Processing System Research Articles

Integration of WebODM Platform as Photogrammetry Cloud Processing and GeoNode as Corporate Spatial Data Infrastructure to Support the Acceleration of Geospatial Data Communication Flow in PT. Waskita Karya Tbk.

Abstract This research explores the integration of two platforms, namely WebODM and GeoNode, with Geoserver as its connecting platform. This platform integration is a solution to support the acceleration of geospatial data communication flow in PT. Waskita Karya Tbk. The main objective of the research is to combine the advantages of Photogrammetry Cloud Processing owned by WebODM with the Spatial Data Infrastructure features owned by GeoNode, thus creating an efficient and integrated system for processing and storing geospatial data. The research methodology involves user needs analysis, system integration development, as well as testing and performance evaluation of the implemented system. The research results indicate that the integration of WebODM and GeoNode has successfully created an environment that allows users to easily perform cloud-based photogrammetry processes and efficiently store and share geospatial data. With this system, PT. Waskita Karya can improve its operational performance in terms of geospatial data processing and distribution, as well as accelerate communication flow between various involved units. The implication of this research is that platform integration like this can be an effective solution for companies managing geospatial data on a large scale.

Ultra-fine quantitative removal of silicon materials by femtosecond laser

The development of miniaturized electronic devices is highly dependent on precision manufacturing techniques. To achieve device miniaturization, material removal and processing accuracy down to the submicrometer or even nanometer level are required. The precision machining of the mass block, a key component of the silicon micro-gyroscope, is taken as a reference. To achieve the precision positioning function of the micro-gyroscope, the mass at a certain position of the Si-based mass block must be precisely controlled. Therefore, the ultra-fine quantitative removal process of silicon materials is studied. A femtosecond laser processing system is constructed, and the evolution of micro–nanostructure and etching depth on the silicon surface is explored. An ultra-fine quantitative removal process is developed to achieve quantitative removal of different etching depths on the silicon surface. The experimental results show that the minimum removal thickness can be 0.502 µm. The processing index requirement for a silicon surface removal thickness of less than 0.001 mm has been fulfilled. It is critical to resolve the issue of high-quality manufacturing of miniaturized silicon-based devices.

An Improved Photovoltaic Differential Power Processing System Using Bidirectional Boost-Type Converters With Synchronous Switching

An Improved Photovoltaic Differential Power Processing System Using Bidirectional Boost-Type Converters With Synchronous Switching

Implementation of Web-Based Administrative Payment Information System Using Laravel 10 Framework

The development of information technology drives the need for an efficient data processing system, especially in the education sector. Madrasah Tsanawiyah NU 14 Kaligading manages financial administration manually, which causes various obstacles such as data inaccuracy and slow processes. This study aims to develop a web-based information system using the Laravel 10 framework to manage tuition payments in a more structured manner. The waterfall software development method encompasses needs analysis, system design, coding, testing, and maintenance. This system is designed to facilitate the management of school financial data with main features including user data input, payments, and student arrears. The implementation results show that the system can improve the efficiency and accuracy of financial management, accelerate the administration process, and facilitate real-time data access. In addition, this system also supports long-term data archiving, improves the effectiveness of administrative staff, and minimises errors in recording. This implementation is expected to help schools utilize modern technology to improve the quality of management and administrative services.

Insight into the impact of various processing stages on metabolites and flavors in jujube jam

Essential to the determination of the ultimate and flavor of jujube jam are various processing stages. Nevertheless, the alterations in metabolites and flavor chemistry throughout the processing of jujube jam are poorly comprehended. This research employed metabolomics, flavor analysis, and microbial indicators to examine the impact of distinct processing stages on the metabolites and flavor profile of jujube jam. The research findings indicated that the sterilization stage (SJ) was the most favorable stage for metabolite accumulation. Hexahydro-pseudoketone and 2-methylbutyraldehyde, compounds responsible for off-odors, exhibited a significant reduction following the concentration stage (NS). The distinctive flavors detected in jujube jam included floral, citrus, sweet and sour, as well as cheesy notes. Furthermore, the alterations observed in microbial indicators confirmed that the jujube jam products adhered to the established jam production benchmarks. In summary, these findings offer a foundational framework for the creation of a regulated processing system and for the improvement of jujube jam quality, thereby providing valuable guidance for the targeted production of premium jujube jam.

Smart Home for Supporting Elderly Based On Ultrawideband Positioning System

In 2017, the rate of dependency among the elderly was reported to be at 13.28%, which was problematic, due to the limited number of caregivers to assist them at all times. To address this issue, a robotic service and vital sign-based system were developed, but it was found to be insufficient for monitoring the activities of the elderly. Therefore, this study aimed to address the high dependency rates of elderly individuals who required constant support and care to survive by designing an ultrawideband-based positioning system. The system consisted of five sub-systems, including an indoor positioning system, a database system, a data processing system, an actuator system, and an application user interface. The system testing phase revealed several important findings, including that the position coordinates of the elderly were accurately read with differences of only 98.884 mm and 279.94 under Line of Sight and Non-Line of Sight conditions, respectively. Furthermore, the initial error rate of 164.39% was successfully reduced to only 1.096% by applying the average filter method in the data processing system. The actuator system also showed an impressive accuracy rate of 98% success, while the Android-based application user interface received a high user experience rate of 92.3%. Overall, these findings suggested that the ultrawideband-based positioning system had significant potential to support smart homes for the elderly and improve their quality of life.

Detection of Damage Motor and Coal Crusher in Power Plant Tanjung Enim 3 X 10 MW Using Vibration Analysis

Motor and coal crushers are critical in coal size reduction for efficient energy production. Continuous operation often impacts their functionality, necessitating effective maintenance methods. This study employs vibration analysis to monitor the health and detect potential faults in the motor and coal crusher. Vibration values, measured under load and no-load conditions, were analyzed in vertical, horizontal, and axial directions using ISO 10816 standards. Results show that while vibration levels remained within safe limits, increased readings in specific areas, particularly at the Crusher NDE in axial direction under load conditions, suggest potential issues like misalignment or bearing wear. Prominent vibration peaks at lower frequencies in the spectrum further support these findings. This research underscores the importance of routine vibration monitoring to preemptively address machine faults, ensuring optimized operation and minimized downtime. Vibration analysis proves effective for predictive maintenance and operational reliability in coal processing systems.

Sign Language Translator via Smartphone Image Analysis using Convolutional Neural Network

This study presents the design and implementation of a smartphone-based image processing system aimed at translating sign language gestures into text in real-time. The primary objective was to create a platform that enhances communication between deaf individuals and non-sign language users through the accurate recognition of sign language gestures. Using Kotlin programming and MediaPipe, the system captures and processes gestures, converting them into readable text. The results demonstrated that the system is capable of recognizing and translating various sign language gestures with a high degree of accuracy and efficiency. The successful deployment of this system highlights its potential to break down communication barriers and offers a foundation for future improvements, including expanded sign language support and performance optimization for real-world applications.

Magnetohydrodynamic micropolar nanofluid flow with pulsation in a vertical permeable channel with Brownian motion, thermophoresis, and thermal radiation: a Buongiorno model approach

Abstract The current paper investigates the magnetohydrodynamic pulsating flow of micropolar nanofluid in a vertical porous channel by using Buongiorno’s nanofluids approach and utilizing entropy generation. The effects of thermal radiation, viscous dissipation, Brownian motion, and thermophoresis are considered. This finding holds implications for the fields of biomedical engineering, food processing systems, cancer treatment, pressure spikes, and nano-drug delivery in arteries. In this case, the
governing partial differential equations (PDEs) are converted into ordinary differential equations (ODEs) using the perturbation approach and we used Runge-Kutta fourth order method for the numerical approach. Graphical representations illustrate the
effects of nanoparticle concentration, velocity, temperature, and micro-rotation across various standard physical parameters. Furthermore, analysis and tabular presentation of the heat and mass transfer rates are provided. The data obtained indicate
that while raising the Hartmann number and coupling parameter decreases velocity profiles then increasing the Grashof number enhances velocity. The temperature of a micropolar nanofluid rises with increased Brownian motion and thermophoresis,
and falls with increased magnetic field and thermal radiation. The concentration of micropolar nanofluid is reducing by enhancing the thermophoresis and chemical reaction parameter. The rate of heat transfer is increasing as a result of increases
in thermal radiation, viscous dissipation, thermophoresis, and Brownian motion values. The heat transfer rate rises with increase values of the viscous dissipation, thermal radiation, the Brownian motion, and the thermophoresis parameters. This
study is useful for various real life applications like biological systems, biotechnology, pharmaceutical medicine, nano-drug development, and engineering industries.

Recognition analysis of spiral and straight-line drawings in tremor assessment.

No standard, objective diagnostic procedure exists for most neurological diseases causing tremors. Therefore, drawing tests have been widely analyzed to support diagnostic procedures. In this study, we examine the comparison of Archimedean spiral and line drawings, the possibilities of their joint application, and the relevance of displaying pressure on the drawings to recognize Parkinsonism and cerebellar dysfunction. We further attempted to use an automatic processing and evaluation system. Digital images were developed from raw data by adding or omitting pressure data. Pre-trained (MobileNet, Xception, ResNet50) models and a Baseline (from scratch) model were applied for binary classification with a fold cross-validation procedure. Predictions were analyzed separately by drawing tasks and in combination. The neurological diseases presented here can be recognized with a significantly higher macro f1 score from the spiral drawing task (up to 95.7 %) than lines (up to 84.3 %). A significant improvement can be achieved if the spiral is supplemented with line drawing. The pressure inclusion in the images did not result in significant information gain. The spiral drawing has a robust recognition power and can be supplemented with a line drawing task to increase the correct recognition. Moreover, X and Y coordinates appeared sufficient without pressure with this methodology.

Discerning Emittable from Extractable Chemicals Identified in Consumer Products by Suspect Screening GCxGC-TOFMS.

Characterization of chemicals in household products is important for understanding this potential source of chemical exposure. Increasingly, suspect screening and nontargeted analysis techniques are used to characterize as many chemical signatures as possible. Solids such as household products are most conveniently prepared using solvent extraction, revealing what chemicals are contained within the product matrix but providing no information about the potential of those chemicals to leave the matrix and cause actual exposure. In this work, the profile and relative abundances of "extractable" chemical signatures found after solvent extraction are compared to those "emittable" to the headspace for 81 household products analyzed by two-dimensional gas chromatography time-of-flight mass spectrometry. This study retrospectively fuses data collected in separate efforts over 3.8 years and 13 analytical batches. Management of the data is made possible by recent developments in processing systems for complex data such as Highlight. Compounds were generically classified as aromatic heteroatom, aromatic hydrocarbon, glycol, hydrocarbon, long chain heteroatom, nonaromatic heteroatom, and unknown/unclassified. Class-based retention time and abundance trends were observed. Liquid extraction resulted in the greatest number of features and the highest relative abundances, while low temperature emission conditions produced the smallest number of features and lowest relative abundances.

Thermally Stable Ag2Se Nanowire Network as an Effective In‐Materio Physical Reservoir Computing Device

AbstractThe artificial intelligence (AI) paradigm shifts from software to implementing general‐purpose or application‐specific hardware systems with lower power requirements. This study explored a material physical reservoir consisting of a material random network, called in‐materio physical reservoir computing (RC) to achieve efficient hardware systems. The device, made up of a random, highly interconnected network of nonlinear Ag2Se nanojunctions as reservoir nodes, demonstrated the requisite characteristics of an in‐materio physical reservoir, including but not limited to nonlinear switching, memory, and higher harmonic generation. The power consumption of the in‐materio physical reservoir is 0.07 nW per nanojunctions, confirming its highly efficient information processing system. As a hardware reservoir, the devices successfully performed waveform generation tasks. Finally, a voice classification by an in‐materio physical reservoir is achieved over 80%, comparable to an RC software simulation. In‐materio physical RC with rich nonlinear dynamics has huge potential for next‐generation hardware‐based AI.

UNIVERSAL BINARY FORMAT FOR FAST DATA TRANSMISSION OVER THE NETWORK

In the context of modern approaches to application development, it is increasingly necessary to use distributed data processing systems, for example, in a microservice architecture. Here, the issue of fast data exchange between parts of the same application becomes relevant. The article discusses a new format for binary data encoding of the non-fixed, supported by the cross-platform approach and with the ability to implement serialization and deserialization operations in different languages using a universal data representation method. The efficiency degree of the new algorithms is deter-mined by comparing them with the results for another formats and frameworks that are widely used at the moment. The analysis shows that, taking into account the limitations imposed by the problem being solved, the new data presentation format provides a higher exchange rate due to optimization of serialization-deserialization algorithms.

A Review on the Application of Automatic Text Summarization and Its Bias Analysis

Abstract. In the era of information explosion, the Internet produces a large number of text documents daily, providing rich information but also posing a challenge: how to swiftly extract key information. Traditional manual reading is time-consuming and inadequate for handling vast data. Thus, automatic text summarization technology emerges as a crucial solution. This paper reviews the application and deviation analysis of this technology across various fields, focusing on addressing shortcomings of traditional methods, such as initial cluster center selection and redundancy. An automatic text summarization method based on an improved TextRank algorithm and K-Means clustering is introduced. Existing methods often struggle with inaccurate initial clustering center selection and high summary redundancy, especially with long texts, resulting in summaries that fail to reflect core content accurately. Furthermore, the widespread use of pre-trained language models introduces potential biases that can propagate to downstream tasks, affecting summary accuracy and impartiality. To address these issues, this paper proposes an innovative automatic text summarization method that optimizes initial clustering center selection and clustering refinement strategies to enhance summary accuracy and readability. Additionally, it discusses name-nationality bias in pre-trained language models and its propagation in text summary tasks, offering a theoretical foundation and practical guidance for developing a more just and reliable Natural Language Processing (NLP) system.

Leveraging Distributed Processing for Enhanced Data Cleaning and Mining: Principles, Techniques, and Case Studies

Abstract. In the era of big data, the volume, variety, and velocity of data generated pose significant challenges for data cleaning and mining processes. Traditional approaches to data cleaning and mining often struggle to handle large datasets efficiently, leading to increased processing time and reduced accuracy. Leveraging distributed processing techniques can significantly enhance the efficiency and effectiveness of these processes. This paper explores the principles behind distributed processing, particularly in the context of data cleaning and mining. It delves into various techniques, including MapReduce, distributed databases, and parallel processing, highlighting their advantages in managing large datasets. Furthermore, the paper presents case studies that illustrate the application of distributed processing in real-world scenarios, demonstrating how these techniques can be employed to achieve cleaner, more accurate data and more insightful mining results. Through these case studies, the paper also discusses the challenges and considerations associated with implementing distributed processing systems, such as data distribution, fault tolerance, and the need for specialized hardware and software. The findings suggest that while distributed processing offers substantial benefits, careful planning and execution are required to fully realize its potential in data cleaning and mining.

Vision-Transformer Model Validation Image Dataset

The removal of plastic contamination from cotton lint is a critical issue for the U.S. cotton industry. One primary source of this contamination is the plastic wrap used on cotton modules by John Deere round module harvesters. Despite rigorous efforts by cotton ginning personnel to eliminate plastic during module unwrapping, fragments still enter the gin’s processing system. To address this, we developed a machine-vision detection and removal system using low-cost color cameras to identify and expel plastic from the gin-stand feeder apron, preventing contamination. However, the system, comprising 30–50 ARM computers running Linux, poses significant challenges in terms of calibration and tuning, requiring extensive technical knowledge. This research aims to transform the system into a plug-and-play appliance by incorporating an auto-calibration algorithm that dynamically tracks cotton colors and excludes plastic images to maintain calibration integrity. We present the image dataset that was used to validate the design, consisting of several key AI Vision-Transformer image classifiers that form the heart of the auto-calibration algorithm, which is expected to reduce setup and operational overhead significantly. The auto-calibration feature will minimize the need for skilled personnel, facilitating the broader adoption of the plastic removal system in the cotton ginning industry.

Controllable switching based on vortex–antivortex pairs in exciton–polariton condensates

Abstract Vortex–antivortex pairs hold significant prospect for applications in high-capacity optical communications, multiparticle manipulations, and data processing systems. In this work, based on vortex–antivortex pairs, we explore a straightforward method to produce a 1–4 optical switch in a polariton condensate with a C-shaped potential. The switch, seeded by a degenerate state containing two vortex–antivortex pairs, can selectively target four objective states: two orthogonal states with a single vortex–antivortex pair and two concentric vortices with opposite circulations. All these topological states are stable states with the application of a single incoherent pump, while the switch is activated by an additional coherent control pulse.

Developing Scalable Microservices for High-Volume Order Processing Systems

Developing Scalable Microservices for High-Volume Order Processing Systems

Optimization of steam-methane reforming process using PSA off gas

This study aims to analyze the effect of PSA off gas utilization on fuel processing system combined with PSA (Pressure Swing Adsorption) system to produce more than 99% hydrogen for LT-PEMFC (Low Temperature Proton Exchange Membrane Fuel Cell) supply. An analysis was performed to optimize the operating conditions of a fuel processing system coupled with a PSA system by utilizing PSA off-gas to supply hydrogen to a 10 kW LT-PEMFC system. The performance and efficiency of the entire system were investigated at a steam-methane reactor temperature of 600 °C–800 °C, a water gas conversion reactor temperature of 200 °C–400 °C, a PSA recovery ratio of 60%–95%, a pressure of 1 bar, and 9 bar using Aspen Plus®, Aspen adsorption®, and Aspen Energy Analyzer® simulators. The sensitivities of various parameters affecting the process were analyzed to obtain optimized conditions for the entire system. Simulation results show that the optimal reformer temperature depends on the PSA recovery. The overall maximum efficiency is 78.67% at a process pressure of 9 bar the PSA recovery ratio of 95% and a temperature of 800 °C. In contrast, the highest efficiency under the 1 bar process condition is 70.55%, with an operating temperature of 600 °C and PSA recovery of 95%, which is relatively lower than the 9 bar process. Subsequently, pinch analysis and economic analysis including exergy analysis according to PSA recovery were conducted on these processes, showing that the thermal efficiency of the 9 bar process is relatively higher. The economic analysis indicated that the optimal profit is achieved at a PSA recovery of approximately 70∼75% and more than 40.2% of energy can be saved by utilizing PSA off gas to supply reaction heat for methane-steam reforming.

Big data processing using hybrid Gaussian mixture model with salp swarm algorithm

The traditional methods used in big data, like cluster creation and query-based data extraction, fail to yield accurate results on massive networks. To address such issues, the proposed approach involves using the Hadoop Distributed File System (HDFS) for data processing, the map-reduce programming paradigm for data processing, and query optimization techniques to quickly and effectively extract accurate outcomes from a variety of options with a high processing capacity. The methodology proposed in this work makes use of Gaussian Mixture Model (GMM) for data clustering and the Salp Swarm Algorithm (SSA) for optimization. The security of preprocessed data stored on networked clusters with interconnections has been ensured by SHA algorithms. Finally, incorporating into consideration the important parameters, evaluation findings for the experimental performance of the model in the indicated methodology are produced. For this work, the estimated range of input file sizes is 60–100 MB. The processing of 100 MB of input files yielded an accuracy of 96% and results for specificity and sensitivity of 90% and 93%, respectively. The outcomes have been compared with well-known methods like fuzzy C-means and K-means approaches, and the results show that the proposed method effectively distributes accurate data processing to cluster nodes with low latency. Moreover, it uses the least amount of memory resources possible when operating on functional CPUs. As a result, the proposed approach outperforms existing techniques.