Machine Technology Research Articles

Effectiveness of airborne particle monitoring technique for assessment of wear progression of mechanical components

For effective implementation of smart machine technology, there is a great demand for real-time monitoring of the wear progression of machine components. In this work, the effectiveness of assessing wear by monitoring the airborne wear particles was investigated. Sliding wear tests were performed using different combinations of stainless steel (SUS304) and alumina material pairs while monitoring the size distribution and the number of particles using a particle counter. The results showed that particles less than 2 μm in size accounted for more than 83% of the total number of airborne particles. However, particles greater than 2 μm account for 95% of the volume of airborne particles. Additionally, in the case of wear volume, the total wear volume, which is the sum of the plate and the ball wear volumes, should be similar to that of the total airborne particle volume. For the SUS304/SUS304 pair, the total wear volume and the total airborne particle volume were very similar. However, in the case of alumina/SUS304 and alumina/alumina, the total wear volume and total airborne particle volume were not similar, and the total wear volume was significantly higher than the total airborne particle volume. The effectiveness of the airborne particle monitoring technique depended strongly on the material pairs. This was due to the fact that particle dispersion behavior varied with respect to the wear mechanisms of the materials.

High-Precision Rotor Position Fitting Method of Permanent Magnet Synchronous Machine Based on Hall-Effect Sensors

The high-performance vector control technology of permanent magnet synchronous machines (PMSMs) relies on high-precision rotor position. The Hall-effect sensor has the advantages of low cost, simple installation, and strong anti-interference ability. However, it can only provide six discrete rotor angles in an electrical cycle, which makes high-precision vector control of PMSMs difficult. Hence, to obtain the necessary rotor position of PMSMs, a rotor position fitting method combining the Hall signal and machine flux information is proposed. Firstly, the rotor position signal output by the Hall-effect sensors is used to calibrate and update the stator flux obtained under pure integration. Then, based on the corrected stator flux and its relationship with current and angle, the rotor position and speed are obtained. Experimental verification shows that the rotor position observer combining Hall signal and flux information can reduce the initial value bias and integral drift caused by traditional average speed method hysteresis and pure integration method calculation of flux and can quickly and accurately track and estimate the rotor position, achieving high-performance vector control of PMSMs.

Sustainable practices: organisational citizenship behaviour and psychological contract fulfilment

PurposeSustainable practices are the modern-day necessities for organisations as the world is becoming highly dynamic. The purpose of this study is to examine the influence of sustainable training and creativity practices (STP and SCP) on organisational citizenship behaviour (OCB-individual and OCB-organisation) via the mediating role of psychological contract fulfilment (PCF).Design/methodology/approachA sample of 326 white-collar Indian service industry employees was collected. The data are analysed using structural equation modelling and random forest regression supervised learning (RFRSL).FindingsThe findings indicate that sustainable training practices (STP) had an indirect impact on organisational citizenship behaviour (OCB-I, OCB-O) via the mediating effect of transactional (T-PCF) and relational psychological contract fulfilment (R-PCF). In terms of sustainable creative practices (SCP), the impact on OCB-I was indirect due to T-PCF. In addition, R-PCF acts as a mediator between SCP and OCB-O. In the latter portion of the analysis, the RFRSL approach created a prediction model for T-PCF, R-PCF, OCB-I and OCB-O, with demographic characteristics such as industry experience, gender, age, etc. playing a constructive role.Originality/valueThe study conducts a combination of both traditional and newer technology (machine learning), resulting in highlighting the uniqueness of the relationship between variables and the role of demographic variables.

Alexander Hamilton’s Report on Manufactures and Industrial Policy

Hamilton’s 1791 state paper on manufactures is a forward-looking argument for US industrialization supported by public policies designed to encourage it. Conventional wisdom circa 1790, along with static considerations of comparative advantage indicated that the United States should stick to farming, export its agricultural surpluses, and import European manufactures. Mercantilist trade policies of the major European empires, however, were barriers to US exports. Hamilton therefore contended that US manufacturing using the latest machine technologies would alleviate the effects of European trade restrictions by creating domestic demand for agricultural surpluses. His report specifies industries worthy of support, and policy measures to encourage their development. During the century that followed, US governments adopted nearly all of Hamilton’s recommendations. These measures contributed to an average annual rate of growth of industrial output of 5 percent during that century, helping the United States to become the world’s leading manufacturing nation.

Human Resource Management and Technology: A study of the Status in Nepalese Enterprises

This article is concentrated on the study of use of technology in HR management system in Nepalese organizations. At present most of the most of the corporate organizations of Nepal use different forms of related software for managing human resource in an effective way to bring effectiveness in performance outcomes. They use technology in various HR activities consisting of recruitment, selection, training, development, financial transactions, performance appraisal, job analysis, HR planning, learning, performance management, career management, compensation management, and maintaining good labour and industrial relation. The main focus of the use of modern technology along with relevant software for managing HR activities is to bring specific system in organizational activities for improving performance to use resources consisting of money, material, machine and information technology in an effective way which helps to maximize productivity, maintaining quality and minimizing cost of products and services which facilitates to improving competitive strengths of the organization in the market. In practice it is found that most of the Nepalese enterprises especially unorganized sectors are still using manual system for managing HR activities of the organization.

Research on in-vivo electron paramagnetic resonance spectrum classification and radiation dose prediction based on machine learning

The in-vivo electron paramagnetic resonance (EPR) method can be used for on-site, rapid, and non-invasive detection of radiation dose to casualties after nuclear and radiation emergencies. For in-vivo EPR spectrum analysis, manual labeling of peaks and calculation of signal intensity are often used, which have problems such as large workload and interference by subjective factors. In this study, a method for automatic classification and identification of in-vivo EPR spectra was established using support vector machine (SVM) technology, which can in-batch and automatically identify and screen out invalid spectra due to vibration and dental surface water interference during in-vivo EPR measurements. In this study, a spectrum analysis method based on genetic algorithm optimization neural network (GA-BPNN) was established, which can automatically identify the radiation-induced signals in in-vivo EPR spectra and predict the radiation doses received by the injured. The experimental results showed that the SVM and GA-BPNN spectrum processing methods established in this study could effectively accomplish the automatic spectra classification and radiation dose prediction, and could meet the needs of dose assessment in nuclear emergency. This study explored the application of machine learning methods in EPR spectrum processing, improved the intelligence level of EPR spectrum processing, and would help to enhance the efficiency of mass EPR spectra processing.

Improvement of Productivity by Using Means of Lean Manufacturing, Just in Time, and Production Technology in Thai SMEs Manufacturing Industry

This study investigated the current situations, opportunities, and obstacles in manufacturing industry, and to create a model for improving the efficiency while reducing the levels of inventory. This study gathered information from in-depth interview using snowball sampling from 20 manufacturing companies in Thailand. The results revealed that the organization has implemented lean production principles as a strategic initiative to mitigate losses. The implementation of comprehensive work manuals, offered in diverse languages such as Thai, Burmese, Japanese, and English, serves as an additional mechanism to mitigate losses by ensuring clarity and understanding among the workforce. Furthermore, the integration of modern technologies, encompassing machine technology, robotics and artificial intelligence, is actively pursued to streamline production processes and consequently reduce losses. The results presented that companies refrain from adopting Just-In-Time production systems due to operational challenges associated with fulfilling customer orders. Specifically, frequent machine shutdowns for adjustments are necessitated, leading to elevated unit costs. Manufacturing companies in Thailand continue to harbor potential for growth, despite the inevitable rise in employee wages leading to increased operational costs. To navigate this challenging landscape, companies must remain adaptive, responding not only to shifts in customer preferences but also to advancements in technology, proactive examination of customer needs prior to initiating the production process, a paradigm shift from low-cost production to the premium-grade products addresses the demands of customers seeking superior product quality, and the process of broadening customer bases both overseas and globally, and the analysis of distinct customer groups in identifying new target demographics.

Diseminasi Teknologi Mesin Feed Grinder dan Pelatihan Pembuatan Silasi Berbahan Limbah pertanian untuk Menghasilkan Pakan Domba Bernutrisi

This community service activity aims to disseminate feed grinder machine technology and provide training on making silage from agricultural waste as an alternative, nutritious feed for sheep in collaboration with the Sumber Mendho Nusantara livestock group. The implementation method involves four stages: preparation, which focuses on identifying the partner's problems and needs; training, which includes socializing the use of agricultural waste and demonstrating the feed grinder machine; mentoring, which focuses on follow-up and monitoring previous activities; and reporting and sustainability to ensure that the program continues to be implemented by the partner. The outcomes of this community service are that the partners are able to understand and apply appropriate technology to process waste, such as cassava and corn cobs, into quality feed. This activity not only reduces the farmers' dependence on commercial feed but also increases operational efficiency and opens up new opportunities in agropreneurship. The results also indicate a positive change in the partners' mindset, skills, and ability to manage agricultural waste. Furthermore, the increased knowledge of operating and maintaining appropriate technology provides a strong foundation for the program's sustainability in the future. With the support of proper technology and training, the livestock group can improve the quality of animal feed and optimize local economic potential.

An IoT platform for smart metal forming

Abstract Manufacturers in the metal forming industry are paying attention on smart manufacturing with the industrial internet of things (IIoT). An innovation has been occurring by changing metal forming processes from analog to digital by utilizing servo press machine and IT technology for improving efficiency and worth of the mass production manufacturing technology and of ability for the SDGs. This paper aims to introduce initiatives to survey of the current situation and formulation of strategies on the digital transformation in metal forming promoted by the Sokeizai Centre in Japan. An IoT platform for smart metal forming which has been developed based on the strategies is also introduced. The platform consists of sensor system for visualization of forming processes, sensor data wireless communication using local 5G and data assimilation with process simulation. Several sensing systems developed for process visualization are introduced.

Opportunities to Enhance sCO2 Power Cycle Turbomachinery with Bearingless Motor/Generators

Thermal power cycles using sCO2 as a working fluid place extreme demands on their turbomachinery components and their electric motors/generators. In this paper, new system topologies for sCO2 turbomachinery are proposed which take advantage of “bearingless” electric machine technology to improve performance. Bearingless motors/generators are a new type of electric machine which integrate the functionality of active magnetic bearings into the existing hardware of an electric motor/generator. The existing electromagnetic surfaces and materials are reused to enable controllable production of radial forces on the machine shaft. This is envisioned to improve hermetic direct-drive turbomachinery systems by either augmenting existing bearings (i.e., bearing assist) or replacing existing bearings (i.e., bearing removal). The state-of-the-art technologies for several bearing types (gas foil bearings, externally pressurized porous (EPP) gas bearings, and active magnetic bearings) and electric machines are reviewed to motivate the introduction of bearingless technology. Two system designs using bearingless machines are proposed and compared against existing commercial solutions in terms of maximum shaft weight, number of passthroughs into the hermetic environment, cost, and complexity. A case-study bearingless motor/generator is assessed via simulations and a hardware prototype to investigate practical considerations for using bearingless technology in sCO2 turbomachinery. The proposed bearingless solutions have potential to enable a new generation of sCO2 turbomachinery with improved reliability, reduced complexity, and lower cost. This paper shows that by transforming the motor/generator already present in turbomachinery into a bearingless motor/generator, the technical challenges involved with sCO2 can be overcome without adding significant cost.

PENINGKATAN PRODUKTIVITAS USAHA KERIPIK IKAN KUNIR UD FARAH DENGAN PENERAPAN TEKNOLOGI MESIN VACUUM FRYING SERTA MANAJEMEN KESELAMATAN DAN KESEHATAN KERJA (K3) PADA KARYAWAN

Panarukan District, Situbondo Regency, has marine potential that supports marine fish-based processing industries, including UD FARAH in Paowan Village, a producer of turmeric fish chips. However, UD FARAH's production capacity is still low because it uses traditional equipment such as wood stoves. Technological development is needed to increase production efficiency and quality, which is expected to increase profits and meet growing market demand. The aim of this activity is to improve employee skills in operating Vacuum Frying Machines for the production of turmeric fish chips and strengthen their understanding of occupational safety and health (K3). UD FARAH faces traditional production problems which result in less than optimal product quality and quantity, as well as working conditions that do not meet K3 standards. Solutions include the application of Vacuum Frying Machine technology and K3 training to increase production efficiency and work safety. The program also includes mentoring and evaluation to ensure long-term success. After the service activities, UD FARAH experienced a significant increase in productivity and product quality with the implementation of the Vacuum Frying Machine. Employees' knowledge of technology and K3 also increases, which contributes to work safety and efficiency. In addition, the sustainability program includes marketing strategies and product diversification, such as the development of mango chips, which utilize local resources and open up new market opportunities. It is also hoped that the sustainability of this program will reduce UD FARAH's dependence on one type of product, increase competitiveness and create added value for the local community. This activity succeeded in improving the skills of UD FARAH employees in operating vacuum frying machines and implementing K3, as well as encouraging product diversification and improving marketing strategies to increase productivity and sales.

Integrating machine and deep learning technologies in green buildings for enhanced energy efficiency and environmental sustainability

A green building (GB) is a design idea that integrates environmentally conscious technology and sustainable procedures throughout the building’s life cycle. However, because different green requirements and performances are integrated into the building design, the GB design procedure typically takes longer than conventional structures. Machine learning (ML) and other advanced artificial intelligence (AI), such as DL techniques, are frequently utilized to assist designers in completing their work more quickly and precisely. Therefore, this study aims to develop a GB design predictive model utilizing ML and DL techniques to optimize resource consumption, improve occupant comfort, and lessen the environmental effect of the built environment of the GB design process. A dataset ASHARE-884 is applied to the suggested models. An Exploratory Data Analysis (EDA) is applied, which involves cleaning, sorting, and converting the category data into numerical values utilizing label encoding. In data preprocessing, the Z-Score normalization technique is applied to normalize the data. After data analysis and preprocessing, preprocessed data is used as input for Machine learning (ML) such as RF, DT, and Extreme GB, and Stacking and Deep Learning (DL) such as GNN, LSTM, and RNN techniques for green building design to enhance environmental sustainability by addressing different criteria of the GB design process. The performance of the proposed models is assessed using different evaluation metrics such as accuracy, precision, recall and F1-score. The experiment results indicate that the proposed GNN and LSTM models function more accurately and efficiently than conventional DL techniques for environmental sustainability in green buildings.

A good balance between the efficiency of ionizing radiation shielding and mechanical performance of various tin-based alloys: Comparative analysis

The Sn-Bix (x = 30, 40, 50, 60 and 70%) radiation shielding alloys were prepared using a melting process of the alloying material in a ceramic crucible at 200 °C for 120 min. The structural characterization for synthesized samples was carried out using X-ray Diffraction technique (XRD). The hardness and elastic properties of the prepared alloys was measured using micro-indentation creep technology and tensile test machine respectively. XRD pattern indicated that that the crystal size of Sn phase is lowered by increasing Bi concentration in the Sn matrix. The hardness decreases as the dwell time increases. The hardness values and elastic properties (in terms of young modulus) were significantly improved due to the decrease in the crystallite size by increasing Bi content. The stress exponent (n) value increase by increasing Bi content which mean that the mechanical properties improved due to increment of resistance. Furthermore, the produced alloys' nuclear shielding ability was investigated. The μm values were calculated using MCNP simulation code and XCOM software over a broad energy range of 0.015 MeV–15 MeV with good agreement between them. Several characteristics are estimated in order to properly understand the researched alloy's radiation and properties of neutron shielding. The findings showed that a higher Bi concentration causes the crystal size to decrease, improving the radiation shielding and mechanical qualities. The alloy Sn-70% Bi has a maximum increase of vicker hardness, tensile strength, and young's modulus. The findings of the shielding parameters indicate that the alloys under study are efficient gamma shielding materials in contrast to other typical shielding materials and materials that have been examined recently. The highest mechanical efficiency and rather strong gamma-ray shielding capabilities are found in the Sn–70Bi alloy. Owing to its ability to effectively balance mechanical performance and shielding, the Sn–70Bi are approved for use in radiation protection. In addition, when compared to all other manufactured alloys, typical neutron shielding materials, and recently investigated materials, the results further demonstrate that the Sn–70Bi alloy has the best neutron attenuation capability. Furthermore, in terms of mass stopping power (MSP) and projected range (PR), the Sn–70Bi alloy demonstrates the most effective attenuation for alpha particles (He+2) and protons (H+1). These results imply that the Sn–70Bi alloy provide superior mechanical performance and nuclear shielding for a range of applications including industrial, medicinal, and nuclear waste storage in the future.

Efektivitas Penerapan Presensi Kehadiran ASN Di Dinas Perhubungan Kabupaten Malang Provinsi Jawa Timur

Currently, technological advances are very rapid and sophisticated. It is not surprising that this progress is utilized by many sectors, both private and government. The government must use technology to develop more sophisticated and inventive government systems. One of them is a unique finger impression-based attendance method that is used to discipline employees in carrying out their duties. The purpose of this research is to find out how effective the use of unique finger impressions is to discipline employees at the Malang Regency Transportation Service, how effective the use of unique finger impressions is to increase employee discipline, and the steps taken to improve employee discipline. This study is based on an inductive approach combined with a descriptive approach. Data collection methods include interviews, observation, and documentation. According to Tangkilisan (2005: 141), there are four ways to evaluate effectiveness: target achievement, adaptability, job satisfaction, and responsibility. By using unique finger impressions, employee discipline and organizational performance improve. However, there are several challenges, one of which is that employees continue to come for unique finger impressions rather than performance, which hinders them from completing tasks on time. One of the efforts made by the Malang Regency Transportation Service is to carry out direct supervision from the leadership and apply disciplinary penalties objectively and according to procedures. The author suggests improving or updating unique fingerprint scanning machine technology, especially the sensitivity of the scanner in reading fingerprints, and adding one unit of unique fingerprint scanning machine

INOVASI TEKNOLOGI MENUJU ZERO WASTE PENANGANAN DARURAT SAMPAH DI KOTA MAGELANG

The problem of domestic waste has become quite serious, considering the volume and rate of accumulation are increasing with the increasing population and economic activities. The Final Waste Disposal Site (TPSA) in Magelang City is currently overloaded because Magelang City only has one Final Waste Disposal Site (TPSA). If strategic and appropriate steps are not taken to reduce the waste entering the Banyu Urip Final Waste Disposal Site (TPSA), then within a maximum of 1-2 years the Banyu Urip Final Waste Disposal Site (TPSA) will not be able to receive waste shipments from Magelang City. The incomplete resolution of waste in Magelang City is due to the unavailability of waste processing technology that produces zero waste and is environmentally friendly. The policy analysis evaluation approach used is the Grid Analysis (Matrix Analysis) method. The results of the study recommend the application of waste processing machine technology by the Environmental Service. This mechanization technology is expected to be able to replace the previous method (open dumping and landfill) with waste mechanization with the advantages of being relatively odorless, relatively no social conflict and saving land. The mechanization technology consists of waste sorting machines, plastic shredding machines, organic waste pulping machines, conveyors and incinerators.

Penerapan Mesin Pencetak Pelet Untuk Pakan Ternak Di Desa Sambigede Pakisaji Malang

Sambigede Village, District. Pakisaji is one of the areas that has high potential in the livestock sector in Malang Regency, the potential is around 20 thousand poultry, 100 cattle, and there are also 3 fish farming groups. So far, the animal feed pellets given by breeders are natural feed in the form of bran and other grains. Animal feed pellets, which are often referred to as pellets, are one of the most important things to pay attention to in the livestock industry, Pellets are a source of nutrition that has a huge impact on livestock, especially poultry breeders, but breeders in this village still have difficulty producing pellets because limited knowledge and technology. So it is necessary to implement an animal feed pellet printing technology machine to provide animal feed pellets in the form of feed pellets independently. The aim of this community service is to diffuse Appropriate Technology for animal feed pellet printing machines by providing knowledge and skills about how to make feed pellet products, and operation and maintenance of animal feed pellet printing machines to partners. Based on the implementation that has been carried out, production increases can be actualized to increase the production of feed pellets 40 kg/hour, so that the implementation of an animal feed pellet printing machine can provide a relatively large contribution to increasing productivity, quality, and turnover for community groups in Sambigede Village, Pakisaji, Malang.. Kata Kunci: pellet machine, pellet press machine, appropriate technology

Quantitative analysis of zearalenone in wheat leveraging support vector machine and olfactory visualization technology

Zearalenone (ZEN) is a contaminant found naturally in grains such as wheat that poses a potential threat to human and animal health. This study describes a colorimetric sensor array detection system based on chemically reactive dyes and chemometric algorithms for the determination of ZEN in wheat. Firstly, six chemically responsive dyes were screened to construct a colorimetric sensor array to obtain the response fingerprints of the volatile components of wheat. The RGB differences of the chemically reactive dyes before and after the response were then calculated to obtain the color component data of the image. The maximum information coefficient (MIC) and ReliefF methods were then used to select the best color features. Finally, the Support Vector Machine Regression (SVR) prediction model was developed and validated based on the optimized features on 132 sample sets. The results showed that the SVR model developed on the initial 132 wheat samples had good predictive ability. Its root mean square error (RMSE) on the prediction set was 38.1625 μg∙kg−1 with a correlation coefficient (R) 0.9516. In particular, the SVR model combining 13 color components reduced the RMSE on the prediction set to 30.4484 μg∙kg−1, with an improved R of 0.9695. Olfactory visualization techniques were shown to be effective for the determination of ZEN in wheat. This study is expected to provide a scientifically novel approach for quality stabilization during the storage of wheat or other cereals.

Multi-objective optimization and inverse design of complementary field-effect transistor using combined approach of machine learning and non-dominated sorting genetic algorithms for next-generation semiconductor devices

Complementary field-effect transistors (CFETs), which are structures in which different types of transistors are vertically stacked with a shared control gate, are being focused on for continuing to satisfy Moore's law by overcoming the limitations in pitch scaling. The structures of semiconductor devices become more complex as technology node shrinks, and interrelated multivariate parameters increase. In addition, predicting problems and proposing solutions by identifying complex patterns within extensive data collected for emerging semiconductor designs pose significant computational challenges and are inherently difficult. As a breakthrough in design technology co-optimization for advanced devices, this study developed a novel optimization framework integrating technology computer-aided design simulations, machine learning, and non-dominated sorting genetic algorithms. The developed framework provides unbiased optimal solutions, even in a high-dimensional objective space, while considering the tradeoff relationships between multiple variables. In addition, it enables inverse design to identify the design parameters of devices that satisfy specific electrical performance criteria using only a forward model, while achieving an error rate of less than 2%. Using this framework, we analyzed the operational mechanism of CFETs by comparing the inverse designs of various devices. This novel approach is particularly important when the design space is complex and extensive and is well suited for developing devices that emerge with technological advancements in the semiconductor industry.

Enhancing autonomous vehicle navigation using SVM-based multi-target detection with photonic radar in complex traffic scenarios

Efficient transportation systems are essential for the development of smart cities. Autonomous vehicles and Intelligent Transportation Systems (ITS) are crucial components of such systems, contributing to safe, reliable, and sustainable transportation. They can reduce traffic congestion, improve traffic flow, and enhance road safety, thereby making urban transportation more efficient and environmentally friendly. We present an innovative combination of photonic radar technology and Support Vector Machine classification, aimed at improving multi-target detection in complex traffic scenarios. Central to our approach is the Frequency-Modulated Continuous-Wave photonic radar, augmented with spatial multiplexing, enabling the identification of multiple targets in various environmental conditions, including challenging weather. Notably, our system achieves an impressive range resolution of 7 cm, even under adverse weather conditions, utilizing an operating bandwidth of 4 GHz. This feature is particularly crucial for precise detection and classification in dynamic traffic environments. The radar system's low power requirement and compact design enhance its suitability for deployment in autonomous vehicles. Through comprehensive numerical simulations, our system demonstrated its capability to accurately detect targets at varying distances and movement states, achieving classification accuracies of 75% for stationary and 33% for moving targets. This research substantially contributes to ITS by offering a sophisticated solution for obstacle detection and classification, thereby improving the safety and efficiency of autonomous vehicles navigating through urban environments.

Research on Flood Disaster Monitoring and Early Warning Technology Based on UAV Remote Sensing and AI Algorithm

This paper, based on UAV far-flung sensing science and synthetic brain algorithm, studies the monitoring and early warning technological know-how of flood catastrophes. Firstly, the far-flung sensing monitoring science of flood catastrophes is summarized. Then, blended with geographic records machine (GIS) technology, the UAV far-flung sensing science software in flood catastrophe monitoring is analyzed. Finally, combining the micro-UAV technological know-how and AI technology, the key applied sciences of the micro-UAV flood catastrophe monitoring and early warning device primarily based on AI imaginative and prescient are studied, along with the lookup of the micro-UAV key gadget and the lookup of AI imaginative and prescient key technology. The key device lookup of micro-UAV is the definition of mindset. Mindset A mindset lookup on key applied sciences of AI, imaginative and prescient broadly speaking, expounds the precept of CNN and introduces Faster RCNN. It lays the groundwork for the later lookup on flood catastrophe monitoring and early warning technological know-how primarily based on UAV far-off sensing and AI algorithms.