Search results for: tablet making machine

Authors: Amir Taghizadeh Vahed, Adithya Thaduri

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Shovels are the main components in the mining transportation system. The productivity of the mines depends on the availability of shovels due to its high capital and operating costs. The unplanned failure/shutdowns of a shovel results in higher repair costs, increase in downtime, as well as increasing indirect cost (i.e. loss of production and company’s reputation). In order to mitigate these failures, predictive maintenance can be useful approach using failure prediction. The modern mining machinery or shovels collect huge datasets automatically; it consists of reliability and maintenance data. However, the gathered datasets are useless until the information and knowledge of data are extracted. Machine learning as well as data mining, which has a major role in recent studies, has been used for the knowledge discovery process. In this study, data mining and machine learning approaches are implemented to detect not only anomalies but also patterns from a dataset and further detection of failures.

Keywords: maintenance, machine learning, shovel, conditional based monitoring

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Authors: Ali Moharrek, Hosein Mobli, Ali Jafari, Ahmad Tabataee Far

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Biomass is a material generally produced by plants living on soil or water and their derivatives. The remains of agricultural and forest products contain biomass which is changeable into fuel. Besides, you can obtain biogas and ethanol from the charcoal produced from biomass through specific actions. this technology was designed for as a useful Native Fuel and Technology in Energy disasters Management Due to the sudden interruption of the flow of heat energy One of the problems confronted by mankind in the future is the limitations of fossil energy which necessitates production of new energies such as biomass. In order to produce biomass from the remains of the plants, different methods shall be applied considering factors like cost of production, production technology, area of requirement, speed of work easy utilization, ect. In this article we are focusing on designing a biomass briquetting portable machine. The speed of installation of the machine on a tractor is estimated as 80 MF 258. Screw press is used in designing this machine. The needed power for running this machine which is estimated as 17.4 kW is provided by the power axis of tractor. The pressing speed of the machine is considered to be 375 RPM Finally the physical and mechanical properties of the product were compared with utilized material which resulted in appropriate outcomes. This machine is designed for Gathering Raw materials of the ground by Head Section. During delivering the raw materials to Briquetting section, they Crushed, Milled & Pre Heated in Transmission section. This machine is a Combine Portable Tractor unit machine and can use all type of Agriculture, Forest & Livestock Animals Resides as Raw material to make Bio fuel. The Briquetting Section was manufactured and it successfully made bio fuel of Sawdust. Also this machine made a biofuel with Ethanol of sugarcane Wastes. This Machine is using P.T.O power source for Briquetting and Hydraulic Power Source for Pre Processing of Row Materials.

Keywords: biomass, briquette, screw press, sawdust, animal wastes, portable, tractors

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Authors: Rene Hellmuth, Jorg Frohnmayer

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The requirements of the factory planning and the building concerned have changed in the last years. Factory planning has the task of designing products, plants, processes, organization, areas, and the building of a factory. Regular restructuring gains more importance in order to maintain the competitiveness of a factory. Even today, the methods and process models used in factory planning are predominantly based on the classical planning principles of Schmigalla, Aggteleky and Kettner, which, however, are not specifically designed for reorganization. In addition, they are designed for a largely static environmental situation and a manageable planning complexity as well as for medium to long-term planning cycles with a low variability of the factory. Existing approaches already regard factory planning as a continuous process that makes it possible to react quickly to adaptation requirements. However, digital factory models are not yet used as a source of information for building data. Approaches which consider building information modeling (BIM) or digital factory models in general either do not refer to factory conversions or do not yet go beyond a concept. This deficit can be further substantiated. A method for factory conversion planning using a current digital building model is lacking. A corresponding approach must take into account both the existing approaches to factory planning and the use of digital factory models in practice. A literature review will be conducted first. In it, approaches to classic factory planning and approaches to conversion planning are examined. In addition, it will be investigated which approaches already contain digital factory models. In the second step, an approach is presented how digital factory models based on building information modeling can be used as a basis for augmented reality tablet applications. This application is suitable for construction sites and provides information on the costs and time required for conversion variants. Thus a fast decision making is supported. In summary, the paper provides an overview of existing factory planning approaches and critically examines the use of digital tools. Based on this preliminary work, an approach is presented, which suggests the sensible use of digital factory models for decision support in the case of conversion variants of the factory building. The augmented reality application is designed to summarize the most important information for decision-makers during a reconstruction process.

Keywords: augmented reality, digital factory model, factory planning, restructuring

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Authors: Pin Xiu Chen, Shang Liang Chen

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A virtualized software controller is developed in this research to replace traditional hardware control units. This virtualized software controller transfers motion interpolation calculations from the motion control units of end devices to edge computing platforms, thereby reducing the end devices' computational load and hardware requirements and making maintenance and updates easier. The study also applies the concept of microservices, dividing the control system into several small functional modules and then deploy into a cloud data server. This reduces the interdependency among modules and enhances the overall system's flexibility and scalability. Finally, with containerization technology, the system can be deployed and started in a matter of seconds, which is more efficient than traditional virtual machine deployment methods. Furthermore, this virtualized software controller communicates with end control devices via wireless networks, making the placement of production equipment or the redesign of processes more flexible and no longer limited by physical wiring. To handle the large data flow and maintain low-latency transmission, this study integrates 5G technology, fully utilizing its high speed, wide bandwidth, and low latency features to achieve rapid and stable remote machine control. An experimental setup is designed to verify the feasibility and test the performance of this framework. This study designs a smart manufacturing site with a 5G communication architecture, serving as a field for experimental data collection and performance testing. The smart manufacturing site includes one robotic arm, three Computer Numerical Control machine tools, several Input/Output ports, and an edge computing architecture. All machinery information is uploaded to edge computing servers and cloud servers via 5G communication and the Internet of Things framework. After analysis and computation, this information is converted into motion control commands, which are transmitted back to the relevant machinery for motion control through 5G communication. The communication time intervals at each stage are calculated using the C++ chrono library to measure the time difference for each command transmission. The relevant test results will be organized and displayed in the full-text.

Keywords: 5G, MEC, microservices, virtualized software controller, smart manufacturing

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Authors: Gennady P. Ginkul, Sergey Yu. Soloviov

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The article proposes to use so-called "quotation-based algorithm" for simulation of decision making process in distributed expert systems and multi-agent systems. The idea was adopted from the techniques for group decision-making. It is based on the assumption that one expert system to perform its logical inference may use rules from another expert system. The application of the algorithm was demonstrated on the example in which the consolidated decision is the decision that requires minimal quotation.

Keywords: backward chaining inference, distributed expert systems, group decision making, multi-agent systems

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Authors: Sam Khozama, Ali M. Mayya

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Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

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Authors: Yogesh C. Sharma, A. Seetharaman

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The decision-making process in humans is a complicated system influenced by a variety of intrinsic and extrinsic factors. Human decisions have a ripple effect on subsequent decisions. In this study, the scope of human decision making is limited to employees. In an organisation, a person makes a variety of decisions from the time they are hired to the time they retire. The goal of this research is to identify various elements that influence decision-making. In addition, the environment in which a decision is made is a significant aspect of the decision-making process. Employees in today's workplace use artificial intelligence (AI) systems for automation and decision augmentation. The impact of AI systems on the decision-making process is examined in this study. This research is designed based on a systematic literature review. Based on gaps in the literature, limitations and the scope of future research have been identified. Based on these findings, a research framework has been designed to identify various factors affecting employee decision making. Employee decision making is influenced by technological advancement, data-driven culture, human trust, decision automation-augmentation, and workplace motivation. Hybrid human-AI systems require the development of new skill sets and organisational design. Employee psychological safety and supportive leadership influences overall job satisfaction.

Keywords: employee decision making, artificial intelligence (AI) environment, human trust, technology innovation, psychological safety

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Authors: Jin Uk Han, Hye Won Han, Hyo Lim Kang, Tae An Kim, Seung Ho Han

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Direct-drive generators for large-scale wind turbine, which are divided into AFPM(Axial Flux Permanent Magnet) and RFPM(Radial Flux Permanent Magnet) type machine, have attracted interest because of a higher energy density in comparison with gear train type generators. Each type of the machines provides distinguishable geometrical features such as narrow width with a large diameter for the AFPM-type machine and wide width with a certain diameter for the RFPM-type machine. When the AFPM-type machine is applied, an increase of electric power production through a multi-stage arrangement in axial direction is easily achieved. On the other hand, the RFPM-type machine can be applied by using its geometric feature of wide width. In this study, a hybrid two-stage direct-drive generator for 6.2MW class wind turbine was proposed, in which the two-stage AFPM-type machine for 5 MW was composed of two models arranged in axial direction with a hollow shape topology of the rotor with annular disc, the stator and the main shaft mounted on coupled slew bearings. In addition, the RFPM-type machine for 1.2MW was installed at the empty space of the rotor. Analytic results obtained from an electro-magnetic and structural interaction analysis showed that the structural weight of the proposed hybrid two-stage direct-drive generator can be achieved as 155tonf in a condition satisfying the requirements of structural behaviors such as allowable air-gap clearance and strength. Therefore, it was sure that the 6.2MW hybrid two-stage direct-drive generator is competitive than conventional generators. (NRF grant funded by the Korea government MEST, No. 2017R1A2B4005405).

Keywords: AFPM-type machine, direct-drive generator, electro-magnetic analysis, large-scale wind turbine, RFPM-type machine

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Authors: Norfiza Ibrahim, Norshuhada Shiratuddin, Siti Mahfuzah Sarif

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Decision-making system is used to facilitate people in making the right choice for their important daily activities. For the youth, proper guidance in making important decisions is needed. Their skills in decision-making aid decisions will indirectly affect their future. For that reason, this study focuses on the intelligent aspects in the development of intelligent decision support application. The aid apparently integrates Personality Traits (PT) and Multiple Intelligence (MI) data in development of a computerized personal decision aid for youth named as Youth Personal Decision Aid (Youth PDA). This study is concerned with the aid’s helpfulness based on the hybrid intelligent process. There are four main items involved which are reliability, decision making effort, confidence, as well as decision process awareness. Survey method was applied to the actual user of this system, namely the school and the Institute of Higher Education (IPT)’s students. An establish instrument was used to evaluate the study. The results of the analysis and findings in the assessment indicates a high mean value of the four dimensions in helping Youth PDA to be accepted as a useful tool for the youth in decision-making.

Keywords: decision support, multiple intelligent, personality traits, youth personal decision aid

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Authors: Joel Weymouth, Ramesh K. Karne, Alexander L. Wijesinha

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Bare Machine Internet is part of the Bare Machine Computing (BMC) paradigm. It is used in programming application ns to run directly on a device. It is software that runs directly against the hardware using CPU, Memory, and I/O. The software application runs without an Operating System and resident mass storage. An important part of the BMC paradigm is the Bare Machine Internet. It utilizes an Application Development model software that interfaces directly with the hardware on a network server and file server. Because it is “bare,” it is a powerful teaching and research tool that can readily display the internals of the network protocols, software, and hardware of the applications running on the Bare Server. It was also demonstrated that the bare server was accessible by laptop and by smartphone/android. The purpose was to show the further practicality of Bare Internet in Computer Engineering and Computer Science Education and Research. It was also to show that an undergraduate student could take advantage of a bare server with any device and any browser at any release version connected to the internet. This paper presents the Bare Web Server as an educational tool. We will discuss possible applications of this paradigm.

Keywords: bare machine computing, online research, network technology, visualizing network internals

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Authors: Abdallah A. Hlayel

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Operations research science (OR) deals with good success in developing and applying scientific methods for problem solving and decision-making. However, by using OR techniques, we can enhance the use of computer decision support systems to achieve optimal management for institutions. OR applies comprehensive analysis including all factors that affect on it and builds mathematical modeling to solve business or organizational problems. In addition, it improves decision-making and uses available resources efficiently. The adoption of OR by universities would definitely contributes to the development and enhancement of the performance of OR techniques. This paper provides an understanding of the structures, approaches and models of OR in problem solving and decision-making.

Keywords: best candidates' method, decision making, decision support system, operations research

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Authors: Joshua van Staden, Dane Brown, Karen Bradshaw

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Gun violence is a significant problem in modern-day society. Early detection of carried handguns through closed-circuit television (CCTV) can aid in preventing potential gun violence. However, CCTV operators have a limited attention span. Machine learning approaches to automating the detection of dangerous gun carriers provide a way to aid CCTV operators in identifying these individuals. This study provides insight into the relationship between human key points extracted using human pose estimation (HPE) and their intention to fire a weapon. We examine the feature importance of each keypoint and their correlations. We use principal component analysis (PCA) to reduce the feature space and optimize detection. Finally, we run a set of classifiers to determine what form of classifier performs well on this data. We find that hips, shoulders, and knees tend to be crucial aspects of the human pose when making these predictions. Furthermore, the horizontal position plays a larger role than the vertical position. Of the 66 key points, nine principal components could be used to make nonlinear classifications with 86% accuracy. Furthermore, linear classifications could be done with 85% accuracy, showing that there is a degree of linearity in the data.

Keywords: feature engineering, human pose, machine learning, security

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Benmansour Nassima, Benmansour Souheyla

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The appropriate integration of reward processing and decision making provided by the environment is vital for behavioural success and individuals’ well being in everyday life. Functional neurological investigation has already provided an inclusive image on affective and emotional (motivational) processing in the healthy human brain and has recently focused its interest also on the assessment of brain function in anxious and depressed individuals. This article offers an overview on the theoretical approaches that relate emotion and decision-making, and spotlights investigation with anxious or depressed individuals to reveal how emotions can interfere with decision-making. This research aims at incorporating the emotional structure based on response and stimulation with a Bayesian approach to decision-making in terms of probability and value processing. It seeks to show how studies of individuals with emotional dysfunctions bear out that alterations of decision-making can be considered in terms of altered probability and value subtraction. The utmost objective is to critically determine if the probabilistic representation of belief affords could be a critical approach to scrutinize alterations in probability and value representation in subjective with anxiety and depression, and draw round the general implications of this approach.

Keywords: decision-making, motivation, alteration, reward processing, response selection

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Authors: Yan Yan, Erhong Sun, Lin Peng, Xuchun Ye

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Aims: The present study aimed to explore Intensive Care Unit (ICU) nurses’ mental workload (MWL) and associated factors with it in performing human-machine tasks. Background: A wide range of emerging technologies have penetrated widely in the field of health care, and ICU nurses are facing a dramatic increase in nursing human-machine tasks. However, there is still a paucity of literature reporting on the general MWL of ICU nurses performing human-machine tasks and the associated influencing factors. Methods: A cross-sectional survey was employed. The data was collected from January to February 2021 from 9 tertiary hospitals in 6 provinces (Shanghai, Gansu, Guangdong, Liaoning, Shandong, and Hubei). Two-stage sampling was used to recruit eligible ICU nurses (n=427). The data were collected with an electronic questionnaire comprising sociodemographic characteristics and the measures of MWL, self-efficacy, system usability, and task difficulty. The univariate analysis, two-way analysis of variance (ANOVA), and a linear mixed model were used for data analysis. Results: Overall, the mental workload of ICU nurses in performing human-machine tasks was medium (score 52.04 on a 0-100 scale). Among the typical nursing human-machine tasks selected, the MWL of ICU nurses in completing first aid and life support tasks (‘Using a defibrillator to defibrillate’ and ‘Use of ventilator’) was significantly higher than others (p < .001). And ICU nurses’ MWL in performing human-machine tasks was also associated with age (p = .001), professional title (p = .002), years of working in ICU (p < .001), willingness to study emerging technology actively (p = .006), task difficulty (p < .001), and system usability (p < .001). Conclusion: The MWL of ICU nurses is at a moderate level in the context of a rapid increase in nursing human-machine tasks. However, there are significant differences in MWL when performing different types of human-machine tasks, and MWL can be influenced by a combination of factors. Nursing managers need to develop intervention strategies in multiple ways. Implications for practice: Multidimensional approaches are required to perform human-machine tasks better, including enhancing nurses' willingness to learn emerging technologies actively, developing training strategies that vary with tasks, and identifying obstacles in the process of human-machine system interaction.

Keywords: mental workload, nurse, ICU, human-machine, tasks, cross-sectional study, linear mixed model, China

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Authors: Nima Mahmoudi, Hamzeh Khazaei

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Serving machine learning inference workloads on the cloud is still a challenging task at the production level. The optimal configuration of the inference workload to meet SLA requirements while optimizing the infrastructure costs is highly complicated due to the complex interaction between batch configuration, resource configurations, and variable arrival process. Serverless computing has emerged in recent years to automate most infrastructure management tasks. Workload batching has revealed the potential to improve the response time and cost-effectiveness of machine learning serving workloads. However, it has not yet been supported out of the box by serverless computing platforms. Our experiments have shown that for various machine learning workloads, batching can hugely improve the system’s efficiency by reducing the processing overhead per request. In this work, we present MLProxy, an adaptive reverse proxy to support efficient machine learning serving workloads on serverless computing systems. MLProxy supports adaptive batching to ensure SLA compliance while optimizing serverless costs. We performed rigorous experiments on Knative to demonstrate the effectiveness of MLProxy. We showed that MLProxy could reduce the cost of serverless deployment by up to 92% while reducing SLA violations by up to 99% that can be generalized across state-of-the-art model serving frameworks.

Keywords: serverless computing, machine learning, inference serving, Knative, google cloud run, optimization

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Authors: S. Apprich, F. Wulle, A. Lechler, A. Pott, A. Verl

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Large-scale machine tools for the manufacturing of large work pieces, e.g. blades, casings or gears for wind turbines, feature pose-dependent dynamic behavior. Small structural damping coefficients lead to long decay times for structural vibrations that have negative impacts on the production process. Typically, these vibrations are handled by increasing the stiffness of the structure by adding mass. That is counterproductive to the needs of sustainable manufacturing as it leads to higher resource consumption both in material and in energy. Recent research activities have led to higher resource efficiency by radical mass reduction that rely on control-integrated active vibration avoidance and damping methods. These control methods depend on information describing the dynamic behavior of the controlled machine tools in order to tune the avoidance or reduction method parameters according to the current state of the machine. The paper presents the appearance, consequences and challenges of the pose-dependent dynamic behavior of lightweight large-scale machine tool structures in production. The paper starts with the theoretical introduction of the challenges of lightweight machine tool structures resulting from reduced stiffness. The statement of the pose-dependent dynamic behavior is corroborated by the results of the experimental modal analysis of a lightweight test structure. Afterwards, the consequences of the pose-dependent dynamic behavior of lightweight machine tool structures for the use of active control and vibration reduction methods are explained. Based on the state of the art on pose-dependent dynamic machine tool models and the modal investigation of an FE-model of the lightweight test structure, the criteria for a pose-dependent model for use in vibration reduction are derived. The description of the approach for a general pose-dependent model of the dynamic behavior of large lightweight machine tools that provides the necessary input to the aforementioned vibration avoidance and reduction methods to properly tackle machine vibrations is the outlook of the paper.

Keywords: dynamic behavior, lightweight, machine tool, pose-dependency

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Authors: Hamidreza Akbari

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In this paper, for detection of inclined eccentricity in an induction motor, time–frequency analysis of the stator startup current is carried out. For this purpose, the discrete wavelet transform is used. Data are obtained from simulations, using winding function approach. The results show the validity of the approach for detecting the fault and discriminating with respect to other faults.

Keywords: induction machine, fault, DWT, electric

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Authors: Yong Bum Shin

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This case focuses on Weighted additive difference, Conjunctive, Disjunctive, and Elimination by aspects methodologies in consumer decision-making models and the Simple additive weighting (SAW) approach in the multi-criteria decision-making (MCDM) area. Most decision-making models illustrate that the rank reversal phenomenon is unpreventable. This paper presents that rank reversal occurs in popular managerial methods such as Weighted Additive Difference (WAD), Conjunctive Method, Disjunctive Method, Elimination by Aspects (EBA) and MCDM methods as well as such as the Simple Additive Weighting (SAW) and finally Unified Commensurate Multiple (UCM) models which successfully addresses these rank reversal problems in most popular MCDM methods in decision-making area.

Keywords: multiple criteria decision making, rank inconsistency, unified commensurate multiple, analytic hierarchy process

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Authors: Sannikumar Patel, Brian Nolan, Markus Hofmann, Philip Owende, Kunjan Patel

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Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study.

Keywords: cross-language analysis, machine learning, machine translation, sentiment analysis

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Authors: Hend Ben Tkhayat , Khaled Al Zahabi, Husam Younes

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Introduction: Vildagliptin (VG), a dipeptidyl peptidase-4 inhibitor (DPP-4), was proven to be an active agent for the treatment of type 2 diabetes. VG works by enhancing and prolonging the activity of incretins which improves insulin secretion and decreases glucagon release, therefore lowering blood glucose level. It is usually used with various classes, such as insulin sensitizers or metformin. VG is currently only marketed as an immediate-release tablet that is administered twice daily. In this project, we aim to formulate an extended-release with a zero-order profile tableted lipid microparticles of VG that could be administered once daily ensuring the patient’s convenience. Method: The spray-congealing technique was used to prepare VG microparticles. Compritol® was heated at 10 oC above its melting point and VG was dispersed in the molten carrier using a homogenizer (IKA T25- USA) set at 13000 rpm. VG dispersed in the molten Compritol® was added dropwise to the molten Gelucire® 50/13 and PEG® (400, 6000, and 35000) in different ratios under manual stirring. The molten mixture was homogenized and Carbomer® amount was added. The melt was pumped through the two-fluid nozzle of the Buchi® Spray-Congealer (Buchi B-290, Switzerland) using a Pump drive (Master flex, USA) connected to a silicone tubing wrapped with silicone heating tape heated at the same temperature of the pumped mix. The physicochemical properties of the produced VG-loaded microparticles were characterized using Mastersizer, Scanning Electron Microscope (SEM), Differential Scanning Calorimeter (DSC) and X‐Ray Diffractometer (XRD). VG microparticles were then pressed into tablets using a single punch tablet machine (YDP-12, Minhua pharmaceutical Co. China) and in vitro dissolution study was investigated using Agilent Dissolution Tester (Agilent, USA). The dissolution test was carried out at 37±0.5 °C for 24 hours in three different dissolution media and time phases. The quantitative analysis of VG in samples was realized using a validated High-Pressure Liquid Chromatography (HPLC-UV) method. Results: The microparticles were spherical in shape with narrow distribution and smooth surface. DSC and XRD analyses confirmed the crystallinity of VG that was lost after being incorporated into the amorphous polymers. The total yields of the different formulas were between 70% and 80%. The VG content in the microparticles was found to be between 99% and 106%. The in vitro dissolution study showed that VG was released from the tableted particles in a controlled fashion. The adjustment of the hydrophilic/hydrophobic ratio of excipients, their concentration and the molecular weight of the used carriers resulted in tablets with zero-order kinetics. The Gelucire 50/13®, a hydrophilic polymer was characterized by a time-dependent profile with an important burst effect that was decreased by adding Compritol® as a lipophilic carrier to retard the release of VG which is highly soluble in water. PEG® (400,6000 and 35 000) were used for their gelling effect that led to a constant rate delivery and achieving a zero-order profile. Conclusion: Tableted spray-congealed lipid microparticles for extended-release of VG were successfully prepared and a zero-order profile was achieved.

Keywords: vildagliptin, spray congealing, microparticles, controlled release

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Authors: Jeelka Ray

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Survey of the papers revealed that there is no practical design available for packaging machine based on Embedded system, so the need arose for the development of the prototype model. In this paper, author has worked on the development of an ARM7 based Embedded Controller for controlling the sequence of packaging machine. The unit is made user friendly with TFT and Touch Screen implementing human machine interface (HMI). The different system components are briefly discussed, followed by a description of the overall design. The major functions which involve bag forming, sealing temperature control, fault detection, alarm, animated view on the home screen when the machine is working as per different parameters set makes the machine performance more successful. LPC2478 ARM 7 Embedded Microcontroller controls the coordination of individual control function modules. In back gone days, these machines were manufactured with mechanical fittings. Later on, the electronic system replaced them. With the help of ongoing technologies, these mechanical systems were controlled electronically using Microprocessors. These became the backbone of the system which became a cause for the updating technologies in which the control was handed over to the Microcontrollers with Servo drives for accurate positioning of the material. This helped to maintain the quality of the products. Including all, RS 485 MODBUS Communication technology is used for synchronizing AC Drive & Servo Drive. These all concepts are operated either manually or through a Graphical User Interface. Automatic tuning of heaters, sealers and their temperature is controlled using Proportional, Integral and Derivation loops. In the upcoming latest technological world, the practical implementation of the above mentioned concepts is really important to be in the user friendly environment. Real time model is implemented and tested on the actual machine and received fruitful results.

Keywords: packaging machine, embedded system, ARM 7, micro controller, HMI, TFT, touch screen, PID

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Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

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Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

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Authors: Adriana Avila Zuniga Nordfjeld

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This paper provides a literature review to document the state of the art with respect to handling 'human bias' in decision making within the established quality management systems (QMS) and LEAN theory, in the context of shipbuilding. Previous research shows that in shipbuilding there is a huge deviation from the planned man-hours under the project management to the actual man-hours used because of errors in planning and reworks caused by human bias in the information flows among others. This reduces the efficiency and increases operational costs. Thus, the research question is how QMS and LEAN handle biases. The findings show the gap in studying the integration of methods to handle human bias in decision making into QMS and lean, not only within shipbuilding but also in general. Theoretical and practical implications are discussed for researchers and practitioners in the areas of decision making QMS, LEAN, and future research is suggested.

Keywords: human bias, decision making, LEAN shipbuilding, quality management systems

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Authors: Jacob Green, Cecilia Cabrera, Maximilian Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan Greenhalgh

Abstract:

Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.

Keywords: activity (QSAR), ADMET, classical methods, drug property prediction, empirical study, machine learning

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Authors: Tolulope A. Olukunle

Abstract:

Plastic waste management has emerged as one of the greatest challenges facing developing countries. This paper describes the design of various components of a plastic shredder. This machine is widely used in industries and recycling plants. The introduction of plastic shredder machine will promote reduction of post-consumer plastic waste accumulation and serves as a system for wealth creation and empowerment through conversion of waste into economically viable products. In this design research, a 10 kW electric motor with a rotational speed of 500 rpm was chosen to drive the shredder. A pulley size of 400 mm is mounted on the electric motor at a distance of 1000 mm away from the shredder pulley. The shredder rotational speed is 300 rpm.

Keywords: design, machine, plastic waste, recycling

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Authors: Saleh Elawgali

Abstract:

Current spectrums of a four pole-pair, 400 kW induction machine were calculated for the cases of full symmetry and static eccentricity. The calculations involve integration of 93 electrical plus four mechanical ordinary differential equations. Electrical equations account for variable inductances affected by slotting and eccentricities. The calculations were followed by Fourier analysis of the stator currents in steady state operation. Zooms of the current spectrums, around the 50 Hz fundamental harmonic as well as of the main slot harmonic zone, were included. The spectrums included refer to both calculated and measured currents.

Keywords: diagnostic, harmonic, induction machine, spectrum

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Authors: G. Götz, P. Stich, J. Backhaus, G. Reinhart

Abstract:

The rising demand for format-flexible packaging machines is caused by current market changes. Increasing the formatflexibility is a new goal for the packaging machine manufacturers’ product development process. There are no methodical or designorientated tools for a comprehensive consideration of this target. This paper defines the term format-flexibility in the context of packaging machines and shows the state-of-the-art for improving the changeover of production machines. The requirements for a new approach and the concept itself will be introduced, and the method elements will be explained. Finally, the use of the concept and the result of the development of a format-flexible packaging machine will be shown.

Keywords: packaging machine, format-flexibility, changeover, design method

Procedia PDF Downloads 413

Authors: Parsa Mahdavi, M. Amin Hariri-Ardebili

Abstract:

The development of accurate machine learning and deep learning models traditionally demands hands-on expertise and a solid background to fine-tune hyperparameters. With the continuous expansion of datasets in various scientific and engineering domains, researchers increasingly turn to machine learning methods to unveil hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of the resultant meta-models and, consequently, the interpretation of the findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. AutoML encompasses crucial stages such as data preparation, feature engineering, hyperparameter optimization, and neural architecture search. This paper provides a comprehensive overview of the principles underpinning AutoML, surveying several widely-used AutoML platforms. Additionally, the paper offers a glimpse into the application of AutoML on various engineering datasets. By comparing these results with those obtained through classical machine learning methods, the paper quantifies the uncertainties inherent in the application of a single ML model versus the holistic approach provided by AutoML. These examples showcase the efficacy of AutoML in extracting meaningful patterns and insights, emphasizing its potential to revolutionize the way we approach and analyze complex datasets.

Keywords: automated machine learning, uncertainty, engineering dataset, regression

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Authors: Krishang Surapaneni

Abstract:

The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

Procedia PDF Downloads 58