Stages Of Data Processing Research Articles

APPLICATION OF TWO GROUPS ANALYSIS AND CLUSTER ANALYSIS ON COMPARISON OF CHARACTERISTICS OF PAGARALAM COFFEE FARMERS CATEGORIES

Land care is one of the factors that must be considered in the sustainability of coffee production. A small number of Pagaralam coffee farmers know and apply reductant herbicide in weed control. This study aims to compare the characteristics of Pagaralam coffee farmers based on the use of reductant herbicide by using two groups analysis and cluster analysis. The characteristics of the farmers studied included 17 variables on 165 respondents who were selected by purposive sampling. The variables studied include land productivity and farmers' income. Respondents were divided into 2 categories, namely users and non-users of reductant herbicide. The initial stages of data processing in the form of descriptive statistics, hypothesis testing by mean difference test, correlation between variables, Principal Component Analysis, and biplot analysis. According to the results of the two groups analysis and cluster analysis, the variables dominantly tend to characterize the similarity in comparison between the categories of non-users and users are the variables that join into one cluster, namely land area, number of trees, coffee bean production, estimated yield, total harvest, Gross income, and Net income. While the variables that dominantly characterize the dissimilarity between the two categories are the variables that form 5 separate clusters, namely the clusters of plantation productivity, respondent identity, education, tree age, and length of harvest period.

The Odd Harmonious Labeling of Layered Graphs

Graphs that have the properties of odd harmonious labeling are odd harmonious graphs. The research objective of this paper is to obtain odd harmonious labeling on layered graph C(x,y) and layered graph D(x,y). The research used in this paper is a qualitative method. The research flow consists of data collection, processing, and analysis. The data collection stage consists of constructing the definition of the new class graph, the data processing stage consists of constructing the vertex labeling and edge labeling, and the data analysis stage consists of constructing the theorem and proving it. The research results show that the layered graph C(x,y) and layered graph D(x,y) fulfill odd harmonious labeling. Such that the layered graph C(x,y) and layered graph D(x,y) are odd harmonious graphs. The benefit of this research is to add new properties of odd harmonious graphs. In addition, it does not rule out the possibility that this research can be developed again both in theory and application.

Intersense: An XGBoost model for traffic regulator identification at intersections through crowdsourced GPS data

Digital maps of the transportation network are the foundation of future mobility solutions. Autonomous and connected vehicles rely on real-time, at-scale updating of the environment in which they operate. Successful operation in a hybrid environment, where human and machine intelligence coexist, requires explicit knowledge of the traffic regulator infrastructure. Future generation traffic management strategies and path planning systems must be tightly integrated with the regulator infrastructure in order to improve traffic dynamics and reduce congestion in urban environments. In this article, we present Intersense, a regulator identification and categorization system that leverages raw, naturalistic GPS trajectory data to infer the existence and type of regulators present at network intersections. A supervised learning approach, based on the eXtreme Gradient Boosting (XGBoost) algorithm, combines infrastructure and vehicle telemetry data features to analyze movement patterns vehicles follow when approaching intersections. For the most widely used regulator types (traffic light, stop sign and right of way), the achieved accuracy surpasses 96%. Permutation feature importance is used to evaluate the relative importance of the generated features, providing a detailed view of the classification model and insights for future data collection and processing stages. We showcase the Intersense’s adaptability to various GPS data sources with uneven penetration and sampling rates and we illustrate the system’s transferability across very diverse cities by performing experiments in San Jose and San Francisco. Finally, the relationship between the number of available trajectories and classification accuracy is derived to determine the necessary data collection investment.

Survey Training for Collecting Data of Nagari Tanjung Balik

Collecting data is the initial stage of data processing. Such that, it is needed to make sure the data collected is representative. Surveyor is one of its principal components. But, Nagari as a small component of a residence lack of professional surveyor for the work of the survey. The Statistics Department as a producer of statistician gives training to local residents to collect their own data using the right method in Nagari Tanjung Balik

Низкогорные территории Большой Белокурихи: туристское проектирование на основе спектра рекреационных возможностей

Introduction. Bolshaya Belokurikha is a dynamically developing tourist destination in the low mountains of the Altai Territory with a sufficient number of underdeveloped territories near the resort town of Belokurikha. The article shows the application of a territory management system based on the recreation opportunity spectrum for the design of the developing territories of Bolshaya Belokurikha. The purpose of the study is to classify the tourist areas of Bolshaya Belokurikha in accordance with the recreation opportunity spectrum and develop recommendations for their design. Materials and methods. The study included the following stages: 1) analysis of the existing use of territories, 2) defining of opportunity setting factors (controlled factors), 3) description of the potential recreation opportunity spectrum with specific values of factors for each class of territory, 4) typification of the territories of Bolshaya Belokurikha in accordance with the characterized spectrum classes, 5) comparison of the planned classes with the current state of the territories and the formulation of design recommendations. The following methods were used to collect data: observation, analysis of secondary data, non-standardized survey of experts from the scientific and business communities in the field of tourism in the form of interviews with open-end questions. At the data processing stage, general scientific methods of analysis, synthesis, and classification were used. As opportunity setting factors (controlled factors), we chose the frequency of encounters with other visitors, the density of buildings and infrastructure, the type of accommodation facilities, the number of service facilities, the type of access to the territory (development of the transport and route network and its quality). Results. The potential recreation opportunity spectrum of Bolshaya Belokurikha includes 8 classes of territories that can satisfy the different needs of visitors: urban territory (obtaining the widest possible range of services in the most comfortable conditions), rural areas (rest from the bustle of the city in comfortable conditions, eco-food, immersion in rural life), motorized sightseeing (quick access to nature in relatively comfortable conditions), a pedestrian zone (short walks along specially equipped health trails near the urban area), semi-primitive motorized territory (active secluded outdoor recreation in comfortable conditions with the possibility of moving on ATVs, bicycles, etc.), hiking zone (solitary short-term (1-3 days) recreation in comfortable conditions, light hiking with overnight stays at camp sites, in glampings, etc.), backcountry (trekking) zone (solitary recreation in training conditions, long hiking with overnight stays in tents in designated places), primitive territory (full immersion in nature with a high level of risk and difficulty). Discussion. Taking into account current trends in demand, the most attention in terms of design in Bolshaya Belokurikha is currently required by the semi-primitive motorized zone and the hiking zone: it is necessary to create accommodation facilities, equip existing routes and design new ones in accordance with the standards defined for each class of territory. Conclusion. The analysis of the existing use of tourist areas within Bolshaya Belokurikha showed that over time, the structure of the tourist space becomes more complex and the transition from point development to area development takes place. The conditions for the development of the tourist territories of Bolshaya Belokurikha as a whole tourist complex and its competitiveness on the basis of the implementation of recreation opportunity spectrum are determined. Resume. 1. The potential recreation opportunity spectrum in the tourist areas of Bolshaya Belokurikha is described in the form of standards for 8 classes of territories. 2. Types of recreational opportunities that require priority development are identified, objects for design are proposed. Suggestions for practical application and direction for future research. The results of the research can be useful in the development of spatial planning schemes for municipal districts and master plans for tourist areas in Bolshaya Belokurikha, as well as in the development of a methodological base for designing tourist areas. Further research can be aimed at conducting a detailed functional zoning of tourist areas in Bolshaya Belokurikha, taking into account the identified recreation opportunitу spectrum.

Leverage Factors that Impact on Company's Financial Performance

The purpose of this study is to analyze the leverage factors that affect the financial performance of the company. Leverage factors used by researchers are company size, profitability, tangible assets, and growth opportunities. The method used is a qualitative library study through the analysis of research that examines the leverage that affects the financial performance of a company. Based on the stages of data processing in this study, it was found that the results of this study indicate that leverage reduces the financial performance of a company. The company's dependence on debt as a source of financing shows that the source of funds in the form of debt has only a small impact on financial performance and tends to decrease each year. Meanwhile, leverage uses the company's assets and strengths to incur short-term and long-term costs, such as debt, to carry out the company's goal of maximizing the wealth of the company's owners.

HASBI ASH-SHIDDIEQY'S THOUGHTS IN THE PEARL OF HADITH: INTERTEXTUAL ANALYSIS OF IDEA

It is hoped that this research can decide how to examine the intertextual ideas of Hasbi Ash-Shiddieqy's thoughts in one of his works, the pearl of hadith. From researching his work entitled "2002 Pearls of Hadith", the considerations he made in searching for hadith in the pearls of hadith, were as follows: 1) Hadith was understood through the guidance of the Qur'an, 2) Musykil hadiths in takwil , 3) Looking for agreement on conflicting traditions. Researchers see that in every study so far, Hasbi has always emphasized the use of a historical socio-cultural approach, or what is commonly called a logical approach, but in this study, the researcher tries to see Hasbi's point of view from an intertextual approach. Where the function of hadith in the Koran is always considered and also seen in other traditions that have similar topics and other traditions that seem problematic. This research is qualitative literature (library research), then the data processing stage and analyze the data that has been collected, the author uses the descriptive analysis method

Online Intelligent Temperature Monitoring System for Tunnel Power Cable Based on Fiber Bragg Grating

To solve the problem that the existing method is better than the power cable and the instability of the environment leads to low accuracy of its monitoring, this paper proposes the research of an online intelligent monitoring system of tunnel power cable temperature based on fiber grating. In the design of system architecture, FACE-TD200, with abundant FPGA programmable logic resources, is used as the main device, and the SuperHawk 1002T fiber grating temperature sensor is set to realize temperature acquisition. In the stage of data acquisition and processing, considering the loss caused by the difference between the wavelength of pump light and Raman scattering light, the spontaneous Raman scattering light power of the acquired signal is calculated. According to a certain energy difference between the upper energy level and the lower energy level of the optical fiber molecule, the temperature information of the cable is calculated. The test results show that the temperature monitoring results of the designed system have a high degree of fitting with the actual temperature values, and the monitoring effect is better than that of the control group.

The Influence of Individual Factors and Individual Behavior on Employee Performance at PT. Naga Aries Teknologi

This study aims to analyze the influence of individual factors and individual behavior on employee performance at PT. Naga Aries Teknologi Surabaya. The research is aimed to employees of PT. Naga Aries Teknologi. Research respondents amounted to 45 people with the type of research sample selection, namely the sampling method in this study using saturated sampling techniques. Saturated sampling is a sampling technique when all members of the population are used as samples. This is often done when the population is relatively small. The research method used is a survey using questionnaire with reference to the Likert scale and type of research is exploratory research. Methods of data analysis using multiple linear regression, hypothesis testing and coefficient of determination. The data processing stage uses IBM Version 25 Statistical Application Solution (SPSS). The results showed that simultaneously, individual factors and individual behavior simultaneously influence the performance of PT. Naga Aries Teknologi. It can be said that individual factors and well-executed individual behavior can improve the performance of PT. Naga Aries Teknologi

Analisis Nilai Tambah dan Strategi Pengembangan Agroindustri Siwalan di Desa Hendrosari, Kecamatan Menganti, Kabupaten Gresik

The purpose of this study was to analyze the added value of siwalan dawet and legen siwalan products in Hendrosari Village, Menganti District, Gresik Regency and to analyze the strategy of developing Siwalan agroindustry in Hendrosari Village, Menganti District, Gresik Regency. The data analysis method used in this study was descriptive quantitative, covering the data processing stage and descriptive data interpretation. The analysis used consisted of value added analysis of the hayami method and SWOT analysis. The results obtained from this study are the added value resulting from processing siwalan fruit into siwalan dawet is higher than the added value from processing siwalan sap into siwalan legen. %, while the added value from processing siwalan sap into siwalan legen is IDR 1,000/bottle or 29%. The SWOT analysis shows the IFAS value of 3.05 and the EFAS value of 2.78. This value is in quadrant IV (stability growth), in the internal external matrix including the White Area, so the strategy that can be done is the S-O strategy. The S-O strategy is a strategy used to maximize the strengths and opportunities of the Siwalan agroindustry. The strategy that can be used is to strengthen relations with suppliers to maintain the availability of raw materials, maintain the existing market and expand the reach of market share, increase the production capacity of dawet siwalan.

MEMS‐based 3‐C borehole gravimeter development

AbstractA micro‐electro‐mechanical‐based 3‐C borehole gravimeter has been developed in China for mineral and hydrocarbon exploration. The 3‐C borehole gravimeter is composed of a three‐axis gravity sensor chip based on a deep silicon etching technique, high‐precision capacitive displacement sensing and weak signal detection circuitry. The gravity‐sensing chip is a silicon‐based integrated spring‐mass block system. Silicon wafer is etched by the micro‐nanofabrication technique to form a high collimation groove. The size of the gravity‐detecting mass block in the sensitive element plays a decisive role in the thermal noise level of the instrument. Deep silicon processing technique can produce a thicker silicon mass block (500 μm), which can obtain a larger mass block in the same area compared with the traditional silicon surface processing technique (10–100 μm). The out diameter of the final tool is 50 mm with 10,000 mGal measurement range, 155°C temperature and 100 MPa pressure rating. Apart from the 3‐C micro‐electro‐mechanical gravity sensor, a 3‐C fluxgate magnetic sensor is also integrated into the downhole tool. This allows us to measure both 3‐C gravity field and 3‐C magnetic field downhole simultaneously and conduct joint inversion of both the downhole 3‐C gravity and 3‐C magnetic data during the data processing stage. The prototype tool was tested in a borehole up to 850 m depth, and the 3‐C gravity sensor inside the tool can measure the vibration of the environment during the stationary measurement downhole. The first test data set shows repeatability near 800 m depth, which verifies the adaptability of the micro‐electro‐mechanical‐based gravimeter to the down instrument environment.

Study of permutation decoding algorithms in UAV control systems

Relevance. This paper presents relevant topic of the use of unmanned aerial vehicles (UAV) in the agroindustrial complex (AIC) to obtain timely reliable information about the state of agricultural land for the entire period. In modern infocommunication technologies and communication systems, high requirements are placed on the reliability of information transmission. In mobile data exchange systems, compositions of noise-resistant codes are used in the form of cascade designs or turbo codes, and in order to increase their efficiency, it is necessary to make the fullest use of the redundancy introduced at each stage of data processing. To solve such a problem, it is advisable to apply a decoding method based on the ordering of the character confidence indices (CCI) of the code combination.Methods. When determining the encoding-decoding systems, the Mathcad application software was used. Mathematical studies have shown that binary block codes for reaching the specified boundary can be decoded not only on the basis of ordered statistics, but also using the method of dividing the space of allowed code combinations into clusters. Based on this method, a block diagram of the algorithm of the simulation model of the operation of the block code decoder based on cluster calculation is compiled.Results. The application of the proposed method of soft decoding of systematic block codes reduces the processing time of code combinations in the decoder by reducing the dimension of the square matrix to determine the nonlinearity property of the rows of the rearranged generating matrix after ordering the columns of the original generating matrix.

Lightweight Midrange Arm-Gesture Recognition System From mmWave Radar Point Clouds

This article considers arm-gesture recognition in the form of a point cloud in mid-range scenarios. A 2-D steer vector is established according to the structure of the radar array antenna, and the average power of the array is added to the point cloud image as a feature. In the data processing stage, the proposed system aggregates multiple measurements on all frames, overcoming the issues of the spatial sparsity and ambiguous temporal variation of the point cloud image of the millimeter-wave (mmWave) radar, as a new arm-gesture point cloud image. In the arm-gesture recognition stage, a lightweight PointNet-based classifier is custom-designed to recognize and classify arm-gestures point cloud images. Extensive experiments are carried out on custom-built radar gesture datasets with eight human subjects performing 19 arm-gestures. The experimental results demonstrate that the proposed arm-gesture recognition system can accurately distinguish 19 different arm-gestures with an average accuracy of 99.69% in two indoor environments and outperform five related arm-gesture classifiers. We further analyze the effect of the scenarios and distance on recognition performance.

Current and future role of data fusion and machine learning in infrastructure health monitoring

Rapid advances in infrastructure health monitoring and sensing technologies allow the monitoring of infrastructure assets continuously and in real-time throughout their life span. However, smart and automated techniques for decision-making (e.g. maintaining or improving infrastructure performance) are in their infancy. The revolution in new sensing capabilities has led to rapidly increasing volumes of data, which makes traditional data analysis techniques inadequate. Adoption of Big Data (BD) analytics and Artificial Intelligence (AI) techniques are urgently needed to automatically integrate information from multiple sensors, extract knowledge and inform decision-making. The objective of this work was to provide a state-of-the-art review of data fusion and machine learning techniques applied to infrastructure health monitoring. In contrast to the previously published, related review articles, the focus of this review is on the techniques implemented by machine learning algorithms, their applications at each data processing stage in a machine learning framework, and their advantages and limitations. Finally, challenges and future trends for machine learning techniques and infrastructure health monitoring systems are discussed. As a review, this paper offers meaningful suggestions for employing data fusion and machine learning techniques in infrastructure health monitoring.

Direct estimation ofVS30 using spatial autocorrelation and centreless circular array coefficient curves obtained from microtremor array data

SUMMARYThe average S-wave velocity (VS) in the upper 30 m (VS30) is a proxy for seismic wave amplification. Microtremor array exploration is one of the available methods for site characterization, but the recorded data require complicated processing that can lead to different estimations of VS30 depending on the analyst and processing software. We propose a method of estimating VS30 by using derivatives obtained in the early stages of microtremor array data processing. Statistical analysis with 2376 virtually generated subsurface VS structure models revealed that the frequencies at which the spatial-autocorrelation (SPAC) coefficients and centreless circular array (CCA) coefficients take specific values strongly correlate with VS30, which we used to develop formulas for estimating VS30. Numerical validations using actual VS profiles at 616 sites in Japan showed that the proposed method could estimate VS30 with a root-mean-square deviation (RMSD) of 57–80 m/s with SPAC coefficients and 56m/s with CCA coefficients. Our proposed methods were applicable for 98–100 per cent of theVS profiles when we limited our estimation to sites with VS30 < 760 m/s. The results indicated that SPAC coefficients from arrays with radii of 8–20 m can be used for VS30 estimation and are less affected by incoherent noise. In contrast, CCA coefficients are much more sensitive to incoherent noise, which resulted in the overestimation of VS30. The estimated VS30 values from the recorded microtremor array data were in good agreement with the reference values from the actual VS profiles. The proposed method allows for robust and efficient VS30 estimation without relying on the analyst’s skills or software.

Perancangan Sistem Informasi Akuntasi Penentuan Harga Pokok Produksi Dengan Metode Full Costing Berbasis Desktop Di PT. Kentang Hollando Sejahtera

The research objective is to design an accounting information system for determining the cost of production at PT. Hollando Sejahtera potatoes and to be implemented according to the company's needs. The design of the accounting information system uses the full costing method and the method of gathering information and supporting data during this research using observation, interviews and literature studies. Software for information system development uses Microsoft Visual Basic and for databases uses MySQL. System development in this study uses the Waterfall method. The documents used are the use of materials, labor, and overhead during the production process. The data processing stage is presented in data tables and their relations in the form of an ERD. The data processing stage is presented in a flowchart and program interface design. The input design consists of raw material transactions, supporting material transactions, labor cost transactions, overhead cost transactions, harvest data. The output designs are inventory reports, HPP reports, general journal reports and harvest reports. The result of this research is to replace an existing system which is still simple and inaccurate in determining production HPP, and the results of implementing this system are able to assist in recording, controlling and reporting from the production team to company management making it easier for company management to make decisions and policies for the next production process.
 Keywords: Accounting Information System, HPP, Full Costing

Dynamic Texture Classification Using Directional Binarized Random Features

Dynamic texture description has been studied extensively due to its wide applications in the field of computer vision. Local binary pattern (LBP) and its various variants account for a large part of dynamic texture description methods because of its advantages, such as good discriminability and low computational complexity. However, many LBP-based methods directly extract feature from pixel intensities and only use a proportion of pixels in a local neighborhood. And their good classification performance is usually achieved at the cost of high feature dimensionality, which would limit their application scenarios. We argue that extracting features from the gradient domain will capture more discriminative features due to the additional directional information, and that making use of all the pixels in a local neighborhood would improve performance. In this paper, we propose a simply but effective dynamic texture descriptor that inherits the advantages of LBP while excluding its disadvantages. The proposed method consists of four stages of data processing: 1) gradients extraction; 2) random feature extraction from gradients; 3) binary hashing of directional random features; and 4) histogramming. Gaussian first-order derivatives are used as gradient filters such that stable gradients could be generated. Then random projection is applied to extract random features from each gradients. Both the above two stages are conducted via 3D filtering, and thus they are efficient. Thirdly, the random features from each gradient are binarized and encoded into integer codes, from which a histogram is built. Finally, the histograms from each gradient are concatenated into a feature vector. Because we use 8-bit codes, The feature dimensionality is very low. We evaluate the proposed method on three benchmark dynamic texture datasets with various test protocols. The results demonstrate its effectiveness and efficiency when comparing to many state-of-the-art methods.

FAO as a tool of ecosystem entrepreneurship and its implementation in Ukraine

In the process of research, it was determined that the cooperation between Ukraine and FAO has a productive activity, in particular, in the formation of national legislation. It is developed together with the government, in particular with the Ministry of Agrarian Policy and Food of Ukraine, and other partners. We developed a special toolkit - a standardized interview questionnaire - for an empirical study of perspectives and strategic priorities in the formation of the FAO activity model and its implementation in Ukraine. In the process of researching enterprises, respondents in the form of managers responsible for ecosystem management were asked to determine the effectiveness of FAO's activities in the field of responsible ecosystem entrepreneurship in order to formalize the model of FAO's activities and its implementation in Ukraine. The answers of the respondents were processed using the developed method of mathematical filtering of the research results, which consists of three filtering blocks, which makes it possible to detect at the first stage the level of variability of the studied data using the mode and median of the studied series. At the second stage we determine the symmetry and sharpness of the distribution, using the calculation of asymmetry, standard deviation and kurtosis. At the third stage of data processing, based on the analysis of the Pearson correlation coefficient, the Fisher F-criterion, the Friedman test and the Kruskal-Wallis H-criterion, correlation-dispersion deviations are determined, which make it possible to exclude such respondent data with significant deviations from the recommendation part from the norm.

PAACDA: Comprehensive Data Corruption Detection Algorithm

With the advent of technology, data and its analysis are no longer just values and attributes strewn across spreadsheets, they are now seen as a stepping stone to bring about revolution in any significant field. Data corruption can be brought about by a variety of unethical and illegal sources, making it crucial to develop a method that is highly effective to identify and appropriately highlight the various corrupted data existing in the dataset. Detection of corrupted data, as well as recovering data from a corrupted dataset, is a challenging problem. This requires utmost importance and if not addressed at earlier stages may pose problems in later stages of data processing with machine or deep learning algorithms. In the following work we begin by introducing the PAACDA: Proximity based Adamic Adar Corruption Detection Algorithm and consolidating the results whilst particularly accentuating the detection of corrupted data rather than outliers. Current state of the art models, such as Isolation forest, DBSCAN also called “Density-Based Spatial Clustering of Applications with Noise” and others, are reliant on fine-tuning parameters to provide high accuracy and recall, but they also have a significant level of uncertainty when factoring the corrupted data. In the present work, the authors look into the most niche performance issues of several unsupervised learning algorithms for linear and clustered corrupted datasets. Also, a novel PAACDA algorithm is proposed which outperforms other unsupervised learning benchmarks on 15 popular baselines including K-means clustering, Isolation forest and LOF (Local Outlier Factor) with an accuracy of 96.35% for clustered data and 99.04% for linear data. This article also conducts a thorough exploration of the relevant literature from the previously stated perspectives. In this research work, we pinpoint all the shortcomings of the present techniques and draw direction for future work in this field.

Multicriteria selection of a method for processing multispectral Earth remote sensing data

The article is devoted to the use of qualimetry methods for models and poly model complexes in order to solve one of the relevant engineering problems-automation of selecting methods for calculating Earth remote sensing (ERS) data processing when analyzing the state of complex natural and technical systems. The proposed approach was discussed using the example of choosing methods for calculating forest sustainability indicators. A typical situation was considered when alternative methods and models can be applied at each stage of data processing. The essence of the proposed approach is to formulate and solve the task on multi criteria comparative analysis of processing methods based on a set of indicators, which include costs, required for implementation one or another method, efficiency, which refers to calculation duration of the analyzed sustainability indicator, and an indicator reflecting the quality of the solution-accuracy of calculations result. The solution algorithm was illustrated within an example of choosing the method for assessing consequences of the forest fire. The selection results were presented in the form of a table, which allows the user to evaluate losses and gains in the values of partial indicators when moving from applying one method to another. The proposed algorithmization of the selection task determines possibility for its automation and, thereby, simplifying application of complex methods for processing ERS data for the end user. In addition, the possibilities and degree of validity for scaling the results of processing ERS data from individual areas to large forest areas are expanding.