Artykuły w czasopismach na temat „Addressing Machines”
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CURRAN, KEVIN, NEIL McCAUGHLEY i XUELONG LI. "ADDRESSING THE PROBLEMS OF DETECTING FACES WITH NEURAL NETWORKS". International Journal of Image and Graphics 07, nr 04 (październik 2007): 617–40. http://dx.doi.org/10.1142/s0219467807002830.
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The face is the most distinctive and widely used key to a person's identity. The area of face detection has attracted considerable attention in the advancement of human-machine interaction as it provides a natural and efficient way to communicate between humans and machines. The problem of facial parts in image sequences has become a popular area of research due to emerging applications in intelligent human-computer interface, surveillance systems, content-based image retrieval, video conferencing, financial transaction, forensic applications, pedestrian detection, image database management system and so on. This paper presents the results of an image based neural network face detection system which seeks to address the problem of detecting faces under gross variations.
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Gulcehre, Caglar, Sarath Chandar, Kyunghyun Cho i Yoshua Bengio. "Dynamic Neural Turing Machine with Continuous and Discrete Addressing Schemes". Neural Computation 30, nr 4 (kwiecień 2018): 857–84. http://dx.doi.org/10.1162/neco_a_01060.
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We extend the neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing trainable address vectors. This addressing scheme maintains for each memory cell two separate vectors, content and address vectors. This allows the D-NTM to learn a wide variety of location-based addressing strategies, including both linear and nonlinear ones. We implement the D-NTM with both continuous and discrete read and write mechanisms. We investigate the mechanisms and effects of learning to read and write into a memory through experiments on Facebook bAbI tasks using both a feedforward and GRU controller. We provide extensive analysis of our model and compare different variations of neural Turing machines on this task. We show that our model outperforms long short-term memory and NTM variants. We provide further experimental results on the sequential [Formula: see text]MNIST, Stanford Natural Language Inference, associative recall, and copy tasks.
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Rakes, David, Muhammad Arif, Agus Setiawan, Kerina Putri Nasution i Yudi Prastyo. "Preventive Maintenance on CNC Machines Using the OEE Method to Reduce Downtime at PT. MTAT". Jurnal Impresi Indonesia 3, nr 7 (5.07.2024): 481–90. http://dx.doi.org/10.58344/jii.v3i7.5116.
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This study examines the performance of CNC machines at PT MTAT Indonesia from January to March 2023. Monthly production data, machine uptime, defect rates, and non-productive periods were collected to assess Overall Equipment Effectiveness (OEE). This study aims to analyze the effectiveness of preventive maintenance of CNC machines at PT MTAT Indonesia using the Overall Equipment Effectiveness (OEE) method to reduce downtime. This study uses monthly data from January to March 2023, including production uptime, defect rates, and non-productive periods, to calculate OEE. The analysis showed that the CNC machines achieved an average OEE of 86.52%, surpassing the global standard of 85%, indicating high efficiency and quality. The study used Pareto analysis to identify the main causes of downtime, finding technical and maintenance issues as the main contributors. By addressing these factors, PT MTAT Indonesia can further improve machine efficiency and productivity. This study contributes to this field by providing a comprehensive analysis of CNC machine maintenance and proposing strategies for continuous improvement.
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Hosseini, Ahmad, Ola Lindroos i Eddie Wadbro. "A holistic optimization framework for forest machine trail network design accounting for multiple objectives and machines". Canadian Journal of Forest Research 49, nr 2 (luty 2019): 111–20. http://dx.doi.org/10.1139/cjfr-2018-0258.
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Ground-based mechanized forestry requires the traversal of terrain by heavy machines. The routes that they take are often called “machine trails” and are created by removing trees from the trail and placing the logs outside it. Designing an optimal machine trail network is a complex locational problem that requires understanding how forestry machines can operate on the terrain, as well as the trade-offs between various economic and ecological aspects. Machine trail designs are currently created manually based on intuitive decisions about the importance, correlations, and effects of many potentially conflicting aspects. Badly designed machine trail networks could result in costly operations and adverse environmental impacts. Therefore, this study was conducted to develop a holistic optimization framework for machine trail network design. Key economic and ecological objectives involved in designing machine trail networks for mechanized cut-to-length operations are presented, along with strategies for simultaneously addressing multiple objectives while accounting for the physical capabilities of forestry machines, the impact of slope, and the operating costs. Ways of quantitatively formulating and combining these different aspects are demonstrated, together with examples showing how the optimal network design changes in response to various inputs.
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Portase, Raluca Laura, Ramona Tolas i Rodica Potolea. "SmartLaundry: A Real-Time System for Public Laundry Allocation in Smart Cities". Sensors 24, nr 7 (28.03.2024): 2159. http://dx.doi.org/10.3390/s24072159.
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Smart cities facilitate the comprehensive management and operation of urban data generated within a city, establishing the foundation for smart services and addressing diverse urban challenges. A smart system for public laundry management uses artificial intelligence-based solutions to solve the challenges of the inefficient utilization of public laundries, waiting times, overbooking or underutilization of machines, balancing of loads across machines, and implementation of energy-saving features. We propose SmartLaundry, a real-time system design for public laundry smart recommendations to better manage the loads across connected machines. Our system integrates the current status of the connected devices and data-driven forecasted usage to offer the end user connected via a mobile application a list of recommended machines that could be used. We forecast the daily usage of devices using traditional machine learning techniques and deep learning approaches, and we perform a comparative analysis of the results. As a proof of concept, we create a simulation of the interaction with our system.
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Tirkashev Dilshodjon Sharobiddin o’gli. "AI and Pragmalinguistics: Bridging the Gap Between Machines and Human Communication". Journal of Advanced Zoology 44, S6 (10.12.2023): 1760–66. http://dx.doi.org/10.17762/jaz.v44is6.2614.
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Pragmatic linguistics, a field that examines the connection between language and context, is at the forefront of efforts to enhance machine understanding of human communication. In the context of artificial intelligence (AI), which has transformed various sectors, pragmatic linguistics plays a vital role in addressing the challenges of human-like interaction. This article delves into the intersection of AI and pragmalinguistics, shedding light on the possibilities and obstacles faced in bridging the divide between machines and human communication.
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Flowers, Michael, i Kai Cheng. "Global Manufacturing: Reconfiguration of Machines in Addressing Changing Customer Requirement Scenarios". Applied Mechanics and Materials 16-19 (październik 2009): 15–19. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.15.
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The dynamics of customer requirements in the global domain are dictating a need for the reconfiguration of existing conventional manufacturing systems in order to adapt and respond to the changing functional requirements. This study presents the investigation of the feasibility of adapting a hydraulic cam system to a power press for uphill sheetmetal piercing. The surface topography of sheared edges of blanks and slugs was analyzed using Zygo New View 200 Scanning White Light Interferometer. The piercing force in each trial was monitored and analyzed via computer-press interface data acquisition system (DAS). The average piercing force, 10721N, obtained was found to be nearly constant throughout the experimental trials.
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Manzotti, Riccardo, i Antonio Chella. "Conscious Machines: A Possibility? If So, How?" Journal of Artificial Intelligence and Consciousness 07, nr 02 (24.07.2020): 183–98. http://dx.doi.org/10.1142/s2705078520710022.
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The scope of the paper is to encourage scientists and engineering to avoid to do what Einstein pointed out as being the hallmark of folly. Machine consciousness scholars must be brave enough to step out of the beaten path. There must be some big recurrent conceptual mistakes that prevent science and technology from addressing machine consciousness.
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Stelzner, Marc, Falko Dressler i Stefan Fischer. "Function Centric Nano-Networking: Addressing nano machines in a medical application scenario". Nano Communication Networks 14 (grudzień 2017): 29–39. http://dx.doi.org/10.1016/j.nancom.2017.09.001.
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Sin, Dong Eui. "A Study on Moral Education in the Age of Artificial Intelligence: Focusing on the Distinction and Application of Ethics of Artificial Moral Agent (AMA)". Korean Journal of Teacher Education 39, nr 3 (31.05.2023): 29–48. http://dx.doi.org/10.14333/kjte.2023.39.3.02.
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Purpose: The purpose of this study is to investigate the ultimate difference and limitations between humans and machines while addressing the most heated controversy in the era of artificial intelligence, particularly the question of “Can machines think?”, which is considered a critical issue in moral education in the age of artificial intelligence. Methods: To this end, the direction of moral education in the AI era is sought through analysis and reflective discourse on literature materials related to digital technologies such as the Fourth Industrial Revolution, artificial intelligence (AI), big data and machine learning. Results: From the perspective of moral education in the era of artificial intelligence, this study discusses the classification of Artificial Moral Agents (AMA) and the application of ethics, focusing on reflective discourse on science and technological civilization. In other words, it examines three principles of machine construction based on Stuart Russell’s belief in “beneficial machines” from the perspective of “preference”, presents Moors four stages of AMAs, and addresses the concept of explicit ethical agents in the third stage. Additionally, it explores Heidegger's critique of technological civilization and discusses creativity in relation to the controversies surrounding artificial intelligence. Conclusion: Based on this, the present study explored the potential application of moral education in artificial intelligence through the question “Can machines think?”, which can be a core topic in the field of artificial intelligence.
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Polous, Nina. "Machines as mapmakers and map users: key questions to ponder upon?" Proceedings of the ICA 5 (7.08.2023): 1–7. http://dx.doi.org/10.5194/ica-proc-5-18-2023.
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Abstract. This article explores the convergence of cartography and robotic mapping, addressing key challenges and opportunities that arise as machines increasingly serve as both mapmakers and map users. The author investigates three critical questions: (1) how to best represent geographical data and maps for machines, (2) what dynamic information about our environment should be made readily available to machines, and (3) which ethical, religious, and cultural norms should be considered for autonomous entities. By addressing these questions, the author aims to facilitate the development of geospatial data representation, management, and analysis for autonomous systems, while ensuring harmonious coexistence with humans. In the scope of this paper, author tries to propose an approach to bridge the gap between traditional cartography and the emerging needs of machines as user and makers by building common ground through cross-disciplinary collaboration, joint research groups, and the development of common standards and frameworks. In this proposal, particularly by using design thinking approach, an event-mapping principle, as an approach that represents spatial information as events, is highlighted as a promising common framework for integrating static and dynamic spatial information. Since, event-based mapping and models can improve the representation of geographical data for machines, enabling them to better understand the environment and make informed decisions in complex and dynamic contexts. This convergence will ultimately transform the way we think about maps and geographical information systems in the age of machines.
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Gobin, Maya, Jeremy Horwood, Sarah Stockwell, Sarah Denford, Joanna Copping, Lottie Lawson, Samuel Hayward, Lindsey Harryman i Joanna M. Kesten. "Qualitative evaluation of digital vending machines to improve access to STI and HIV testing in South West England: using a Person-Based Approach". BMJ Open 14, nr 6 (czerwiec 2024): e084786. http://dx.doi.org/10.1136/bmjopen-2024-084786.
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ObjectivesTo report the development, implementation, acceptability and feasibility of vending machines offering HIV and sexually transmitted infection (STI) testing kits.DesignA qualitative study using the Person-Based Approach with patient and public involvement workshops and stakeholder involvement and interviews with machine users, sexual health service (SHS) staff, venue staff and local authority sexual health commissioners. Transcripts were analysed thematically.SettingBristol, North Somerset and South Gloucestershire (BNSSG).Participants15 machine users, 5 SHS staff, 3 venue staff and 3 local authority commissioners.InterventionFour vending machines dispensing free HIV self-testing and STI self-sampling kits in publicly accessible venues across BNSSG were introduced to increase access to testing for groups at higher risk of HIV and STI infection who are less likely to access SHS clinic testing services (young people, people from black communities, and gay, bisexual and other men who have sex with men).ResultsMachine users reported the service was convenient, easy to use and accessible; however, concerns regarding privacy related to machine placement within the venues and issues of maintenance were raised. Promotional material was inclusive and informative; however, awareness of the service through the promotional campaign was limited. Vending machines were acceptable to venue staff once clear processes for their management were agreed with the SHS. SHS staff identified challenges with the implementation of the service related to the limited involvement of the whole SHS team in the planning and development.ConclusionsThe codeveloped vending machine service was acceptable, addressing some barriers to testing. Resources and protected staff time are needed to support greater involvement of the whole SHS team and service providers in venues. Adopting a similarly robust coproduction approach to the implementation of the machines could avoid the challenges reported. The placement of the machines to assure users privacy and repeated, targeted promotion could encourage service use among target groups.
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Pintér, Róbert. "A mesterséges intelligencia nyomában – Konferenciabeszámolók". Információs Társadalom 19, nr 1 (26.11.2019): 138. http://dx.doi.org/10.22503/inftars.xix.2019.1.8.
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A mesterséges intelligencia nyomában – Konferenciabeszámolók
Beszámoló a V4 Conference on Artificial Intelligence (2018. október 11., Brüsszel, Belgium), a Making AI at Google (2018. november 6–7., Amszterdam, Hollandia)és a The Aspen Institute Central Europe, PUBLIC DEBATE: Beyond Human. Trust in Machines and AI és WORKSHOP: Building the Future: Addressing the Opportunities and Challenges of an AI-Enabled World (2019. január 22–23., Prága, Csehország) rendezvényekről.
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In the footsteps of artificial intelligence – Conference reports
Conference reports on V4 Conference on Artificial Intelligence (11 October 2018., Brussels, Belgium), the Making AI at Google (6-7 November 2018., Amsterdam, The Netherlands) and The Aspen Institute Central Europe, PUBLIC DEBATE: Beyond Human. Trust in Machines and AI és WORKSHOP: Building the Future: Addressing the Opportunities and Challenges of an AI-Enabled World (22-23 January 2019., Práague, Czechia).
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Enas, Enas, Ahmed M. Dinar, Mazin Abed .. i Bourair AL AL-Attar. "Improving Loan Status Prediction Accuracy with Generative Adversarial Networks: Addressing Data Scarcity and Bias". Journal of Intelligent Systems and Internet of Things 13, nr 1 (2024): 225–33. http://dx.doi.org/10.54216/jisiot.130116.
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A precise and reliable loan status prediction is of the essence for financial institutions, However, the lack of real-world data and biases within that data can greatly impact the accuracy of machine learning models. Another challenge faced by loan status prediction models is class imbalance, where one category (such as approved loans) is much more common than another (such as defaulted loans), leading to skewed predictions towards the majority class. This study inspects Generative Adversarial Networks (GANs) to augment the data and improve the machine learning models’ performance. Several machine learning (ML) models including but not limited to Support Vector Machines (SVM) and ensemble bagged trees were employed on a Kaggle loan dataset (380 samples). Baseline training and testing accuracies were 86.9% and 86.3% (SVM) and 84.5% and 82.1% (ensemble). ActGAN (Activating Generative Networks) was then utilized to generate synthetic data points for both accepted and rejected loans. Retraining the models with new augmented data showed remarkable improvements: SVM accuracies for training and testing rose to 94.4% and 93.4%, while ensemble models achieved 97.4% and 95.8%, respectively. Other ML models were also explored such as KNN, Decision tree and logistic Regression and showed promising results in terms of accuracy as compared to the state of art. These findings put forward that GAN-based data augmentation can enhance the performance of loan status prediction. Future research could explore GAN’s impact of different architectures and assess the general applicability of this approach.
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Enas, Enas, Ahmed M. Dinar, Mazin Abed .. i Bourair AL AL-Attar. "Improving Loan Status Prediction Accuracy with Generative Adversarial Networks: Addressing Data Scarcity and Bias". Journal of Intelligent Systems and Internet of Things 13, nr 1 (2024): 251–58. http://dx.doi.org/10.54216/jisiot.130118.
Pełny tekst źródłaStreszczenie:
A precise and reliable loan status prediction is of the essence for financial institutions, However, the lack of real-world data and biases within that data can greatly impact the accuracy of machine learning models. Another challenge faced by loan status prediction models is class imbalance, where one category (such as approved loans) is much more common than another (such as defaulted loans), leading to skewed predictions towards the majority class. This study inspects Generative Adversarial Networks (GANs) to augment the data and improve the machine learning models’ performance. Several machine learning (ML) models including but not limited to Support Vector Machines (SVM) and ensemble bagged trees were employed on a Kaggle loan dataset (380 samples). Baseline training and testing accuracies were 86.9% and 86.3% (SVM) and 84.5% and 82.1% (ensemble). ActGAN (Activating Generative Networks) was then utilized to generate synthetic data points for both accepted and rejected loans. Retraining the models with new augmented data showed remarkable improvements: SVM accuracies for training and testing rose to 94.4% and 93.4%, while ensemble models achieved 97.4% and 95.8%, respectively. Other ML models were also explored such as KNN, Decision tree and logistic Regression and showed promising results in terms of accuracy as compared to the state of art. These findings put forward that GAN-based data augmentation can enhance the performance of loan status prediction. Future research could explore GAN’s impact of different architectures and assess the general applicability of this approach.
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Pashkevich, Natallia, Darek Haftor, Mikael Karlsson i Soumitra Chowdhury. "Sustainability through the Digitalization of Industrial Machines: Complementary Factors of Fuel Consumption and Productivity for Forklifts with Sensors". Sustainability 11, nr 23 (27.11.2019): 6708. http://dx.doi.org/10.3390/su11236708.
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Increasing the fuel efficiency of industrial machines through digitalization can enable the transport and logistics sector to overcome challenges such as low productivity growth and increasing CO2 emissions. Modern digitalized machines with embedded sensors that collect and transmit operational data have opened up new avenues for the identification of more efficient machine use. While existing studies of industrial machines have mostly focused on one or a few conditioning factors at a time, this study took a complementary approach, using a large set of known factors that simultaneously conditioned both the fuel consumption and productivity of medium-range forklifts (n = 285) that operated in a natural industrial setting for one full year. The results confirm the importance of a set of factors, including aspects related to the vehicles’ travels, drivers, operations, workload spectra, and contextual factors, such as industry and country. As a novel contribution, this study shows that the key conditioning factors interact with each other in a non-linear and non-additive manner. This means that addressing one factor at a time might not provide optimal fuel consumption, and instead all factors need to be addressed simultaneously as a system.
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Rhee, Eugene, i Jihoon Lee. "Improved DAG in blockchain tangle for IOTA". Indonesian Journal of Electrical Engineering and Computer Science 34, nr 2 (1.05.2024): 806. http://dx.doi.org/10.11591/ijeecs.v34.i2.pp806-813.
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The internet of things (IoT) enables machine-to-machine communication without human intervention. Consequently, every object connected to the internet can exchange information with each other. Internet of things application (IOTA) has undertaken a project to address the high transaction fees inherent in traditional blockchain systems and enhance the efficiency of microtransactions between machines by combining blockchain and IoT. IOTA employs its unique Tangle technology, which introduces a novel transaction consensus method, addressing the fee issues, limited scalability, and the inability to conduct offline transactions associated with traditional blockchains. This paper provides a detailed overview of the characteristics of the Tangle structure and the concepts applied in IOTA. Additionally, it explores potential approaches for integrating blockchain into IoT.
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Piersanti, Eleonora, i Naeeme Danesh Moghaddam. "Automated Load and Dump Detection for CO2 Reduction". Nordic Machine Intelligence 3, nr 3 (10.06.2024): 20–24. http://dx.doi.org/10.5617/nmi.10535.
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In this paper, we detail our methodology for addressing the challenge of reducing CO2 emissions in road construction from construction machines in a Norwegian construction site. In particular, we focus on the automatic detection of load and dump locations from various data sources
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Saw Shu Zhen, Angelica Audrey, Purnomo Purnomo i Yurida Ekawati. "Analisis Efektivitas Mesin Cetak Flexo menggunakan Metode Overall Equipment Effectiveness Berbasis Six Big Losses". Jurnal Sains dan Aplikasi Keilmuan Teknik Industri (SAKTI) 4, nr 1 (28.06.2024): 31–38. http://dx.doi.org/10.33479/sakti.v4i1.71.
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PT X is a manufacturing company operating in the integrated cardboard industry, utilizing various machines, including flexography machines. This study aims to analyze the primary causes of production deficiencies in the flexography process and provide improvement recommendations using the Six Big Losses-based Overall Equipment Effectiveness (OEE) method. The OEE analysis revealed that the average availability ratio is 88.8%, falling short of the world-class standard of 90%. A deeper analysis using the Six Big Losses methodology identified that the primary cause for not achieving the desired availability ratio is the high percentage of setup and adjustment losses, which stands at 7%. To address these issues, several recommendations are proposed. Firstly, conducting comprehensive training for operators on the proper and correct operation of flexography machines is essential. This training will enhance their technical skills and reduce the time lost during setup and adjustments. Secondly, implementing daily briefings for operators before shifts begin, coupled with regular supervision, will ensure consistent adherence to operational standards. Moreover, establishing a routine and time-limited cleaning schedule for machines and work locations will minimize unexpected downtimes due to maintenance issues. Finally, developing and enforcing standardized machine operation protocols will promote discipline among operators, ensuring that all procedures are followed meticulously. These recommendations aim to improve the overall efficiency of flexography machines, thereby increasing the availability ratio to meet or exceed the world-class standard. By addressing the root causes of production inefficiencies, PT X can enhance its operational performance and maintain its competitive edge in the cardboard industry.
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Kiranti, Irma, Lukman Hudi i Rifky Pradiko. "Chicken Sausage Production Process At PT. Charoen Pokphand Indonesia Food Division, Ngoro Unit". Procedia of Engineering and Life Science 7 (20.02.2024): 1–7. http://dx.doi.org/10.21070/pels.v7i0.1494.
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This study investigated the chicken sausage production process and explored problem-solving strategies using fishbone diagrams. The analysis identified key stages, including raw material preparation, grinding, mixing, molding, cooking, cooling, cutting, and packaging. Fishbone diagrams were employed to systematically dissect process issues, revealing root causes related to personnel, methods, machines, materials, and environment. The findings highlight the effectiveness of fishbone diagrams for identifying and addressing production bottlenecks, paving the way for optimized efficiency and quality control in sausage manufacturing.
Highlight :
Process breakdown: The study identified key stages in chicken sausage production (preparation, grinding, mixing, etc.) using fishbone diagrams.
Root cause analysis: Fishbone diagrams helped pinpoint root causes of process issues across various factors (personnel, methods, machines, etc.).
Quality improvement: The findings suggest fishbone diagrams as an effective tool for identifying and addressing bottlenecks, leading to optimized efficiency and quality control.
Keywords: chicken sausage, production process, fishbone diagrams, problem-solving, quality control
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Remacle, F., J. R. Heath i R. D. Levine. "Electrical addressing of confined quantum systems for quasiclassical computation and finite state logic machines". Proceedings of the National Academy of Sciences 102, nr 16 (8.04.2005): 5653–58. http://dx.doi.org/10.1073/pnas.0501623102.
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Hagnestal, Anders. "Addressing the Magnet Mounting Problem for Double-Sided TFM Machines With Flux-Concentrating Setup". IEEE Power and Energy Technology Systems Journal 6, nr 2 (czerwiec 2019): 122–30. http://dx.doi.org/10.1109/jpets.2019.2913167.
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Rifai, Achmad Pratama, Arief Rahman Alfithra, Chanif Faruq Al'adiat i Wangi Pandan Sari. "Facility Layout Planning for Pyrolyzer Production Using Automated Layouts Design Program (ALDEP) Method". OPSI 16, nr 1 (19.06.2023): 165. http://dx.doi.org/10.31315/opsi.v16i1.8745.
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One important factor to consider in increasing productivity in a company is the design of the facility layout. PT Hari Mukti Teknik is keen producing pyrolysis machine to contribute to addressing waste issue in Indonesia. Currently, the facility layout at the company is not suitable to produce pyrolizers as it was set up to produce industrial scale washing machines. To improve production efficiency, PT Hari Mukti Teknik needs facility layout that can be optimized for pyrolizers. The purpose of this study is to provide a layout design proposal for the pyrolysis machine manufacturing process to obtain an effective and efficient process. Here, we used ALDEP method to produce layouts based on consideration of the level of relationship between departments. There were 4 alternatives for the manufacturing and production and two alternative layouts for employee and office area. The design for new layout was selected based on the closeness relationship between the departments. An overall facility layout plan that is required for the production of pyrolysis machines is a building area the total of 1045 m² covering manufacturing, production, employee, office and parking space areas.
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Roy, Subhrajit. "A Research Paper on Biometric based ATM System". International Journal for Research in Applied Science and Engineering Technology 10, nr 4 (30.04.2022): 1700–1703. http://dx.doi.org/10.22214/ijraset.2022.41583.
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Abstract: Biometrics-based authentication is a potential alternative to password-based authentication. Of all biometric methods, face-based identification is one of the most convenient one. In ATM systems, facial images are captured using a high resolution camera. Bank security measures can play an important role in preventing customer attacks. These measures are of paramount importance in addressing the weaknesses of civil lawsuits. Banks must meet the criteria to provide their customers with a secure banking environment. This paper focuses on the increased vulnerability and criminal activity of automated teller machines (ATMs), not the bank themselves. Both customers and bankers. Keywords: Automated Teller Machine (ATM), Camera, Verification, Crime, E-Banking.
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Seshubabu, K., K. Aravind kumar, CL Jose i Dr R. Venkatraman. "A CASE STUDY ON ANALYSIS OF CAPACITOR FAILURE IN PERMANENT SPLIT CAPACITOR RUN SINGLE PHASE INDUCTION MOTOR IN LOW SLIP REGION". International Journal of Engineering Applied Sciences and Technology 7, nr 3 (1.07.2022): 197–209. http://dx.doi.org/10.33564/ijeast.2022.v07i03.031.
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This paper presents a case study on capacitor failure analysis in Permanent-Split Capacitor-Run SinglePhase Induction Motor (PSCRSPIM) in low slip region. It emphasizes the motor performance in low slip region and detailed analysis carried out for Capacitor voltage and current variation with respect to slip. Motor Equivalent circuit parameters are extracted from dc test, no-load test and locked-rotor test. MATLAB simulations are carried out. Theoretical and experimental results are plotted. The premature failure of capacitor faced by PSCRSPIM users is answered at the end of the Paper. Contrary to the general concept that overloaded machine will fail; the paper is addressing the failure of lightly loaded machines.
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Arora, Himanshu, Minika Lamba, Lokesh kumar i Sameer Kumar. "EXPLAINABLE AI AND ITS IMPORTANCE IN DECISION MAKING". Journal of Nonlinear Analysis and Optimization 14, nr 01 (2023): 19–27. http://dx.doi.org/10.36893/jnao.2023.v14i1.0019-0027.
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AI is the technology that builds intelligent machines able to perform tasks that generally need human intelligence in machines that are programmed to think and act like humans. AI has penetrated many organization processes, resulting in a growing fear that intelligent machines will soon replace many humans in decision-making. To provide a more proactive and pragmatic perspective, this article highlights the complementarities of humans and AI. It examines how each can strengthen organizational decision-making processes typically characterized by uncertain, complex and equal vocalists. With excellent computation information processing capacity and an analytical approach, AI can extend human cognition when addressing complexity. In contrast, humans can still offer a more holistic, intuitive approach in dealing with uncertainly and equal vocalist in organizational decisionmaking.
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Kharat, Shrikant Devidas. "Musical Notes Classification and Recommendation Using Machine Learning". International Journal for Research in Applied Science and Engineering Technology 12, nr 6 (30.06.2024): 2154–64. http://dx.doi.org/10.22214/ijraset.2024.63390.
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Abstract: The abstract explores the fundamental concept of understanding algorithms through data within the realm of machine learning, introducing its key paradigms: supervised and unsupervised learning. It provides an in-depth analysis of supervised learning, demonstrating how data classification is accomplished through various algorithms such as support vector machines (SVM), linear regression, logistic regression, neural networks, and nearest neighbour. Particularly emphasized is SVM's capacity to establish non-linear decision boundaries using kernel functions, showcasing its relevance in addressing practical challenges like face detection and handwriting recognition. The research outcomes underscore two significant points: the applicability of the PCP for describing chords in a machine- learning context and the algorithm's proficiency in recognizing chords played on diverse instruments, including those unseen during the training phase. This highlights the versatility of machine learning methodologies in chord recognition and their potential for real-world applications.
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Kang, Ye Gu, Kang Min Lee i Gilsu Choi. "Rapid Prototyping of Three-Phase AC Machine Drive System with Subtractive and Additive Manufacturing". Energies 16, nr 5 (27.02.2023): 2266. http://dx.doi.org/10.3390/en16052266.
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We propose a method for rapid prototyping of a three-phase AC drive system for educational purposes. The proposed method allows college students to design and manufacture a drive system comprising three-phase inverters, a permanent-magnet (PM) machine, a controller, and sensors within a semester. The rapid prototyping process, which requires less than a day, enables efficient iteration and testing during the development process. In addition to addressing the electrical design considerations, this study also addresses the mechanical aspects of the drive system, including the use of coreless PM machines fabricated, using additive manufacturing technology, and the inverter manufacturing process, utilizing an auto-milling machine. Finally, we provide details of the rapid prototyping of closed-loop control, based on sensor feedback to regulate the rotating magnetic fields and output torque.
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Riquelme, Danilo, Carlos Madariaga, Werner Jara, Gerd Bramerdorfer, Juan A. Tapia i Javier Riedemann. "Study on Stator-Rotor Misalignment in Modular Permanent Magnet Synchronous Machines with Different Slot/Pole Combinations". Applied Sciences 13, nr 5 (21.02.2023): 2777. http://dx.doi.org/10.3390/app13052777.
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Addressing stator-rotor misalignment, usually called eccentricity, is critical in permanent magnet (PM) machines since significantly high radial forces can be developed on the bearings, which can trigger a major fault and compromise the structural integrity of the machine. In this regard, this paper aims to provide insight into the unaddressed identification and analysis of the impact of eccentric tolerances on relevant performance indices of permanent magnet synchronous machines (PMSMs) with modular stator core. Static and dynamic eccentricity are assessed for different slot/pole combinations through the finite element method (FEM), and the results are compared with those of PMSMs with a conventional stator core. The unbalanced magnetic forces (UMF), cogging torque, back-emf, and mean torque variations are described and related to the eccentricity magnitude and classification. The main findings indicate that severe radial forces and significant additional cogging torque harmonics are generated because of eccentricity. Additionally, it is found that the main differences between modular PMSMs and conventional PMSMs rely on the value of slots per pole per phase.
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Frontistis, Zacharias, Grigoris Lykogiannis i Anastasios Sarmpanis. "Machine Learning Implementation in Membrane Bioreactor Systems: Progress, Challenges, and Future Perspectives: A Review". Environments 10, nr 7 (19.07.2023): 127. http://dx.doi.org/10.3390/environments10070127.
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This study offers a review of machine learning (ML) applications in membrane bioreactor (MBR) systems, an emerging technology in advanced wastewater treatment. The review focuses on implementing ML algorithms to enhance the prediction of membrane fouling, control and optimize the system, and predict faults early, thereby enabling the development of novel cleaning strategies. Key ML algorithms such as artificial neural networks (ANNs), support vector machines (SVMs), random forest, and reinforcement learning (RL) are briefly introduced, with an emphasis on their potential and limitations in advanced wastewater applications. The main challenges obstructing the implementation, namely data quality, interpretability, and transferability of ML, are identified. Finally, future research trends are proposed, including ML integration with big data, the Internet of Things (IoT), and hybrid model development. The review also underscores the need for interdisciplinary collaboration and investment in data management, along with the implementation of new policies addressing data privacy and security. By addressing these challenges, the integration of ML into MBRs has the potential to significantly enhance performance and reduce the energy footprint, providing a sustainable solution for advanced wastewater treatment.
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Fazi, M. Beatrice. "Distraction Machines? Augmentation, Automation and Attention in a Computational Age". New Formations 98, nr 98 (1.07.2019): 85–100. http://dx.doi.org/10.3898/newf:98.06.2019.
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It is often argued (and feared) that the human capacity to pay attention is being transformed by computational technologies. Are computing machines distraction machines? This article takes this question as its starting point in order to address concerns about attention deficits visà-vis questions and issues about the mechanisation of cognitive procedures. I will claim that, when approaching the attention ecology of the twenty-first century, it is necessary to differentiate between augmentation and automation. While augmentation implies the extension of predefined forms or modes of behaviour, contemporary developments in computational automation ask us instead to consider the possibility of moving beyond phenomenological analogies. The article will thus discuss how transformations in the capacity to pay attention in a computational age need to be analysed in relation to the emergence of quasi-autonomous artificial cognitive agents driven by AI technologies, such as those known as machine learning. I will argue that these artificial cognitive agents can no longer be described in terms of technological add-ons to pre-existing human cognitive capacities. Today, we think alongside machines that are, is a sense, already thinking. Similarly, we pay attention alongside machines that are, in a sense, already paying attention. The challenge for philosophy and cultural theory is that of moving beyond 'projectionist' conceptions of such technological agency. This challenge, however, also involves overcoming the anthropomorphism that is implicit in expression such as 'thinking machines'. In a century where robot-to-robot communications have outpaced and outnumbered human-machine interactions, these artificial cognitive agents are not just reframing the human capacity to pay attention: they are also re-structuring the conditions for such capacity. Addressing the conditions for attention beyond augmentation and vis-à-vis computational automation involves considering the role and scope of both human and algorithmic decisionmaking, and engaging with the ways in which the humanities can intervene upon contemporary complex cognitive scenarios.
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Li, Keqin, Peng Zhao, Shuying Dai, Armando Zhu, Bo Hong, Jiabei Liu, Changsong Wei, Wenqian Huang i Yang Zhang. "Exploring the Impact of Quantum Computing on Machine Learning Performance". Middle East Journal of Applied Science & Technology 07, nr 02 (2024): 145–61. http://dx.doi.org/10.46431/mejast.2024.7215.
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This paper delves into the integration of machine learning and quantum computing, highlighting the potential of quantum computing to enhance the performance and computational efficiency of machine learning. Through theoretical analysis and experimental studies, this paper demonstrates how quantum computing can accelerate traditional machine learning algorithms via its unique properties of superposition and entanglement, particularly in handling large datasets and solving high-dimensional problems. Detailed introductions to quantum-enhanced machine learning models such as quantum neural networks and quantum support vector machines are provided, and their efficacy is validated through experimental applications in tasks like handwriting digit recognition. Results indicate that the parallel processing capabilities of quantum computing significantly enhance the speed and precision of model training, while also addressing the challenges and potential solutions for practical applications of quantum computing. Finally, the paper discusses future research directions and the importance of interdisciplinary collaboration in the integration of machine learning and quantum computing.
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Zhang, Tongrui, Ran Li i Yongqin Zhou. "Battery Fault Diagnosis Method Based on Online Least Squares Support Vector Machine". Energies 16, nr 21 (26.10.2023): 7273. http://dx.doi.org/10.3390/en16217273.
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Battery fault diagnosis technology is crucial for the reliable functioning of battery systems. This research introduces an online least squares support vector machine method tailored for battery fault diagnosis. After examining battery fault types and gathering relevant data, this method creates a diagnostic model, effectively addressing small and sporadic fault data that is inadequately handled by conventional support vector machines. Recognizing that certain battery malfunctions evolve over time and are multifaceted, confidence intervals have been integrated into the diagnostic models, enhancing accuracy. Upon testing this model using empirical data, it demonstrated rapid diagnostic capabilities and outperformed other algorithms in identifying progressive faults, ensuring precise fault identification, minimizing false alarms, and bolstering battery system safety.
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Murphy, Maureen, i Barbara Polivka. "Parental Perceptions of the Schools’ Role in Addressing Childhood Obesity". Journal of School Nursing 23, nr 1 (luty 2007): 40–46. http://dx.doi.org/10.1177/10598405070230010701.
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As childhood obesity has increased, schools have struggled with their role in this epidemic. Parents with a school-age child in a suburban latchkey program were surveyed regarding their perceptions of childhood obesity, body mass index, and the school’s role in prevention and treatment of obesity. More than 80% of participants identified inactivity, poor eating behavior, lack of parental control in what children eat, and eating too much as the main causes of childhood obesity. Parents preferred receiving information about their child’s body mass index from the school via a letter from the school nurse. Participants agreed that physical education classes, as well as units on nutrition and weight control, should be present in schools. Parents also supported eliminating junk food machines and offering special low-calorie meals. By supporting these strategies, parents indicated that schools should have a role in childhood obesity. School nurses can advocate for parental preferences in their school district.
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Sagar, K. Manoj. "MultiClass Text Classification Using Support Vector Machine". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, nr 12 (1.12.2023): 1–10. http://dx.doi.org/10.55041/ijsrem27465.
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Support vector machine (SVM) was initially designed for binary classification .To solve multi-class problems of support vector machines (SVM) more efficiently, a novel framework, which we call class-incremental learning (CIL) CIL reuses the old models of the classifier and learns only one binary sub-classifier with an additional phase of feature selection when a new class comes. In text classification, where computers sort text documents into categories, keeping up with new information can be tricky. Traditional methods need lots of retraining to adapt. However, Incremental Learning for multi-class Support Vector Machines (SVMs) offers a solution. It lets us update the model with new data while remembering what it learned before .In this project, we'll explore how Incremental Learning makes multi-class SVMs better at handling changing data and even learning about new categories as they appear .There is a problem in addressing the challenge of integrating new classes while maintaining classification accuracy on existing and new classes .The main goal of this project is to create a method that effectively adapts the MC- SVM to evolving data distributions while minimizing the impact on previously learned classes and optimising resource utilization. Keywords— feature extraction, support vector machine, multi class incremental learning, Gaussian kernel.
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Anil, Aindree. "Enhancing Criminal Analysis through Multi-Model Integration: Addressing Challenges and Ensuring Ethical Implementation". International Journal for Research in Applied Science and Engineering Technology 12, nr 5 (31.05.2024): 2306–10. http://dx.doi.org/10.22214/ijraset.2024.62056.
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Abstract: Law enforcement agencies face numerous challenges in criminal analysis, from data quality issues to ethical concerns. To address these challenges, this research proposes a novel approach: integrating multiple modelling techniques into a unified framework. By combining regression analysis, decision trees, support vector machines, neural networks, and ensemble methods, this approach aims to provide more robust and accurate solutions. The research seeks to develop a framework for integration, evaluate effectiveness using real-world data, and explore ethical implications. Ultimately, the goal is to advance criminal analysis, promote multi-model integration in law enforcement, and ensure ethical implementation for justice.
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Du, Shuangli, Haohao Fu, Xueguang Shao, Christophe Chipot i Wensheng Cai. "Addressing Polarization Phenomena in Molecular Machines Containing Transition Metal Ions with an Additive Force Field". Journal of Chemical Theory and Computation 15, nr 3 (24.01.2019): 1841–47. http://dx.doi.org/10.1021/acs.jctc.8b00972.
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Harshith, Maddila, Ayushi Sahu, Sanju Indrakanti, R. Kameshwar Reddy i Sunil Bhutada. "Optimizing Crop Yields through Machine Learning-Based Prediction". Journal of Scientific Research and Reports 29, nr 4 (12.04.2023): 27–33. http://dx.doi.org/10.9734/jsrr/2023/v29i41741.
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The application of machine learning techniques in agriculture, particularly in harvest forecasting, is gaining traction as a means of addressing this issue. The major project, "Optimizing Crop Yields through Machine Learning-Based Prediction," takes a comprehensive approach to this issue by considering a variety of parameters, including temperature, humidity, rainfall, and soil nutrient levels, to Figure out which crop is best to grow in those conditions. Naive Bayes, Random Forest, Support Vector Machines, Decision Trees, K-Nearest Neighbours, and Bagging, as well as feature selection methods like Synthetic Minority Oversampling Technique, Majority Weighted Minority Oversampling Technique, Random Over-Sampling Examples, and Recursive Feature Elimination, are used to accomplish this. High precision rates and improved forecast outcomes are the goals of these methods. Using machine learning techniques in crop forecasts, farmers can gain useful insights and make decisions based on data that increase crop production and overall agricultural productivity. This work demonstrates the potential of machine learning to address issues in agriculture and influence the sector's future.
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JÓZWIK, Jerzy, Magdalena ZAWADA-MICHAŁOWSKA, Monika KULISZ, Paweł TOMIŁO, Marcin BARSZCZ, Paweł PIEŚKO, Michał LELEŃ i Kamil CYBUL. "MODELING THE OPTIMAL MEASUREMENT TIME WITH A PROBE ON THE MACHINE TOOL USING MACHINE LEARNING METHODS". Applied Computer Science 20, nr 2 (30.06.2024): 43–59. http://dx.doi.org/10.35784/acs-2024-15.
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This paper explores the application of various machine learning techniques to model the optimal measurement time required after machining with a probe on CNC machine tools. Specifically, the research employs four different machine learning models: Elastic Net, Neural Networks, Decision Trees, and Support Vector Machines, each chosen for their unique strengths in addressing different aspects of predictive modeling in an industrial context. The study examines as input parameters such as material type, post-processing wall thickness, cutting depth, and rotational speed over measurement time. This approach ensures that the models account for the variables that significantly affect CNC machine operations. Regression value, mean square error, root mean square error, mean absolute percentage error, and mean absolute error were used to evaluate the quality of the obtained models. As a result of the analyses, the best modeling results were obtained using neural networks. Their ability to accurately predict measurement times can significantly increase operational efficiency by optimizing schedules and reducing downtime in machining processes.
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Sevryugina, Nadegda, i Pavel Kapyrin. "Technological machines, construction resources, efficiency and safety". MATEC Web of Conferences 178 (2018): 06017. http://dx.doi.org/10.1051/matecconf/201817806017.
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Understanding the direct relationship of the person, his activities Wednesday habitats requires not only new methodological approaches to research, but, first and foremost, clarify the existing scientific theories, their coherence with the development the environment Wednesday. A model of modernization of transport and technological machines as a factor in the realization of tasks of improving their effectiveness, safety and environmental performance. The main focus in addressing modifications of technological machines in use is considered lowering their material intensity, by adopting a more rugged and durable in endurance and wear materials. Modified methodology of estimation of residual resource of technical facilities by implementing technology upgrades for further reliable and safe operation. In the method a key factor is the effective and safe operation. Developed a calculation model of optimization of the resource in terms of assessing risk of system failure due to functional aging; optimization model of the resource, frequency of maintenance and wear limit of units in terms of assessing risk of occurrence of a system failure affecting safety. The solution of the question of resource upgrading to provide manufacturers, as an additional stage of the life cycle of construction machinery and means of complex mechanization.
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D, Suma, Raviraja Holla M i Darshan Holla M. "Decoding sarcasm: unveiling nuances in newspaper headlines". International Journal of Electrical and Computer Engineering (IJECE) 14, nr 3 (1.06.2024): 3011. http://dx.doi.org/10.11591/ijece.v14i3.pp3011-3020.
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This study navigates the intricate landscape of sarcasm detection within the condensed confines of newspaper titles, addressing the nuanced challenge of decoding layered meanings. Leveraging natural language processing (NLP) techniques, we explore the efficacy of various machine learning models—linear regression, support vector machines (SVM), random forest, na¨ıve Bayes multinomial, and gaussian na¨ıve Bayes—tailored for sarcasm detection. Our investigation aims to provide insights into sarcasm within the succinct framework of newspaper titles, offering a comparative analysis of the selected models. We highlight the varied strengths and weaknesses of these models. Random forest exhibits superior performance, achieving a remarkable 94% accuracy in accurately identifying sarcasm in text. It is closely trailed by SVM with 90% accuracy and logistic regression with 83% accuracy.
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Katona, Mihály, i Tamás Orosz. "Circular Economy Aspects of Permanent Magnet Synchronous Reluctance Machine Design for Electric Vehicle Applications: A Review". Energies 17, nr 6 (14.03.2024): 1408. http://dx.doi.org/10.3390/en17061408.
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Innovative technological solutions have become increasingly critical in addressing the transportation sector’s environmental impact. Passenger vehicles present an opportunity to introduce novel drivetrain solutions that can quickly penetrate the electric vehicle market due to their shorter development time and lifetime compared to commercial vehicles. As environmental policy pressure increases and customers demand more sustainable products, shifting from a linear business approach to a circular economy model is in prospect. The new generation of economically competitive machines must be designed with a restorative intention, considering future reuse, refurbishment, remanufacture, and recycling possibilities. This review investigates the market penetration possibilities of permanent magnet-assisted synchronous reluctance machines for mini and small-segment electric vehicles, considering the urban environment and sustainability aspects of the circular economy model. When making changes to the materials used in an electric machine, it is crucial to evaluate their potential impact on efficiency while keeping the environmental impact of those materials in mind. The indirect ecological effect of the vehicle’s use phase may outweigh the reduction in manufacturing and recycling at its end-of-life. Therefore, thoroughly analysing the materials used in the design process is necessary to ensure maximum efficiency while minimising the environmental impact.
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Li, Yang, Shang Ping Li, Cheng Chen i Chuang Rui Zheng. "Design and experiment of real-time reseeding system for transversal sugarcane planter with seeds pre-cutting". Applied and Computational Engineering 65, nr 1 (23.05.2024): 1–9. http://dx.doi.org/10.54254/2755-2721/65/20240454.
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The issue of sugarcane seed leakage is one of the practical challenges encountered in the current utilization of sugarcane planting machinery, directly leading to reduced yields in sugarcane fields. This paper addresses the problem of seed leakage in the process of transverse planting of pre-cut sugarcane using theoretical analysis, modeling, simulation, and experimental research. It designs a real-time reseeding system for pre-cut sugarcane transverse planting machines, composed of seed boxes, backup seed rollers, reseeding rollers, and an electronic control system. The real-time reseeding system detects seed leakage on the planting machines seeding chain to control the reseeding mechanism, filling the gaps in the seeding chain with sugarcane seeds. During experimentation, the real-time reseeding system achieved a maximum reduction of seed leakage by 6% in the seeding chain, effectively addressing the issue of seed leakage in pre-cut sugarcane transverse planting machines.
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Selema, Ahmed, Mohamed N. Ibrahim i Peter Sergeant. "Advanced Manufacturability of Electrical Machine Architecture through 3D Printing Technology". Machines 11, nr 9 (10.09.2023): 900. http://dx.doi.org/10.3390/machines11090900.
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The rapid evolution of electric machines requires innovative approaches to boost performance, efficiency, and sustainability. Additive Manufacturing (AM) has emerged as a transformative technique, reshaping the landscape of electric machine components, ranging from magnetic materials to windings and extending to thermal management. In the area of magnetic materials, AM’s capacity to fabricate intricate structures optimizes magnetic flux dynamics, yielding advanced shape-profile cores and self-coating laminations for superior performance. In windings, AM’s prowess is evident through innovative concepts, effectively mitigating AC conduction effects while reducing weight. Furthermore, AM revolutionizes thermal management, as exemplified by 3D-printed ceramic heat exchangers, intricate cooling channels, and novel housing designs, all contributing to enhanced thermal efficiency and power density. The integration of AM not only transcends conventional manufacturing constraints but also promises to usher in an era of unprecedented electric machine innovation, addressing the intricate interplay of magnetic, winding, and thermal dynamics.
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Zhou, Qi, Xuyan Zhang i Chaoqun Wu. "A Novel MSFED Feature for the Intelligent Fault Diagnosis of Rotating Machines". Machines 10, nr 9 (29.08.2022): 743. http://dx.doi.org/10.3390/machines10090743.
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The vibration energy distribution pattern usually changes with the rotating machine’s health state and is a good indicator for intelligent fault diagnosis (IFD). The existing initial features such as RMS are less effective in revealing the vibration energy distribution pattern, and the frequency spectrum cannot provide a rich and hierarchical description of the vibration energy distribution pattern. Addressing this issue, we proposed a multi-scale frequency energy distribution (MSFED) feature for the IFD of rotating machines. The MSFED feature can reveal the vibration energy distribution patterns in the frequency domain in a multi-scale manner, and its one-dimensional vector and two-dimensional map formats make it usable for most IFD models. Experimental validation on the gearbox and bearing datasets verified that the MSFED feature achieved the highest diagnostic accuracy among commonly used initial features, in typical fault diagnosis scenarios except for the variable-load scenario. Furthermore, the separability and transferability of the MSFED feature were evaluated by distance-based metrics, and the results were in agreement with the features’ diagnostic performance. This work provides an important reference for the IFD of rotating machines, not only proposing a novel MSFED feature but also opening a new avenue for model-independent methods of the initial quality evaluation.
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Reddy, Dr M. Rama Prasad, Chodagam Srinivas, Bireddi Eswararao, Rajendraprasad Kuriti i Dr M. Koteswara Rao. "Addressing Power Loss and Voltage Profile Issues in Electrical Distribution Systems: A Novel Approach Using Polar Bear Gradient-Based Optimization". International Journal of Electrical and Electronics Research 11, nr 3 (23.09.2023): 788–93. http://dx.doi.org/10.37391/ijeer.110323.
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Energy is an essential commodity for everyone, with electrical energy being the most preferred form. Unfortunately, non-renewable energy resources are gradually depleting, and renewable energy sources take several years to establish. To mitigate this problem, technology has shifted from non-renewable energy sources to electrical devices and machines, including household appliances like washing machines and air conditioners. However, the generation of electricity is still inadequate to meet the growing demand. This leads to two major problems: high power loss and poor voltage profile, making it difficult for power distribution companies to ensure a consistent and reliable power supply. This paper aims to address the reduction and minimization of power losses by adjusting distribution side transformer tap settings using the polar bear gradient-based optimization. The proposed approach uses the 14-bus system as a reference and calculates losses for this system using the backward-forward sweeping technique. The results are compared with standard PSO algorithm; the proposed strategy shows superior results.
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Wan Mahmood, Wan Hasrulnizzam, Mohd Nizam A. Rahman, Md Deros Baba i Jaharah Abd Ghani. "Improving Production Line Performance: A Case Study". Applied Mechanics and Materials 44-47 (grudzień 2010): 4136–40. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.4136.
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A case-based research method was chosen with the aim to provide an exemplar of practice and test the proposition that the use of simulation can improve productivity. Three alternatives were performed by considering the aspects of operator, machine, and workstation to define productivity improvement alternatives for operation optimisation. The research determines the optimum result to improve the current operation system. The experiments on simulated and real data clearly indicate that the productivity improvement in the current performance can be achieved by re-allocating the number of operators and machines effectively instead of a combination. The paper presents a novel example of the use of simulation to estimate the production line performance. The paper highlights this method by addressing this operational issue and the likelihood of the success of the strategic decision to improve productivity.
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Shetty, Nayana. "A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems". September 2021 3, nr 3 (7.08.2021): 135–48. http://dx.doi.org/10.36548/jscp.2021.3.001.
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For the purpose of high performance computation, several machines are developed at an exascale level. These machines can perform at least one exaflop calculations per second, which corresponds to a billion billon or 108. The universe and nature can be understood in a better manner while addressing certain challenging computational issues by using these machines. However, certain obstacles are faced by these machines. As huge quantity of components is encompassed in the exascale machines, frequent failure may be experienced and also the resilience may be challenging. High progress rate must be maintained for the applications by incorporating certain form of fault tolerance in the system. Power management has to be performed by incorporating the system in a parallel manner. All layers inclusive of fault tolerance layer must adhere to the power limitation in the system. Huge energy bills may be expected on installation of exascale machines due to the high power consumption. For various fault tolerance models, the energy profile must be analyzed. Parallel recovery, message-logging, and restart or checkpoint fault tolerance models for rollback recovery are evaluated in this paper. For execution with failure, the most energy efficient solution is provided by parallel recovery when programs with various programming models are used. The execution is performed faster with parallel recovery when compared to the other techniques. An analytical model is used for exploring these models and their behavior at extreme scales.
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Baraškova, Tatjana, Karolina Kudelina i Veroonika Shirokova. "New Opportunities in Real-Time Diagnostics of Induction Machines". Energies 17, nr 13 (3.07.2024): 3265. http://dx.doi.org/10.3390/en17133265.
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This manuscript addresses the critical challenges in achieving high-accuracy remote control of electromechanical systems, given their inherent nonlinearities and dynamic complexities. Traditional diagnostics often suffer from data inaccuracies and limitations in analytical techniques. The focus is on enhancing the dynamic model accuracy for remote induction motor control in both closed- and open-loop speed control systems, which is essential for real-time process monitoring. The proposed solution includes real-time measurements of input and output physical quantities to mitigate inaccuracies in traditional diagnostic methods. The manuscript discusses theoretical aspects of nonlinear torque formation in induction drives and introduces a dynamic model employing vector control and speed control schemes alongside standard frequency control methods. These approaches optimize frequency converter settings to enhance system performance under varying nonlinear conditions. Additionally, the manuscript explores methods to analyze dynamic, systematic errors arising from frequency converter inertial properties, thereby improving electromechanical equipment condition diagnostics. By addressing these challenges, the manuscript significantly advances the field, offering a promising future with enhanced dynamic model accuracy, real-time monitoring techniques, and advanced control methods to optimize system reliability and performance.
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Figueroa-Torrez, Paulo, Orlando Durán, Broderick Crawford i Felipe Cisternas-Caneo. "A Binary Black Widow Optimization Algorithm for Addressing the Cell Formation Problem Involving Alternative Routes and Machine Reliability". Mathematics 11, nr 16 (11.08.2023): 3475. http://dx.doi.org/10.3390/math11163475.
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The Cell Formation Problem (CFP) involves the clustering of machines to enhance productivity and capitalize on various benefits. This study addresses a variant of the problem where alternative routes and machine reliability are included, which we call a Generalized Cell Formation Problem with Machine Reliability (GCFP-MR). This problem is known to be NP-Hard, and finding efficient solutions is of utmost importance. Metaheuristics have been recognized as effective optimization techniques due to their adaptability and ability to generate high-quality solutions in a short time. Since BWO was originally designed for continuous optimization problems, its adaptation involves binarization. Accordingly, our proposal focuses on adapting the Black Widow Optimization (BWO) metaheuristic to tackle GCFP-MR, leading to a new approach named Binary Black Widow Optimization (B-BWO). We compare our proposal in two ways. Firstly, it is benchmarked against a previous Clonal Selection Algorithm approach. Secondly, we evaluate B-BWO with various parameter configurations. The experimental results indicate that the best configuration of parameters includes a population size (Pop) set to 100, and the number of iterations (Maxiter) defined as 75. Procreating Rate (PR) is set at 0.8, Cannibalism Rate (CR) is set at 0.4, and the Mutation Rate (PM) is also set at 0.4. Significantly, the proposed B-BWO outperforms the state-of-the-art literature’s best result, achieving a noteworthy improvement of 1.40%. This finding reveals the efficacy of B-BWO in solving GCFP-MR and its potential to produce superior solutions compared to alternative methods.