A360 Bot Framework: Empowering Smart Robotic Process Automation Solutions

This research paper explores the significance of the “A360 Bot Framework” in Automation 360 (A360) platform. A360 is Automation Anywhere’s cloud-based automation platform designed to make business processes more efficient. It’s known for its user-friendly interface, which allows both technical and non-technical users to use it effectively. Automation 360 is versatile, offering a range of tools to automate tasks, manage complex workflows, and integrate various applications. It empowers users to create customized solutions for their specific needs. Being cloud-based it ensures scalability, security, and real-time updates, making it a top choice in the fast-paced digital world. As demand for A360 rises, having a structured way to develop bots becomes crucial. The paper introduces the “A360 Bot Framework” as a guiding approach for bot developments. This framework ensures consistency and scalability, especially when working with both professional developers and non-technical users. It outlines key elements like setting up work folders, managing logs, dealing with errors, and ensuring secure bot execution. Ultimately, the “A360 Bot Framework” is presented as a foundational structure that enhances consistency, resiliency, and development efficiency. By following predefined practices and templates, bot developers can mitigate risks and streamline debugging processes. This framework accelerates the bot development lifecycle, allowing developers to focus on specific functionalities and value-added features. The research paper aims to provide insights into the benefits of adopting the A360 Bot Framework and its potential to revolutionize A360 bot development practices, leading to more efficient and effective automation solutions.

How Does Robotics Process Automation (RPA) Affect Project Management Practices

The main purpose of this literature review is to understand the effectiveness and impacts of Robotics Process Automation (RPA) on the practices of Project Management (PM). For attaining the purposes of the research study, an extensive literature review was conducted, which helped in gaining a theoretical understanding. These findings were then justified with the help of current and relevant secondary sources in the analysis section. The findings suggested that RPA is quite advantageous for implementing in varied aspects of a business, especially in the field of project management. However, it can pose several challenges, which need to be taken into due consideration by the organizations during execution such as the capabilities of the employees and the abilities of the existing systems to incorporate automation, among others. It was ultimately concluded that RPA is highly advantageous for project management teams but its effective implementation is the key to success.

Maximizing the Efficiency of Automation Solutions with Automation 360: Approaches for Developing Subtasks and Retry Framework

I present a solution that explores the use of A360 subtasks as a comparable concept to functions in programming. By leveraging subtasks as reusable and maintainable functions, users can efficiently develop customized high-quality automation solutions. Additionally, the paper introduces the retry framework, which allows for the automatic retrying of subtasks in the event of system or unknown exceptions. This framework enhances efficiency and reduces the manual effort required to retrigger bots. The A360 Subtask and Retry Framework templates provide valuable assistance to both professional and citizen developers, improving code quality, maintainability, and the overall efficiency and resiliency of automation solutions.

Smart Approaches to Efficient Text Mining for Categorizing Sexual Reproductive Health Short Messages into Key Themes

To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved access to information on various Sexual Reproductive Health topics through Short Messaging Service (SMS) messages. Over the years, the platform has accumulated millions of incoming and outgoing messages, which need to be categorized into key thematic areas for better tracking of sexual reproductive health knowledge gaps among young people. The current manual categorization process of these text messages is inefficient and time-consuming and this study aims to automate the process for improved analysis using text-mining techniques. Firstly, the study investigates the current text message categorization process and identifies a list of categories adopted by counselors over time which are then used to build and train a categorization model. Secondly, the study presents a proof of concept tool that automates the categorization of U-report messages into key thematic areas using the developed categorization model. Finally, it compares the performance and effectiveness of the developed proof of concept tool against the manual system. The study used a dataset comprising 206,625 text messages. The current process would take roughly 2.82 years to categorise this dataset whereas the trained SVM model would require only 6.4 minutes while achieving an accuracy of 70.4% demonstrating that the automated method is significantly faster, more scalable, and consistent when compared to the current manual categorization. These advantages make the SVM model a more efficient and effective tool for categorizing large unstructured text datasets. These results and the proof-of-concept tool developed demonstrate the potential for enhancing the efficiency and accuracy of message categorization on the Zambia U-report platform and other similar text messages-based platforms.

An Automated Process of Creating 3D City Model for Monitoring Urban Infrastructures

This paper describes the process of designing models and tools for an automated way of creating 3D city model based on a raw point cloud.Also,making and forming 3D models of buildings.Models and tools for creating tools made in the model builder application within the ArcGIS Pro software.An unclassified point cloud obtained by the LiDAR system was used for the model input data.The point cloud,collected by the airborne laser scanning system(ALS),is classified into several classes:ground,high and low noise,and buildings.Based on the created DEMs,points classified as buildings and formed prints of buildings,realistic 3D city models were created.Created 3D models of cities can be used as a basis for monitoring the infrastructure of settlements and other analyzes that are important for further development and architecture of cities.

数字化转型中RPA技术与配网抢修工单监控的融合应用

机器人流程自动化技术可以将人机交互的业务进行脚本化设计,通过逐一执行自动访问、执行、记录等模块化的功能,高效解决重复、复杂的流程,能够节约人工成本并可靠的完成既定任务。对RPA(Robotic process automation)技术进行了研究,分析其自动化执行工作流程的工作原理,针对配网抢修工单监控业务进行RPA开发,设计一套可定时触发轮询监控工单状态的RPA脚本。通过推广应用验证了所设计的配网工单监控RPA机器人的可行性,为进一步推广研究RPA技术提供参考。

Early Detection of Heartbeat from Multimodal Data Using RPA Learning with KDNN-SAE

Heartbeat detection stays central to cardiovascular an electrocardiogram(ECG)is used to help with disease diagnosis and management.Existing Convolutional Neural Network(CNN)-based methods suffer from the less generalization problem thus;the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed.In contrast,this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders(KDNN-SAE)that computes the disease before the exact heart rate by combining features from multiple ECG Signals.Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart beat.This work contained Training and testing stages,in the preparation part at first the Adaptive Filter Enthalpy-based Empirical Mode Decomposition(EMD)is utilized to eliminate the motion artifact in the signal.At that point,the robotic process automation(RPA)learning part extracts the effective features are extracted,and normalized the value of the feature then estimated utilizing the RPA loss function.At last KDNN-SAE prepared training for the data stored in the dataset.In the subsequent stage,input signal compute motion artifact and RPA Learning the evaluation part determines the detection of Heartbeat.So early diagnosis of heart failures is an essential factor.The results of the experiments show that our proposed method has a high score outcome of 0.9997.Comparable to the CIF,which reaches 0.9990.The CNN and Artificial Neural Network(ANN)had less score 0.95115 and 0.90147.

Photovoltaic energy balance estimation based on the building integration level

The photovoltaic module building integration level affects the module temperature and,consequently,its output power.In this work,a methodology has been proposed to estimate the influence of the level of architectural photovoltaic integration on the photovoltaic energy balance with natural ventilation or with forced cooling systems.The developed methodology is applied for five photovoltaic module technologies(m⁃Si,p⁃Si,a⁃Si,CdTe,and CIGS)on four characteristic locations(Athens,Davos,Stockholm,and Würzburg).To this end,a photovoltaic module thermal radiation parameter,PVj,is introduced in the characterization of the PV module technology,rendering the correlations suitable for building⁃integrated photovoltaic(BIPV)applications,with natural ventilation or with forced cooling systems.The results show that PVj has a significant influence on the energy balances,according to the architectural photovoltaic integration and climatic conditions.Keywords:Photovoltaic cooling;BIPV;Photovoltaic;Ventilation;Photovoltaic integration level in building【OA】(2)Graph⁃Based methodology for Multi⁃Scale generation of energy analysis models from IFC,by Asier Mediavilla,Peru Elguezabal,Natalia Lasarte,Article 112795 Abstract:Process digitalisation and automation is unstoppable in all industries,including construction.However,its widespread adoption,even for non⁃experts,demands easy⁃to⁃use tools that reduce technical requirements.BIM to BEM(Building Energy Models)workflows are a clear example,where ad⁃hoc prepared models are needed.This paper describes a methodology,based on graph techniques,to automate it by highly reducing the input BIM requirements found in similar approaches,being applicable to almost any IFC.This is especially relevant in retrofitting,where reality capture tools(e.g.,3D laser scanning,object recognition in drawings)are prone to create geometry clashes and other inconsistencies,posing higher challenges for automation.Another innovation presented is its multi⁃scale nature,efficiently addressing the surroundings impact in the energy model.The application to selected test cases has been successful and further tests are ongoing,considering a higher variety of BIM models in relation to tools and techniques used and model sizes.

Analyzing and Enabling the Harmonious Coexistence of Heterogeneous Industrial Wireless Networks

Nowadays multiple wireless communication systems operate in industrial environments side by side.In such an environment performance of one wireless network can be degraded by the collocated hostile wireless network having higher transmission power or higher carrier sensing threshold.Unlike the previous research works which considered IEEE 802.15.4 for the Industrial Wireless communication systems(iWCS)this paper examines the coexistence of IEEE 802.11 based iWCS used for delay-stringent communication in process automation and gWLAN(general-purpose WLAN)used for non-real time communication.In this paper,we present a Markov chain-based performance model that described the transmission failure of iWCS due to geographical collision with gWLAN.The presented analytic model accurately determines throughput,packet transaction delay,and packet loss probability of iWCS when it is collocated with gWLAN.The results of the Markov model match more than 90%with our simulation results.Furthermore,we proposed an adaptive transmission power control technique for iWCS to overcome the potential interferences caused by the gWLAN transmissions.The simulation results show that the proposed technique significantly improves iWCS performance in terms of throughput,packet transaction,and cycle period reduction.Moreover,it enables the industrial network for the use of delay critical applications in the presence of gWLAN without affecting its performance.

An adaptive thresholding for weld seam image recognition in real time

Image prooessing of wehl seam in real time is an importunity to make welding rohot be able to track weld seam. The algorithm described in this paper combines some image technologies, such as modified Sobel edge detector and Hough transformation function, and especially the thresholds for image processing are ore aled adaptively by Ineans of a neural network. aests proved that this algorithm has a high reliability and rapidity in distinguishing the position of weld seam even with noises. The algorithm can be used ac the basic program .for robot to track welding seam and furthermore for calculating 3 dimensional information plan robot movement automatically.

Automated synthesis of steady-state continuous processes using reinforcement learning

Automated flowsheet synthesis is an important field in computer-aided process engineering.The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis without any heuristics or prior knowledge of conceptual design.The environment consists of a steady-state flowsheet simulator that contains all physical knowledge.An agent is trained to take discrete actions and sequentially build up flowsheets that solve a given process problem.A novel method named SynGameZero is developed to ensure good exploration schemes in the complex problem.Therein,flowsheet synthesis is modelled as a game of two competing players.The agent plays this game against itself during training and consists of an artificial neural network and a tree search for forward planning.The method is applied successfully to a reaction-distillation process in a quaternary system.

PLATFORM SELECTION FOR COMPLEX SYSTEMS:BUILDING AUTOMATION SYSTEMS

Automation systems for buildings interconnect components and technologies from the information technology industry and the telecommunications industry.In these industries,existing platforms and new platforms（that are designed to make building automation systems work） compete for market acceptance and consequently several platform battles among suppliers for building automation networking are being waged.It is unclear what the outcome of these battles will be and also which factors are important in achieving platform dominance.Taking the fuzziness of decision makers＇ judgments into account,a fuzzy multi-criteria decision-making methodology called the Fuzzy Analytic Hierarchy Process is applied to investigate the importance of such factors in platform battles for building automation networking.We present the relative importance of the factors for three types of platforms（subsystem platforms,system platforms,and evolved subsystem platforms）.The results provide a first indication that the set of important factors differs per type of platform.For example,when focusing on other stakeholders,for subsystem platforms,the previous installed base is of importance;for system platforms,the diversity of the network of stakeholders is essential;and for evolved subsystem platforms,the judiciary is an important factor.

Hybrid Approach to Document Anomaly Detection:An Application to Facilitate RPA in Title Insurance

Anomaly detection(AD)is an important aspect of various domains and title insurance(TI)is no exception.Robotic process automation(RPA)is taking over manual tasks in TI business processes,but it has its limitations without the support of artificial intelligence(AI)and machine learning(ML).With increasing data dimensionality and in composite population scenarios,the complexity of detecting anomalies increases and AD in automated document management systems(ADMS)is the least explored domain.Deep learning,being the fastest maturing technology can be combined along with traditional anomaly detectors to facilitate and improve the RPAs in TI.We present a hybrid model for AD,using autoencoders(AE)and a one-class support vector machine(OSVM).In the present study,OSVM receives input features representing real-time documents from the TI business,orchestrated and with dimensions reduced by AE.The results obtained from multiple experiments are comparable with traditional methods and within a business acceptable range,regarding accuracy and performance.