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Knowledge Transfer Factors for Internal Combustion Engine (ICE) Industry to Electric Vehicle (EV) Industry by Artificial Intelligent: Machine Learning 15

Knowledge Transfer Factors for Internal Combustion Engine (I...

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15th International Conference on Knowledge Management and Information Systems, KMIS 2023 as part of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Dangjaroen, Yinglak Wiriyapinit, Mongkolchai Sinthupinyo, Sukree Technopreneurship and Innovation Management Program Graduate School Chulalongkorn University Bangkok Thailand Department of Commerce Chulalongkorn Business School Chulalongkorn University Bangkok Thailand Department of Computer Engineering Faculty of Engineer Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9789897586712

This study aims to identify the factors influencing knowledge transfer within companies transitioning from the internal combustion engine (ICE) industry to the electric vehicle (EV) industry through an extensive literature review. In addition to summarizing findings and proposing strategies for utilizing artificial intelligence in knowledge transfer, our framework reveals the relevance of three key knowledge transfer factors and three distinct forms of artificial intelligence, including machine learning, in facilitating knowledge transfer. These insights can prove invaluable to entrepreneurs operating within the internal combustion engine automotive sector, offering essential guidance for enhancing the knowledge transfer process and navigating the transition to the electric vehicle industry. By implementing these strategies, businesses can maintain and support their competitiveness in this evolving business. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Internal combustion engines

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IoT-Based portable modules for energy consumption monitoring in smart home system 3

IoT-Based portable modules for energy consumption monitoring...

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3rd International Conference on Vocational Education and Technology, IConVET 2020

作者： Bayu, J.P. Aryanto, K.A.A. Suweken, G. Aryanto, K.Y.E. Study Program of Computer Science Graduate Program Universitas Pendidikan Ganesha Singaraja-Bali Indonesia Department of Computer System Stikom Bali Denpasar-Bali Indonesia

Energy consumption has become one of the major problems in Indonesia. The use of recent technology is highly beneficial since various automation could be done even in simple devices. In this research, portable smart home modules based on the internet of things (IoT) technology to monitor the power consumption in household electrical devices were built. The module consisted of current sensors, voltage sensors, and IoT Wi-Fi Development Board. It communicated with the server, built using the Raspberry Pi, using the MQTT protocol. The server was equipped with web pages that allowed users to monitor the devices' electrical power usage. Therefore, all of the connected modules could be monitored to provide information regarding an electric household device's defect. The results had shown that the prototypes of the modules had been successfully built. It was shown that very slight differences were found between the system measurements compared to the manual one using the 60 seconds interval measurements. The power consumed by the module was very low, where the current sensor uses 0.125 Watt while the voltage sensor uses 0.001 Watt. The portable devices were developed in the shape of small boxes;therefore, it could be easy to move and install. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment

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Procedia computer Science 2024年 245卷 127-136页

作者： Christian Kenneth Rudi Nirwantono Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco -derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy.

关键词： bioinformatics network pharmacology Calamus draco colorectal cancer drug discovery

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Multi-step ahead wind power forecasting based on N-BEATS model

Multi-step ahead wind power forecasting based on N-BEATS mod...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Luis Fernando Rodrigues Agottani Matheus Melara Girardi Wendel Rafael de Souza Chaves Lucas de Azevedo Takara Leandro dos Santos Coelho Viviana Cocco Mariani Graduate Program in Mechanical Eng. Federal University of Parana Curitiba Brazil Department of Computer Science Pontifical Catholic University of Parana Curitiba Brazil Graduate Program in Electrical Eng. Federal University of Parana Curitiba Brazil

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

For wind power systems to operate successfully and efficiently, accurate forecasting is critical. The integration of large wind farms with smart grid technology mainly depend on accurate models for wind power output prediction. This study introduces a neural basis expansion approach named N-BEATS (neural basis expansion analysis for interpretable time series forecasting) for time series multi-step wind power forecasting. N-BEATS is a deep neural network architecture composed of multiple fully connected layers, arranged with both forward and backward residual connections. The performance of N-BEATS was benchmarked using classical approaches including AutoRegressive Integrated Moving Average (ARIMA), Seasonal AutoRegressive Integrated Moving Average (SARIMA), Holt-Winters, and the recurrent neural network named Long Short-Term Memory (LSTM). All models hyperparameters were tuned by Bayesian optimization. The N-BEATS model demonstrated superior forecasting scores and outperformed traditional methods in metrics such as mean absolute error and root mean square error for different forecasting horizons.

关键词： Accuracy Time series analysis Europe Wind power generation Predictive models Wind farms Smart grids Forecasting Wind forecasting Long short term memory

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YOLOS-MCI: Automating Microservices Coupling Index Calculation using Vision Transformers

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Procedia computer Science 2024年 245卷 924-933页

作者： Gintoro Yaya Heryadi Lukas Lili Ayu Wulandhari Ilvico Sonata Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta 12930 Indonesia

Microservice Architectures (MSA) provide flexibility and scalability in software development. However, accurately measuring the level of interdependence among Microservices continues to be a difficult task. Precisely evaluating this connection is essential for efficient MSA design, maintenance, and future development. Conventional techniques for assessing Microservice coupling are frequently done by hand, require a significant amount of time, and are susceptible to mistakes. This impedes the capacity to make well-informed judgments regarding the integration and adjustment of services. This study introduces a new method for automating the computation of the Microservice Coupling Index (MCI) by utilizing the You Only Look at One Sequence (YOLOS) object identification technique in combination with Vision Transformer (ViTs) technology. YOLOS is utilized for identifying constituents within Unified Modeling Language (UML) Component Diagrams, facilitating precise classification and effective assessment of coupling. The model exhibits varying performance over multiple Intersection over Union (IoU) thresholds and object sizes, with an average precision (AP) of 0.406 over IoU values ranging from 0.50 to 0.95. The maximum precision is achieved at an IoU of 0.50, with an AP of 0.709. The model demonstrates good performance in identifying smaller components, especially when evaluated at a 0.75 IoU threshold. However, it faces challenges in detecting small items, suggesting potential areas for improvement in future iterations. Initial results indicate that this automation greatly decreases the need for manual, labor-intensive tasks and enhances the precision of measuring coupling in MSA, hence facilitating effective decision-making in service integration and modification. Automating the computation of the coupling index has the potential to significantly influence the design and management of durable and readily controllable microservice architectures.

关键词： Coupling Microservices Microservices Coupling Index UML Component Diagrams Vision Transformer YOLO

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Improving Supplier Evaluation Model using Ensemble Method-Machine Learning for Food Industry

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Procedia computer Science 2023年 227卷 307-315页

作者： Muhammad Asrol Sofyan Wahyudi Suharjito Christian Harito Ditdit N Utama Muhammad Syafrudin Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Artificial Intelligence Sejong University Seoul 05006 South Korea

Supplier evaluation has a crucial role in maintaining efficiency in the food industry supply chain. Machine learning approaches can be employed to formulate models aimed at analyzing and evaluating supplier performance. Previous research has successfully designed decision tree and neural network models for assessing suppliers in the food industry with accuracies of 84.2% and 92.8% separately. Recognizing the opportunity to improve the model's performance, this study aims to advancing the machine learning models accuracy for analyzing and evaluating suppliers in the food industry. Two main models are proposed to enhance model accuracy: ensemble methods and support vector machine. This research has successfully designed a supplier evaluation model and demonstrated that the ensemble method - gradient boosting model outperforms other ensemble methods and support vector machine which is achieved a accuracy of 93.6% on a cross-validation dataset. The development of a dashboard is required to implement the supplier evaluation model using machine learning, facilitating decision-makers in evaluating and controlling supplier performance.

关键词： Ensemble Method Food Machine Learning Supply chain Supplier evaluation

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AI-driven Maintenance Tool for Synchronous Power Generator Temperature Monitoring Using Fiber Bragg Gratings

AI-driven Maintenance Tool for Synchronous Power Generator T...

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SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： Uilian José Dreyer Erlon V. da Silva Wilian Oliveira Paulo H. R. Mazzo Kleiton de M. Sousa André Biffe Di Renzo Cicero Martelli Jean Carlos Cardozo da Silva Graduate Program in Energy Systems (PPGSE) ENGlE Brasil Energia Universidade Tecnologica Federal do Parana-UTFPR Curitiba Parana Brazil Graduate Program in Electrical and Computer Engineering (CPGEI)

The carbon brushes and slip rings of a hydrogen-erator are the main components guiding the excitation current from the bridge to the rotor windings. The brushes' temperature are crucial to infer their operational condition and, the generator status. This work presents the temperature measurement of six Fiber Bragg Gratings (FBG) sensors installed in a 370 MVA electric generator brushes. The results show the sensors' capacity to monitor the brushes' temperature in accordance with the current flowing through them. Together with the sensors, an Artificial Intelligence (AI) technique was applied to the measured temperature to detect anomalous events regarding the current supplied to the rotor windings. The optical sensors combined with the AI could detect five events of abnormal current behaviour. One is presented in detail in this paper. This sensing system can be further applied to online fault detection using the temperature measured by the FBGs as a brush condition indicator and a generator operation and maintenance tool.

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Six Sigma for Evaluating Electronic Signature in eProcurement System: A Case Study 4

Six Sigma for Evaluating Electronic Signature in eProcuremen...

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4th Asia Pacific Conference on Research in Industrial and Systems Engineering: Building Business Resilience to Face the Challenge in Pandemic Era, APCORISE 2021

作者： Nugroho, Eko Cahyo Saputra, Herry Gunarso, Davy Yeria Wibowo, Antoni Utama, Ditdit Nugeraha Computer Science Department Binus Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781450390385

Six-sigma is an approach to appraise a company's prospect in generating a number of piece with homogenized processes without any production defects or zero faults. It is operated not only for declining defect numbers, but also for reducing the company's imperfection itself. It means, company is able to decrease the cost (in term of money or time), also to increase the efficiency by evaluating current process and planning to improve company management process. In this study, six-sigma benefited to determine the success level of implementation of e-signature application in e-procurement system based on user experience rate. Conclusively, the implementation of "change management"has enhanced the business process in detail, while it was still in the equivalent sigma level 2. © 2021 Owner/Author.

关键词： Six sigma

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Recommendation System Journalist for Getting Top News Based on Twitter Data

Recommendation System Journalist for Getting Top News Based ...

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2019 International Conference of Science and Information Technology in Smart Administration, ICSINTeSA 2019

作者： Girsang, Abba Suganda Isa, Sani Muhamad Harvy, Ikrar Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

One application that can be utilized in finding the latest news is by utilizing the development of information and communication technology such as seeing the delivery of public information through social media such as twitter. This study implements the system which is able to get the trending or top news based on twitter data. This information will connect to the journalist via mobile application. It helps journalists to get the potential news to be reported in their media. The application will group each tweets into various themes based on the cosine similarity. The tweets which are more the certain threshold, can be indicated as the top news. The top news is then classified into one category news by using SVM algorithm. The training data becomes from the source data of one media news. The specific topic of this result will ease to order the journalist cover this top news. The system can be implemented well. © Published under licence by IOP Publishing Ltd.

关键词： Social networking (online)

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Social media data crowdsourcing as a new stream for environmental planning & monitoring: A review 2

Social media data crowdsourcing as a new stream for environm...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Lawu, B.L. Lim, F. Susilo, A. Surantha, N. Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This research focused on social media applications that had been used by large-scale users. Data might be in the form of text, image, video, each with its own data processing complexity. In this study, the researchers had performed a systematic review of literature on the development and usage of crowdsourcing data collection in recent days, with a range of techniques/methods/approaches to present meaningful data results/conclusions for environmental management. From the collected references, it is found that most papers discuss the monitoring and planning the environment through the use of social media data. An in-depth review of the literature was performed on the state-of-the-art environmental monitoring and planning and the general architecture of the data crowdsourcing system. Finally, based on the literature review, the challenge of data crowdsourcing for environmental monitoring and planning is identified and addressed. © Published under licence by IOP Publishing Ltd.

关键词： Crowdsourcing

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