, considering multi-source uncertainty quantification. First, the relationship between error components and total error is quantified using homogeneous coordinate transformation theory. Second, by applying the Nataf transformation to uncertain variables, a hybrid interval-probabilistic uncertainty quantification model based on generalized polynomial chaos is constructed. Third, by selecting points from the probability distribution domain, a parameterized finite element simulation is conducted to create a pointing accuracy prediction model, obtaining the theoretical limit accuracy for the opto-mechanical system. Finally, considering multi-bolt elastic interactions, an assembly process adjustment model is developed to achieve performance-based assembly process adjustments, and tests are conducted to measure the pointing accuracy of the opto-mechanical system after calibration. Pointing accuracy measurements following calibration showed an improvement from 249" to 117", an increase of 53.01%, approaching the theoretical limit of 108". This approach requires only one adjustment to approach optimal accuracy compared to eight adjustments with traditional methods, greatly enhancing assembly efficiency. This study offers a theoretical foundation for predicting and adjusting pointing accuracy in opto-mechanical systems.

Service-level computation in time-varying queueing system with priorities: Application to physician staffing in the emergency department

For many service systems, waiting time is a critical performance indicator of customer experience. This study addresses service-level computation techniques in a time-varying mixed-preemptive priorities queueing system. This queueing system has two distinguishing characteristics. First, it has time-varying arrival rates and service capacities. Second, and more importantly, there are multiple classes of customers, and both preemptive and non-preemptive priorities are considered in this system. Combining these two characteristics in a queueing system makes it difficult to compute the service level for each customer class. We derive the exact computation methods for pointwise and piecewise service levels. Furthermore, we propose closed-form expressions to approximate the service levels. Lastly, we show how to apply these service-level computation techniques to a real-life physician staffing problem in the emergency department, in which the service levels of each class of patients are considered as constraints. The computational tests are performed using data generated based on real data from a large hospital. The results show that our staffing solutions are better than the hospital’s current staffing solution, guaranteeing patients’ service levels and reducing physicians’ working times.

Lantern-inspired capacitive pressure sensor with wide linear measuring range

In the field of pressure sensing, high sensitivity and linearity are two critical performance indicators, but striking a balance between them remains a prominent challenge for sensors. Existing solutions typically involve the structural design of electrodes or dielectric layers, often requiring complex and expensive techniques for designing microstructures. This study presents a novel flexible capacitive pressure sensor characterized by its low-cost nature, ease of manufacture, and scalability in design. The lantern accordion-like structure inspires the design of the dielectric layer and the porous structure is designed using thermally expandable microspheres (TEM). The synergy of surface microstructures and internal microstructures results in a high sensitivity of 3.07 × 10−3 kPa−1 within a wide linear response range of 0–100 kPa. The developed sensor is also evaluated for its weight detection capabilities, highlighting its promising weight and pressure data acquisition.

Uncertainty quantification of exchange efficiency in membrane-based air-to-air energy exchanger cores: Repetitive manufacturing and experiments

Air-to-air membrane-energy exchangers have been widely used in green buildings as passive energy-recovery technologies for energy conservation and carbon reduction. An air-to-air membrane-energy exchanger conserves building energy by forcing the indoor exhaust air and unconditioned fresh air to exchange heat and moisture. As a critical performance indicator, the energy-exchange efficiency is primarily determined by the core. To improve energy-saving performance, many studies have been conducted to analyze and enhance the efficiency of air-to-air membrane-energy exchanger cores by manufacturing, testing, modeling, and comparing different cores. However, few studies have investigated the uncertainty introduced by the manufacturing and testing processes (most related research only studied the uncertainty of the sensible efficiency), which means that some positive results of those controlled trials are possibly owing to the manufacturing tolerance or testing errors, rather than real technical progress. This study addresses this problem by quantifying the efficiency uncertainty caused by manufacturing and testing processes. Three cores were produced using the same manufacturing process and materials, followed by identical experimental tests on their sensible, latent, and enthalpy efficiencies. Comparison results suggest that even when all variables are controlled, the differential enthalpy efficiency of two “identical” cores still vary from 0 % to 2 %. Variance of repetitive testing results of a certain core can afford an enthalpy efficiency of 4 % owing to a non-ideal experimental procedure. When manufacturing tolerances and experimental errors are considered simultaneously, the differential sensible efficiency among multiple “identical” cores is within 0–5%, whereas latent efficiency data in winter shows a significant variation of 5–10 % for “identical” cores. The contributions and conclusions of this study are as follows. The efficiency uncertainty caused by manufacturing and testing processes is non-negligible, particularly in latent efficiency. This phenomenon could alert researchers and engineers in this field to conduct repetitive manufacturing and testing while trying to modify/test/compare air-to-air membrane-energy exchanger cores. Because this study is based on a limited number of cores, the quantitative results of this study should be considered a non-universal reference, and a systematic benchmark must be established in the future.

Game-Based Vehicle Strategy Equalization Algorithm for Unsignalized Intersections

To address the coordination issue of connected autonomous vehicles (CAVs) at unsignalized intersections, this paper proposes a game-theory-based distributed strategy equalization algorithm. To begin, the vehicles present in the scene are conceptualized as participants in a game theory. The decision-payoff function takes into account three critical performance indicators: driving safety, driving comfort, and driving efficiency. Then, virtual logic lines connect the front and rear extremities of vehicles with odd and even numbers at the intersection to create a virtual logic ring. By dividing the virtual logic ring into numerous overlapping game groups, CAVs can engage in negotiation and interaction within their respective game groups. This enables the revision of action strategies and facilitates interaction between the overlapping game groups. A further application of the genetic algorithm (GA) is the search for the optimal set of strategies in constrained multi-objective optimization problems. The proposed decision algorithm is ultimately assessed and certified through a collaborative simulation utilizing Python and SUMO. In comparison to the first-come, first-served algorithm and the cooperative driving model based on cooperative games, the average passing delay is decreased by 40.7% and 6.17%, respectively, resulting in an overall improvement in the traffic system’s passing efficiency.

Parallel Processing Impact on Random Forest Classifier Performance: A CIFAR-10 Dataset Study

Using the CIFAR-10 dataset, this research investigates how parallel processing affects the Random Forest method's machine learning performance. Accuracy and training time are highlighted in the study as critical performance indicators. Two cases were studied, one with and one without parallel processing. The results show the strong prediction powers of the Random Forest algorithm, which continues to analyze data in parallel while retaining a high accuracy of 97.50%. In addition, training times are notably shortened by parallelization, going from 0.6187 to 0.4753 seconds. The noted increase in time efficiency highlights the importance of parallelization in carrying out activities simultaneously, which enhances the training process's computational efficiency. These results provide important new information about how to optimize machine learning algorithms using parallel processing approaches.

Analysis Of Ship Main Engine Procurement Process In The Shipyard Industry Using Supply Chain Operation Reference Method (SCOR) (Case Study: PT XYZ)

As a sector playing a crucial role in ship construction and repair, the shipbuilding industry faces significant challenges in material procurement, where materials constitute 50-70% of the total project cost. One of the materials requiring paramount attention is the ship's main engine. Effective supply chain management is critical to enhancing efficiency and effectiveness in the procurement process of ship engines. However, supply chain performance measurement research, especially in ship engine procurement, still needs to be improved. Therefore, this study aims to identify relevant performance metrics, analyze critical performance indicators, and provide recommendations based on the SCOR (Supply et al.) framework to improve ship engine procurement. The research results present 24 metrics distributed into four performance attributes based on SCOR 12.0, with 22 selected metrics aligned with the company's context. Analysis using the AHP method indicates that the reliability attribute has the highest weight (0.296), with the metric "Percentage of procurement received on time as per request" being the top priority (weight 0.143). This study provides a holistic view of crucial aspects of the ship engine procurement supply chain, supporting companies in enhancing their performance in the procurement process.

Feedback Beamforming in the Time Domain.

Real-time source localization is crucial for high-end automation and artificial intelligence (AI) products. However, a low signal-to-noise ratio (SNR) and limited processing time can reduce localization accuracy. This work proposes a new architecture for a time-domain feedback-based beamformer that meets real-time processing demands. The main objective of this design is to locate reflective sources by estimating their direction of arrival (DOA) and signal range. Incorporating a feedback mechanism in this architecture refines localization precision, a unique aspect of this approach. We conducted an in-depth analysis to compare the effectiveness of time-domain feedback beamforming against conventional time-domain methods, highlighting their benefits and limitations. Our evaluation of the proposed architecture, based on critical performance indicators such as peak-to-sidelobe ratio, mainlobe width, and directivity factor, demonstrates its ability to improve beamformer effectiveness significantly.

Energy, exergy and exergo-economic analysis of a novel HT-PEMFC based cogeneration system integrated with plasma gasification of food waste

Landfill and incineration of food waste would result in a significant waste of resources. Plasma gasification, as a novel solid waste treatment technology with cleaner product, is suitable for this perishable kitchen waste with high water and oil content. In present research, a food waste plasma gasification driven HT-PEMFC cogeneration system is proposed. Based on the established models, a comprehensive study is conducted from the perspective of energy, exergy and exergo-economy. Effects of key operational parameters on critical performance indicators are investigated. Results show that current density imposes an obvious effect on the cogeneration efficiency, this efficiency is obtained as 0.523–0.457 at low current density varying from 0.1 to 0.4 A/cm2. Exergy analysis results indicate that the plasma gasifier and the stack have the largest exergy destructions, accounting for 62.1 % and 32.2 % of the total, respectively. Gasification temperature has a great effect on the system exergy efficiency which is 0.603–0.384 when this temperature increases from 1000 K to 1300 K. Current density also has an obvious effect on the exergo-economic performance, the exergy cost per unit is obtained as 46.1–51.8 $/GJ when current density varies from 0.2 to 0.4 A/cm2. The optimization results are obtained as cogeneration efficiency of 0.4636, exergy efficiency of 0.6125 and exergy cost per unit of 24.58 $/GJ. This research provides a novel solution for substantial treatment of food waste, and guidelines for sustainable development of efficient and clean utilization technology of municipal solid waste.

Sustainable Optimizing Performance and Energy Efficiency in Proof of Work Blockchain: A Multilinear Regression Approach

The energy-intensive characteristics of the computations performed by graphics processing units (GPUs) in proof-of-work (PoW) blockchain technology are readily apparent. The optimization of GPU feature configuration is a complex subject that significantly impacts a system’s energy consumption and performance efficiency. The primary objectives of this study are to examine and improve the energy consumption characteristics of GPUs, which play a crucial role in the functioning of blockchains and the mining of cryptocurrencies. This study examines the complex relationship between GPU configurations and system architecture components and their effects on energy efficiency and sustainability. The methodology of this study conducts experiments involving various GPU models and mining software, evaluating their effectiveness across various configurations and environments. Multilinear regression analysis is used to study the complex relationships between critical performance indicators like power consumption, thermal dynamics, core speed, and hash rate and their effects on energy efficiency and performance. The results reveal that strategically adjusting GPU hardware, software, and configuration can preserve substantial energy while preserving computational efficiency. GPU core speed, temperature, core memory speed, ETASH algorithms, fan speed, and energy usage significantly affected the dependent computational-efficiency variable (p = 0.000 and R2 = 0.962) using multilinear regression analysis. GPU core speed, temperature, core memory speed, fan speed, and energy usage significantly affected efficient energy usage (p = 0.000 and R2 = 0.989). The contributions of this study offer practical recommendations for optimizing the feature configurations of GPUs to reduce energy consumption, mitigate the environmental impacts of blockchain operations, and contribute to the current research on performance in PoW blockchain applications.

Kinematic Calibration for the 3-UPS/S Shipborne Stabilized Platform Based on Transfer Learning

The three-degrees-of-freedom (3-DOF) parallel robot is commonly employed as a shipborne stabilized platform for real-time compensation of ship disturbances. Pose accuracy is one of its most critical performance indicators. Currently, neural networks have been applied to the kinematic calibration of stabilized platforms to compensate for pose errors and enhance motion accuracy. However, collecting a large amount of measured configuration data for robots entails high costs and time, which restricts the widespread use of neural networks. In this study, a “transfer network” is established by combining fine-tuning with a Back Propagation (BP) neural network. This network takes the motion transmission characteristics inherent in the ideal kinematic model as prior knowledge and transfers them to a network trained based on the actual poses. Compared with the conventional BP neural network trained by actual poses alone, the transfer network shows significant performance advantages, effectively solving the problems of low prediction accuracy and weak generalization ability in the case of small-sample measured data. Considering this, the impact pattern of the sample number of the actual pose on the effectiveness of transfer learning is revealed through the construction of multiple transfer network models under varying sample numbers of the actual pose, providing valuable marine engineering guidance. Finally, simulated sea-service experiments were conducted on the 3-UPS/S shipborne stabilized platform to validate the correctness and superiority of the proposed method.