Average Method Research Articles

Analisis Peramalan Penjualan untuk Perencanaan Produksi pada Lavanda Brownies Karawang

This research was conducted at Lavanda Brownies Karawang which compared the moving average and exponential smoothing forecasting methods. This research aims to find out, calculate, analyze and explain how to use the moving average and exponential smoothing methods in sales forecasting for production planning at Lavanda Brownies Karawang as well as to find out, calculate, analyze and explain what forecasting method is most appropriate to use for Lavanda Brownies Karawang based on calculations of Mean Absolute Devitiation (MAD), Mean Square Error (MSE), and Mean Absolute Percent Error (MAPE). This research uses a quantitative descriptive method, to get a general picture of the company's condition, a literature review related to the research topic is carried out as well as field research through interviews and direct observation. Based on the research results, the exponential smoothing forecasting method based on alpha 0.9 is the best forecasting method for production planning. This method was chosen because it has the smallest MAD, MSE and MAPE accuracy values ​​when compared to other methods, namely with MAD of 489,909, MSE of 506.950 and a MAPE percentage of 14,14%, which percentage has the smallest value and is included in the category Good. A smaller MAPE accuracy value indicates more precise forecasting. Because in forecasting the smaller the accuracy value, the better the forecasting results so that the Lavanda Brownies business can plan optimal steamed brownie production activities in the next period.

MODELING TOURIST ARRIVALS IN THE ISLAND GARDEN CITY OF SAMAL: IMPLICATIONS TO BUSINESS OPERATIONS OF TOURISM-RELATED INDUSTRY

<p>This study aimed to model the tourist arrivals in the Island Garden City of Samal (IGaCoS) by utilizing the autoregressive integrated moving average (ARIMA) method. Specifically, this paper sought to determine whether there was a statistically appropriate forecasting model using the ARIMA approach, based on which it would be possible to reasonably forecast the number of tourist arrivals in IGaCoS. Quantitative secondary data were analyzed by employing the Box-Jenkins ARIMA. The time series plot was characterized by an apparent increasing trend combined with intermittent sinusoidal oscillations. The time series revealed a steady, gradual increase up until 2020. Next, a sharp decline was observed midway through 2020, indicative of the ill effects of the COVID-19 pandemic. A gradual increase was noted once more, starting in 2021, with some fluctuations. The Augmented Dickey-Fuller (ADF) test revealed that the time series was non-stationary at level— but was made stationary after differenced twice. The ARIMA (1,2,1) model was found to be statistically significant. The fitted model initially showed some notable deviations from the original data, but, with time, converged closely to the actual values, proving it is a reliable way to represent underlying patterns. The ARIMA (1,2,1) model, selected for having the lowest AIC and BIC values and a MAPE within the acceptable range for reasonable forecasting, was used to predict tourist arrivals in IGaCoS over the next six months. The forecasted values suggested a mix of fluctuations and stability. There was a noticeable upward trajectory at the beginning, which was followed by a slight decline at the end that yet remained stable. The forecasted results from the ARIMA (1,2,1) model may provide businesses with a roadmap for comprehensive planning in terms of operational and strategic concerns. This study helped address Sustainable Development Goal (SDG) No. 8, Decent Work and Economic Growth.</p><p><strong>JEL:</strong> L83, R11, C53</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0776/a.php" alt="Hit counter" /></p>

Landslide Displacement Prediction Stacking Deep Learning Algorithms: A Case Study of Shengjibao Landslide in the Three Gorges Reservoir Area of China

Computational models enable accurate, timely prediction of landslides based on the monitoring data on-site as the development of artificial intelligence technology. The most existing prediction methods focus on finding a single prediction algorithm with excellent performance or an integrated and efficient hyperparameter optimization algorithm with a highly accurate regression prediction algorithm. In order to break through the limitation of generalization of prediction models, this paper proposes an ensemble model that combines deep learning algorithms, with a stacking framework optimized with the sliding window method. Multiple deep learning algorithms are set as the first layer of the stacking framework, which is optimized with the sliding window method to avoid confusion in the time order of datasets based on time series analysis. The Shengjibao landslide in the Three Gorges Reservoir is used as a case study. First, the cumulative displacement is decomposed into a trend and a periodic term using a moving average method. A single-factor and a multi-factor superposition model based on multiple deep learning algorithms are used to predict the trend and periodic term of the displacement, respectively. Finally, the predicted values of the trend and periodic terms are added to obtain the total predicted landslide displacement. For monitoring point ZK2-3, the values of RMSE and MAPE of the total displacement prediction with the stacking model are 15.93 mm and 0.54%, and the values of RMSE and MAPE of the best-performing individual deep learning model are 20.00 mm and 0.64%. The results show that the stacking model outperforms other models by combining the advantages of each individual deep learning algorithm. This study provides a framework for integrating landslide displacement prediction models. It can serve as a reference for the geological disaster prediction and the establishment of an early warning system in the Three Gorges Reservoir Area.

Psychometric Measurements of the Adherence to Refills and Medication Scales Among Patients With HIV/AIDS in Indonesia

ObjectiveThis study aimed to conduct a psychometric evaluation of the Adherence to Refills and Medications Scale (ARMS) among patients with HIV/AIDS in Indonesia. MethodsPsychometric analysis was conducted at 2 hospitals and 7 public health centers at the voluntary counseling and testing clinic. Content validity was measured by assessing the relevance and clarity of each ARMS item. Construct validity was also assessed. Reliability was evaluated using internal consistency and test-retest reliability. ResultsThis study involved 11 experts in the assessment of the content validity and 240 participants in the estimation of the construct validity. All ARMS items were generally considered easy to understand and relevant, with scale-level content validity index based on the average method (S-CVI/Ave) of 0.9 (>0.78) and item-level content validity index (I-CVI) in the range from 0.5 to 1 for the relevance level and S-CVI/Ave of 0.95 (>0.78) and I-CVI in the range from 0.8 to 1 for the clarity level. Two items (numbers 2 and 3) were revised based on experts’ suggestions to enhance comprehension. Confirmatory factor analysis supported 2 subscales: adherence to taking medications and adherence to refilling prescriptions. Good reliability was supported by internal consistency (Cronbach’s α 0.793) and test-retest reliability (intraclass correlation coefficient 0.722) for the overall adherence score. ConclusionsThe Indonesian version of ARMS is a valid and reliable medication adherence scale when used in Indonesian patients with HIV/AIDS.

Machine learning approach for detection of land subsidence induced by underground coal fire using multi-sensor satellite data

ABSTRACT High-rank coal reserves in Jharia Coalfield (JCF, India), are invariably associated with underground coal fires and land subsidence. This study explores multi-sensor time series satellite data (Landsat 8 OLI and Sentinel-1) through machine learning (ML) to determine the regional ground deformation accompanying coal fires and their contextual relationship. The results show that the highest degree of subsidence is closely associated with the active mine benches with overburden dumps. The relationship between the coal fire and land subsidence parameters is considered as a binary classification problem, explored by calculating the probability of subsidence with a desirable categorical outcome through different ML models. The accuracy of the models is validated using performance metrics that shows that the Random Forest (RF) metrics predict the probability of deformation locations in response to the volume reduction of the burning coal fire and vertical compression due to Overburden Dump (OBD) near active mine benches. The estimated displacement trends have been used to forecast the Autoregressive Integrated Moving Average (ARIMA) method, estimated using Line-of-Sight (LOS) displacement values vary around the best fit within the 95% confidence limits. The trend shows ∼15–25% increase in subsidence compared to the cumulative subsidence.

Multisource Data-Driven Carbon Price Composite Forecasting Model: Based on Feature Selection and Multiscale Prediction Strategy

Accurate prediction of carbon trading prices is crucial for guiding investors to make informed decisions and assisting governments in formulating scientific carbon trading policies. This paper introduces a multisource data-driven carbon price composite forecasting model, aimed at enhancing prediction accuracy through advanced data processing and analysis methods. The model initially employs a secondary decomposition strategy, including Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) methods, to decompose the original data series into three sub-sequences of different frequencies. Subsequently, it utilizes the Partial Autocorrelation Function (PACF) and Random Forest algorithm for feature selection to determine the input variables for different frequency sequences and conducts in-depth analysis and selection of influencing factors, including unstructured data. Furthermore, the model employs a multiscale forecasting strategy, combining Particle Swarm Optimization (PSO) enhanced Bidirectional Long Short-Term Memory (BiLSTM) and Extreme Gradient Boosting (XGBoost) models, along with the Autoregressive Integrated Moving Average (ARIMA) method, to predict based on the characteristics of different frequency components. Finally, the forecasts are integrated using PSO-BiLSTM to form a comprehensive forecast. Notably, given the high correlation between the trend series and influencing factor variables, the model jointly predicts them. A case study based on the Guangdong carbon market in China demonstrates that the proposed composite forecasting model outperforms other benchmark models, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values of 0.4009, 0.2699, and 0.5183%, respectively. This forecasting model provides an effective tool for predicting and analyzing carbon price fluctuations, offering new insights for precise carbon market price predictions.

Evaluation of the implementation of Main Distribution Material (MDU) spare part procurement at seven customer service unit (ULP) in Semarang are using ABC analysis and time series forecasting

Evaluation of the Implementation of Main Distribution Material (MDU) Spare Part Procurement at Seven Customer Service Units (ULP) in Semarang Area using ABC Analysis and Time Series Forecasting (Case Study: PT PLN (Persero) UP3 Semarang) Time series forecasting is a method used to predict the number of future needs in a time series. This research was conducted at the company PT PLN (Persero) UP3 Semarang, where the author tried to deal with the company's problems related to the procurement of Main Distribution Material (MDU) which often occurs overstock or lack of stock. The author uses ABC analysis to classify goods based on value ratings ranging from the highest value so that three parts with group A are obtained for forecasting, kWH meters, 2 × 10 mm2 PWR cables, and 4A MCBs. Forecasting is done with four methods, the Multiplicative Decomposition method, the Holt-Winters method, the Moving Average method, and the Exponential Smoothing method. From the results of the error calculation, it is obtained that the Holt-Winters method obtains the lowest percentage error with results of 10% - 20% or is good to use for forecasting. The comparison between the forecasting results and the overall actual demand is above 70%, which means that the forecasting results are close to the actual demand that occurs, thus increasing the accuracy of the Holt-Winters forecasting method. Suggestions for improvement that can be given to the company are to apply the Holt-Winters forecasting method in forecasting MDU spare part inventory at PT PLN (Persero) UP3 Semarang.

Risk Assessment of Urban Water and Energy Supply Using Copula Function: A Water–Energy Nexus Approach in an Arid City

Planning for the future of water and energy supply systems in urban areas requires a thorough assessment of associated risks. In this study, monthly water and energy demand data from 2011 to 2022 in an arid city was used to predict the corresponding demands from 2023 to 2032 using the seasonal auto-regressive integrated moving average (SARIMA) method. The aim is to estimate future water and energy supply risks both individually and jointly, using cumulative distribution functions (CDFs) derived from historical data. The main focus is to calculate the combined risk of water and energy, referred to as the water–energy nexus (WEN) risk. Based on the interdependent relationship between water and energy, the Copula function was utilized to model the bivariate distribution between these two variables. Pearson correlation analysis indicated a strong correlation between water and energy supplies. Among the distributions fitted to the data, the log-normal and gamma distributions were the best fit for water supply and energy supply systems, respectively, with the lowest Akaike information criterion (AIC) values. The Gumbel Copula, with a parameter of 1.66, was identified as the most suitable for modeling the joint distribution, yielding the lowest AIC value. The results indicate that the risks associated with energy supply, water supply, and their joint dependency could exceed 0.8% in the future, highlighting a potentially critical situation for the city. The trend analysis revealed that forecasted water and energy demands and their corresponding risks and the WEN risk are expected to have a significant upward trend in the future. Finally, local authorities need to explore alternative sources to supply water and energy in the future to address the ever-growing water and energy demands.

A “WEST” Theory in the East: Decoupling and synergistic evolution of water utilization, economy, and society in China

AbstractTo attain a thorough examination of the dynamic interaction mechanism between China's utilization of water resources and its economic and social development, this paper innovatively introduces a Western theoretical framework within an Eastern context. The aim is to quantify the process of decoupling and synergistic evolution from 2006 to 2020. Decoupling signifies achieving economic growth while reducing resource use or mitigating environmental impacts, whereas synergy denotes enhancing economic efficiency while contributing to environmental protection. This paper employs the Tapio elastic coefficient method to construct a decoupling model. A collaborative model, based on the entropy weight Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and weighted average methods, also assesses the organization and coordination of subsystems within the complex water‐economic‐social system. The findings reveal a strong decoupling between China's water resource management and economic and social systems, as indicated by a consistently negative decoupling index. Similarly, the economic system exhibits a degree of inefficiency influenced by factors like economic cyclicality and resource allocation. In contrast, the social system experienced a decline, particularly during the pandemic (2019–2020), leading to a social instability. This study provides valuable policy formulation and sustainable development insights, contributing to a comprehensive understanding of the intricate dynamics between water utilization, economy, and society.

Simultaneous fault-tolerant consensus control and disturbance suppression for multi-agent systems with polynomial form fault under switching topology

For continuous-time linear leader-follower multi-agent systems with polynomial faults, the problem of fault-tolerant state consensus under switching topologies is studied. firstly, the average dwell time method is introduced, and observers are designed for different topologies to observe faults and disturbances. Secondly, according to the observed estimated value, the corresponding fault-tolerant control consensus protocol is proposed considering the switching situation of the communication topology. Finally, both theoretical analysis and simulation examples verify that the proposed design scheme can compensate faults and disturbance rejection.

Distributed Multi-Sensor Fusion for Multi-Group/Extended Target Tracking with Different Limited Fields of View

The identification of sensing group targets and extended targets is of paramount importance in the context of vehicle tracking and early warning detection. As the scope of target monitoring and tracking extends, conventional single-sensor-based tracking techniques are proving to be inadequate in meeting the practical demands of the field. Consequently, multi-sensor fusion tracking technology has emerged as a viable alternative. However, the use of multiple sensors is constrained by their limited fields of view (FOVs), which leads to issues such as the loss of target detection and the introduction of false targets after fusion. Hence, this study proposes a combination of weighted geometric averaging (WGA) and weighted arithmetic averaging (WAA) methods to solve distributed multi-group/extended target tracking with different fields of view. Specifically, a local Poisson multi-Bernoulli mixture (PMBM) filter was first used on individual sensors. Subsequently, we combined a sequential fusion technique and the proposed fusion approach to fuse the PMBM filter densities. The efficacy and superiority of this approach were demonstrated through simulations.

Comparison of Moving Average and Exponential Smoothing Methods for Estimating the Number of Internet Service Disruptions

Fairly tight competition in telecommunication business requires companies to continue improving the quality of their services, including handling network problems or customer complaints. However, the amount of disturbance that occurs is often unpredictable. Indeed, this condition will impact the allocation of resources and budget for handling customer disturbances in the following period, with uncertainty. This research aims to apply the moving average (MA) and single exponential smoothing (SES) forecasting methods to predict the number of internet service disruptions. The case study used in this paper is an internet service provider company with service coverage in the northern part of Surabaya. The number of disturbances data was collected from January 2022 to May 2022, with 18,453 data in total. The disturbances can be divided into five types: physical, mass, logical, PSB/migration, and others. Forecasting is carried out using the MA method with a period of three months. Meanwhile, forecasting using the SES method was carried out by first determining the alpha value that produces the smallest Mean Absolute Percentage Error (MAPE) value. The analysis results show that both forecasting methods are relatively effective and efficient in estimating the number of disturbances. Forecasting performance testing was carried out by measuring the Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE) values. The results of forecasting performance measurements show that the SES method is much better than MA for all types of disturbance data, with MAPE values ​​below 2%.

Revisiting a Drag Partition Model For Canopy-Like Roughness Elements

Turbulent flows over a large surface area (S) covered by n obstacles experience an overall drag due to the presence of the ground and the protruding obstacles into the flow. The drag partition between the roughness obstacles and the ground is analyzed using an analytical model proposed by Raupach (Boundary-Layer Meteorol 60:375-395, 1992) and is hereafter referred to as R92. The R92 is based on the premise that the wake behind an isolated roughness element can be described by a shelter area A and a shelter volume V. The individual sizes of A and V without any interference from other obstacles can be determined from scaling analysis for the spread of wakes. To upscale from an individual roughness element to n/S elements where wakes may interact, R92 adopted a background stress re-normalizing instead of reducing A or V with each element addition. This work demonstrates that R92’s approach results in a linear background stress reduction in A and V only when the ratio of n/S is small, due to a low probability of wake interactions. This probabilistic nature suggests that up-scaling from individual to multiple roughness elements can be re-formulated using stochastic averaging methods proposed here. The two approaches are shown to recover R92 under plausible conditions. An alternative scaling for the shelter volume is also proposed here using thermodynamic arguments of work and dissipation though the final outcome remains similar to R92. Comparisons between R92 and available data spanning more than two decades after R92 on blocks and vegetation-like roughness elements confirm the practical utility of R92. The agreement between R92 and this updated databases of experiments and simulations confirm the potential use of R92 in large-scale models provided that the relevant parameters accommodate certain features of the roughness element type (cube versus vegetation-like) and, to a lesser extent, their configuration throughout S. Last, a comparison between R92 and models based on first-order closure principles with constant mixing length suggests that R92 can outperform such models when evaluated across a wide range of roughness densities.

Higher-order stochastic averaging for investigating a vehicle suspension system with nonlinear damping and stiffness

The paper deals with a quarter-car model with nonlinear damping and stiffness under white noise base excitation using the higher-order stochastic averaging method for analyzing approximate responses. Recently, a novel higher-order averaging procedure has been developed to find analytically the first-, second-, and third-order stationary joint probability density functions (PDF) of amplitude and full phase by solving the corresponding Fokker-Planck-Kolmogorov (FPK) equation, and it will be extended to the nonlinear quarter-car model. Accordingly, the mean-square responses such as the displacement, and velocity of the sprung mass can be obtained analytically. The influences of excitation intensity on the dynamic responses, as well as the variations of linear and nonlinear damping, are analyzed. A very satisfactory agreement is found between the accuracy of the solutions corresponding to higher-order stochastic averaging and that of Monte Carlo simulation.

Development of an Algorithm for Determining the Class of an Obstacle in the Process of its Interaction with the Manipulator

The article discusses the problem of determining the nature of an obstacle using a force sensor for terrain mapping. Three classes of objects have been introduced: the first class is "static" obstacles, the second class is "moving" obstacles, the third class is "unstable" obstacles. To conduct experiments, a simulation system was built in the Gazebo modeling environment, including a manipulator with the ability to horizontally move a probe with a force sensor and a set of obstacles corresponding to three classes of obstacles. All experiments were carried out using the ROS framework, the Gazebo simulator and the Movelt manipulation software. Processing of signals from the force sensor showed that the sig­nals for the three classes of obstacles are different, which makes it possible to identify the class based on sensor readings. Based on the analysis of data received from the sensor, rules were formed that make it possible to determine the class of an obstacle during its interaction with the manipulator in real time. To determine the obstacle class, the values of the force sensor signals are considered, smoothed by the moving average method. According to the rules discussed in the article, an algorithm was developed to determine the class of obstacle, which was implemented in the Gazebo modeling environment. Testing of the algorithm showed the stability of its operation under the given experimental conditions. Successful determination of the obstacle class will allow one not only to construct a map of the area, but also to prevent damage to the device when exposed to a "static" object with excessive force. The presented approach can be expanded to narrower classes of obstacles that characterize objects that can be encountered by a mobile device when mapping a forest area.

Modified Centroid of Root Projection Method for Determining the Center of Resistance of a Tooth

Abstract Center of resistance (CR) has been widely accepted in dentistry as a reference point for controlling tooth moment, which depends on the direction of loading and the morphology of the periodontal ligament (PDL). In clinical practice, dentists estimate the location of CR based on the morphology of the root of teeth, which may lead to a misestimation of orthodontic treatment. A quick method was proposed to efficiently determine the CR by identifying the centroid of the root projection (CRP), according to the orthodontic force. However, the original CRP method was limited to single-rooted teeth and it did not provide a strategy for handling the overlapping roots projection of multi-rooted teeth. To address this issue, we expanded the CRP method to accommodate multi-rooted teeth by applying the weighted average method. We further validated the modified CRP method using finite element simulation (FEA) for both single-rooted and multi-rooted teeth considering mesial-distal and buccal-lingual force directions. The evaluation of displacement distribution along the projection direction allowed us to assess translation and rotation movements, which confirmed that the centroid of root projection can accurately serve as the CR for the multi-rooted teeth. Additionally, we observed heterogeneous stress distributions in the multi-rooted teeth. Considering the well-acknowledged bone remodeling effect in response to local stress states, this indicated that the comprehensive indexes beyond the CR are desired for evaluating or controlling the tooth movement.

Model predictive control for space robot manipulator modeled by switched systems: A data‐driven approach

AbstractThis article investigates the data‐driven based model predictive control (MPC) problem for a class of space robot manipulator (SRM). First, the SRM system is modeled as a class of discrete‐time switched system to reflect more system characteristics. Second, only the input‐state data are used for end‐to‐end controller design, and the system identification process is avoided. In order to meet the real‐time requirements of the manipulator arm angles, a kind of constrained predictive control method is designed to prevent the overheating or damage of SRM motor, optimizing the cost function for each decision cycle under the safe control input constraints. Then, based on the average dwell time method, the asymptotic stability is assured for the presented SRM system. The design conditions for data‐driven based MPC are provided in the form of linear matrix inequalities. Finally, the effectiveness and advantage for the developed data‐driven based MPC strategy are validated via a case study of SRM system.

Mapping of Food Crop Commodity Production Areas in Indonesia Using The Average Linkage Method

Mapping of Food Crop Commodity Production Areas in Indonesia Using The Average Linkage Method

High-frequency average phase compensation method for gamma nonlinearity based on optimal-frequency strategy

The accuracy of fringe projection phase-shifting profilometry (PSP) is affected by gamma nonlinearity greatly, and the average phase compensation method is an effective technique to reduce the nonlinear error. However, double fringe patterns are commonly required, especially combined with the multi-frequency phase unwrapping method (MFPU), using 6 × 3 images in three-frequency method, which limits the measurement eiciency. To reduce the number of required images, this paper presents an efficient average phase compensation method using 6f h + 3f l + 3f u algorithm based on an optimal-frequency strategy. Six high-frequency standard and π/3 shifted 3-step phase-shifting fringe patterns are used together to generate high-accuracy wrapped phase. Three unit-frequency and three low-frequency fringe patterns are used to obtain coarse a unit-frequency wrapped phase and a coarse low-frequency wrapped phase, respectively. To ensure the robust phase unwrapping for high-frequency phase, the mathematical model of the optimal frequency is derived and determined by phase error amplitude calculation. Simulation and experimental results verified that only applying average phase compensation under the guidance of optimal-frequency selection strategy could achieve robust phase unwrapping and high-accurate measurement by reducing the nonlinear error substantially.

UDIS: Enhancing Collaborative Filtering with Fusion of Dimensionality Reduction and Semantic Similarity

In the era of vast information, individuals are immersed in choices when purchasing goods and services. Recommender systems (RS) have emerged as vital tools to navigate these excess options. However, these systems encounter challenges like data sparsity, impairing their effectiveness. This paper proposes a novel approach to address this issue and enhance RS performance. By integrating user demographic data, singular value decomposition (SVD) clustering, and semantic similarity in collaborative filtering (CF), we introduce the UDIS method. This method amalgamates four prediction types—user-based CF (U), demographic-similarity-based (D), item-based CF (I), and semantic-similarity-based (S). UDIS generates separate predictions for each category and evaluates four different merging techniques—the average, max, weighted sum, and Shambour methods—to integrate these predictions. Among these, the average method proved most effective, offering a balanced approach that significantly improved precision and accuracy on the MovieLens dataset compared to alternative methods.