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Analysis on Operation Mode of Y-Type Rail Transit in Suburbs

Analysis on Operation Mode of Y-Type Rail Transit in Suburbs

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2021 international conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2021

作者： Gao, Qiqi Mao, Baohua Wang, Min Zhou, Qi Ge, Yi Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing China School of Transportation Beijing Jiaotong University Beijing China China Railway Siyuan Survey and Design Group Co. LTD China

ISBN: (纸本)9781643682549

The construction of a Y-type urban rail transit line can effectively solve the problems of low line coverage and low utilization in suburban areas. The purpose of this paper is to study and analyze the effect of different operation modes of the Y-type line of suburban urban rail transit. In this paper, the objective function is to minimize value of passenger travel time costs and enterprise operating costs, and the main and branch train frequencies are used as decision variables to establish a selection model for Y-type operation mode, and design an improved multi-objective optimization algorithm based on NSGA-II. A case study was carried out on the Y-type line to evaluate the effect of each scheme. The results of the study found that there is no obvious difference between the advantages and disadvantages of the three operation modes. The passenger waiting time under the independent operation plan is the highest but the comfort level is greater, and the train travel distance is the lowest. The partial- through operation accommodates the interests of both passenger and business. Therefore, the corresponding operating mode should be selected according to the preferences of passengers and enterprises. © 2022 The authors and IOS Press.

关键词： Operating costs

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Task Migration in Volunteer computing Systems

Task Migration in Volunteer Computing Systems

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ehab Saleh Chandrasekar Shastry Department of Computer Science and Engineering Jain University Bangalore India

Volunteer computing is the use of underutilised computing power donated by volunteers who want to participate in ongoing high-throughput scientific projects. Due to the general simplicity of this concept, this type of computing attracts thousands of volunteers from all over the world. Many of them, however, leave the network, prompting the main server to initiate Task Migration, which involves transferring the remaining job to another volunteer’s device on the network. In this paper, we conduct two experiments in the task migration procedure in peer-to-peer volunteer network. In the first experiment, the server starts the migration procedure when it detects the first available volunteer, whereas in the second one, the server starts the migration after all of the server’s sub-peers have completed their jobs. To ensure the network’s heterogeneity, we select the dataset GWA-T-13 Materna, which contains performance metrics described as trace files of over 1500 VMs from the distributed Materna data Centers in Dortmund, Germany. In both experiments, we compare the total execution time of the entire task and the maximum execution time that a peer can have to complete its assigned work. The simulation results show that in the second experiment, the main task was completed in less time than in the first experiment.

关键词： Measurement Distributed processing data centers Simulation Computer applications Peer-to-peer computing Servers

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Estimating State of Charge and State of Health of Electrified Vehicle Battery by data Driven Approach: Machine Learning

Estimating State of Charge and State of Health of Electrifie...

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Emerging technologies in Electronics, computing and Communication (ICETECC), international conference on

作者： Shaffa Ali Memon Ali Hamza Syed Sajjad Haider Zaidi Bilal Muhammad Khan Electronics and Power Engineering (EPE) Pakistan Navy Engineering College (PNEC) Karachi National University of Science and Technology (NUST) Islamabad Pakistan School of Electrical Engineering and Computer Science (SEECS) H-12 (NUST) Islamabad National University of Science and Technology (NUST) Islamabad Pakistan

The recent development, increased interest and achievements in artificial intelligence and Machine Learning (ML) have facilitated the development of novel methods for estimating State of charge (SoC) and State of heath (SoH) of electrified car batteries. SoC and SoH are critical to the performance, passenger comfort, and safety of electric vehicles (EVs), as well as minimizing costs associated with overdesign or oversizing of the battery pack. Two methods of ML techniques: Feedforward Back Propagation Neural Network (FBPNN) and Cascaded Feedforward Neural Network (CFNN) for estimation of SoC and SoH for electrified car batteries have been proposed using real time sample data retrieved from NASA Ames Prognostics and Panasonic 18650PF Li-Ion Battery data Repository. The input data set contains discharging current, ambient temperature and battery voltage. The ML algorithms have been trained using three inputs and battery states (SoH and SoC) of electrified car batteries as targets. The MATLAB based nntool toolbox has been utilized for estimation purpose. The results demonstrated that proposed CFNN has better performance in estimation and have smaller overshoots and undershoots from the actual value than the FBPNN.

关键词： Backpropagation Maximum likelihood estimation Temperature distribution Machine learning Voltage Batteries Feedforward neural networks

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Performance comparison of software-defined network controllers 1

Performance comparison of software-defined network controlle...

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1st IEEE international conference on advances in Electrical, computing, Communications and Sustainable technologies, ICAECT 2021

作者： Shirvar, Arash Goswami, Bhargavi School of Computer Science Queensland University of Technology Brisbane Australia

ISBN: (纸本)9781728157900

The concept of Software-Defined Networking (SDN) was introduced to replace the current network infrastructures suffering from being highly hardware-based, inflexible to change and development, error-prone for the configuration of proprietary devices, etc. Optimistically though, SDN is set to address these challenges by its intrinsic merits including its centralized software-based architecture with vendor-neutral devices, flexibility to change and development, the programmability of data transmission functionalities. Nevertheless, it has yet to be fully developed and implemented. One major reason behind this setback is believed to be a lack of advancement of a controller for centralized management of the SDN environment. To achieve a highly advanced and reliable controller, first, it is vital to find the most essential features and capabilities of the current controllers. With this, the paper intends to first, compare ten major existing SDN controllers against various essential criteria and then evaluate the scalability and performance based on various topologies using mininet, an SDN network emulator. Ultimately, after conducting both evaluative stages, the key results and contributions of this paper are 1. A detailed list of all the key features and capabilities that every controller must entail 2. Python-based controllers cannot meet the high performance and low latency in large networks whereas Java and C-based controllers can offer better scalability and performance 3. A verdict on the most suitable controller based on the Multi-Criteria Decision Making technique. © 2021 IEEE

关键词： Software defined networking

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Analysis of the Cyber Attacks in Indonesia’s Health Sector in Industry 4.0 Era

Analysis of the Cyber Attacks in Indonesia’s Health Sector ...

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international conference on computing, Engineering, and Design (ICCED)

作者： Arif Hidayat Teddy Mantoro Armi Susandi Medical Intelligence Post-Graduate Program Indonesian State Intelligence College Bogor Indonesia Department of Computer Science Media-Tech Lab Sampoema University Jakarta Indonesia

ISBN: (纸本)9781665453905

At this time the world has undergone considerable changes, where the world has entered the era of the industrial revolution 4.0. With these changes, there are many positive impacts or opportunities that can support human activities in the economic, political, socio-cultural, health, and other fields. And alsothe author tries to analyze about industry 4.0 era for Indonesia’s health sector with SWOT Analysis to know about Strenght, Weaknesses, Opportunities, and Threats. However, the challenges of technological advances can pose a threat to countries that are not ready to facethem, one of which is cyber attacks that can threaten data security. Therefore, it is necessary to be prepared in terms of data security by cooperating with various parties involved in it.

关键词： Law data security Weapons Government data protection Security National security

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Evaluating Machine Learning Models Best Fit for Crime Prediction in Windhoek

Evaluating Machine Learning Models Best Fit for Crime Predic...

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international conference on advances in Big data, computing and data Communication Systems (icABCD)

作者： Maria Liisa Angondji Mercy Chitauro Fungai Bhunu Shava Department of Informatics Namibia University of Science and Technology Windhoek Namibia

ISBN: (数字)9798350387902

ISBN: (纸本)9798350387919

Crime refers to any unlawful act which is punishable by law. Crime has a harmful effect on the community, it hinders the economic growth and it causes fears in the society. Crime rates have been on the increase around the world, which led to law enforcement agencies and stakeholders to implement crime prevention approaches such as predictive policing as most of the traditional crime-solving techniques were proven to be incompetent and ineffective. In Namibia, there is a lack of data-driven predictive method for combating crime. Hence, the paper evaluated machine learning (ML) models best fit for predicting crime types in Windhoek. The study employed a quantitative research approach to historical crime data from January 2018 to December 2022. After pre-processing, supervised classification models namely; Random Forest, K-Nearest Neighbors (KNN), Naive Bayes, and Decision Trees were trained and tested on the dataset. Initial findings revealed low accuracy rates across all models due to the imbalanced class distribution within the crime dataset mainly on the target variable which is Type of crime. To tackle the imbalances, various resampling techniques namely, Synthetic Minority Over-sampling Technique (SMOTE), Edited Nearest Neighbor (ENN), SMOTETomek and SMOTEEN were applied, leading to a significant improvement in model performance. The Random Forest model demonstrated superior performance, especially when trained on balanced datasets created through SMOTE and SMOTETomek, achieving an accuracy rate of 84%. The paper highlights the important role of addressing class imbalance through resampling techniques to improve the predictive accuracy of machine learning models in crime prediction. The findings suggest that the Random Forest model, coupled with appropriate resampling methods, provides a robust solution for crime type prediction in Windhoek.

关键词： Economics Accuracy Law enforcement Biological system modeling Prevention and mitigation Predictive models Nearest neighbor methods

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High speed data transactions for memory controller based on AXI4 interface protocol SoC 1

High speed data transactions for memory controller based on ...

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1st IEEE international conference on advances in Electrical, computing, Communications and Sustainable technologies, ICAECT 2021

作者： Noami, Ahmed Alahdal, Abdullah Kumar, B. Pradeep Chandrasekhar, P. Safi, Nada College of Engineering Osmania University Hyderabad India College of Science Osmania University Hyderabad India

ISBN: (纸本)9781728157900

Recently, the latency resulted from the communication between the multi-core processor IP cores and the other IP cores such memory controller and peripherals on the same die SoC became a critical issue especially when all the core processors using advanced interface protocol such AXI4 and make write and read transactions in different burst mode capability. In this paper, we proposed a multi-core memory controller for four core processors using the AXI4 interface protocol. The multi-core memory controller can make the write and read transactions using the AXI4 interface protocol in parallel to improve the entire system speed up on the same die SoC. The SoC design architecture is modeled in System Verilog HDL, simulation and synthesis are done by using the Vivado tool and FPGA ZYNQ-7 ZC702 Evaluation Board (xc7z020clg484-1). © 2021 IEEE

关键词： System-on-chip

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From data Literacy to Co-design Environmental Monitoring Innovations and Civic Action

From Data Literacy to Co-design Environmental Monitoring Inn...

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international conference on Wireless Communications, Networking and Applications, WCNA 2021

作者： Happonen, Ari Wolff, Annika Palacin, Victoria Software Engineering School of Engineering Science LUT University Lappeenranta53850 Finland Social Computing Research Group Faculty of Social Sciences University of Helsinki Helsinki00014 Finland

ISBN: (纸本)9789811924552

SENSEI is an environmental monitoring initiative run by Lappeenranta University of Technology (LUT University) and the municipality of Lappeenranta in south-east Finland. The aim was to collaboratively innovate and co-design, develop and deploy civic technologies with local civics to monitor positive and negative issues. These are planned to improve local’s participation to social governance issues in hand. These issues can be e.g. waste related matters like illegal dumping of waste, small vandalism into city properties, alien plant species, but on the other hand nice places to visits too. This publication presents initiatives data literacy facet overview, which is aimed at creating equitable access to information from open data, which in turn is hoped for to increase participants motivation and entrepreneurship like attitude to work with the municipals and the system. This is done by curating environmental datasets to allow participatory sensemaking via exploration, games and reflection, allowing citizens to combine their collective knowledge about the town with the often-complex data. The ultimate aim of this data literacy process is to enhance collective civic actions for the good of the environment, to reduce the resource burden in the municipality level and help citizens to be part of sustainability and environmental monitoring innovation activities. For further research, we suggest follow up studies to consider on similar activities e.g. in specific age groups and to do comparisons on working with different stage holders to pin point most appropriate methods for any specific focus group towards collaborative innovation and co-design of civic technologies deployment. © 2022, The Author(s).

关键词： Open data

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Enlargement of ubiquitous security organism for cloud computing 1st

Enlargement of ubiquitous security organism for cloud comput...

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1st international conference on Frontiers in Smart Systems technologies, ICFSST 2019

作者： Dhaya, R. Kanthavel, R. Devi, M. Algarni, Fahad Dixikha, Pooja Department of Computer Science King Khalid University Sarat Abidha Campus Abha Saudi Arabia Department of Computer Engineering King Khalid University Abha Saudi Arabia Department of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of ECE Sona College of Technology SalemTamil Nadu India

ISBN: (纸本)9789811550287

Cloud storage empowers clients to tenuously pile their information and appreciate the on-request excellent cloud requests deprived of the weight of neighborhood hardware and software the executives. But there is some security hazards headed for the precision of files or documents or information in the cloud in added with the loss of storage in the existing which utilize the homomorphic token. In order to discourse this delinquent, a new effective security mechanism is proposed called data aware routing (DAR). This scheme reduces the data contention by partitioning the data storage, and using the concept of logical IP, it makes the data secured in the cloud. It also preserves data integrity and reduces the transmission time by partitioning the data in the cloud. DAR proved to be an efficient scheme in terms of reducing the transmission time during data request © Springer Nature Singapore Pte Ltd 2021.

关键词： Cloud storage

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Machine Learning Based Hybrid Technique for Heart Disease Prediction

Machine Learning Based Hybrid Technique for Heart Disease Pr...

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advances in computing, Communication & Materials (ICACCM), 2020 international conference on

作者： Sourav Singh Negi Minakshi Memoria Rajiv Kumar Kapil Joshi Shiv Dayal Pandey Ashulekha Gupta Department of Computer Science and Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun India Uttaranchal Institute of Technology Uttaranchal University Dehradun India Dept. of Management studies Graphic Era University Dehradun India

ISBN: (纸本)9781665474405

There is a lot of information in the medical services industry. With such a big amount of data, the illness can often be identified, predicted, or reduced. Infections such as cardiovascular sickness, malignant development, tumours, and so on pose a significant threat to humanity. In this research, we try to focus on coronary sickness prediction using AI approaches. Coronary illness is frequently predicted. Information such as pulse, hypertension, diabetes, and cigarette smoking is collected, and these highlights are then displayed for forecasting. The calculations like K-nearest neighbor, Random Forest and Decision tree are used. We have also proposed a hybrid model of combining Decision Tree and Random Forest. The precision of the model is to investigate utilizing every one of the calculations. At that point with the more exactness is taken as a result of the model for anticipating the daringness infection.

关键词： Heart Radio frequency Industries Hypertension Computational modeling Medical services Predictive models

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