Mechanical Engineering eJournal最新文献

{"title":"Is Factors Timing Overrated","authors":"Farah Bouzida, P. Digard","doi":"10.2139/ssrn.3697314","DOIUrl":"https://doi.org/10.2139/ssrn.3697314","url":null,"abstract":"In this paper we investigate two factor rotation approaches, performed directly on the MSCI smart beta indices that are Value, Quality, Momentum, Low Volatility and Size, over US and European Markets. Both approaches use the same indicators built on a macroeconomic signal (PMI), a market sentiment signal based on (VIX, credit spreads), and a momentum signal (time-series, cross-sectional). While the first approach is rule-based and mostly inspired by already known factor rotation frameworks, our work explores those by using our own specifications and it also seeks to check whether a style rotation works at the indices level. Our results show that our framework outperforms a simple equal-weight factor exposure in spite of application of transaction costs. On a stand-alone basis the PMI based rotation fol- lowed by the time-series momentum exhibit the strongest returns. Then we explore if machine learning techniques (tree-based) outperform equal-weight and the rule based strategies particularily after counting for transaction costs.","PeriodicalId":404477,"journal":{"name":"Mechanical Engineering eJournal","volume":"277 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114140516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT Based Smart Garbage Monitoring System with Geo-Tag","authors":"R. Naga Swetha, Ashwini Gona, Dr. D. Narendar Singh","doi":"10.2139/ssrn.3554257","DOIUrl":"https://doi.org/10.2139/ssrn.3554257","url":null,"abstract":"Cities around the globe are getting smarter. Some of the private and public organization dedicated for cleanliness of their cities round the clock. People are getting more active in doing all the things possible to clean their surroundings. Many initiations were taken by the government to increase cleanliness. Municipal access networks to support all types of city management services requiring a data connection .our system is designed to monitor the Garbage bins with geo location tag which will notify the status of bin to authorities to execute required action. Sensor on top of the garbage bin which will detect level of garbage in according to the Specification of the bin. When the garbage is reached to the maximum level, a notification will be sent to the concern authorities via SMS with a mapped location to take necessary action.","PeriodicalId":404477,"journal":{"name":"Mechanical Engineering eJournal","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122105394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Silk Fibroin Magnetoactive Nanocomposite Films and Membranes for Dynamic Bone Tissue Engineering Strategies","authors":"A. Reizabal, R. Brito‐Pereira, M. Fernandes, N. Castro, V. Correia, C. Ribeiro, C. M. Costa, L. Pérez, J. Vilas, S. Lanceros‐Méndez","doi":"10.2139/ssrn.3539234","DOIUrl":"https://doi.org/10.2139/ssrn.3539234","url":null,"abstract":"Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.mtla.2020.100709.","PeriodicalId":404477,"journal":{"name":"Mechanical Engineering eJournal","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131587317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ARP Spoofing Detection for IoT Networks Using Neural Networks","authors":"Husain Abdulla, H. Al-Raweshidy, Wasan S. Awad","doi":"10.2139/ssrn.3659129","DOIUrl":"https://doi.org/10.2139/ssrn.3659129","url":null,"abstract":"Networks equipped with Internet of Things (IoT) devices are increasingly under threat from an escalating number of cyber-attacks and breaches (Su et al., 2016). ARP-Spoofing attack is one of the Internet security problems that affects IoT devices. Attackers use legitimate ARP packets which traditional detection systems may find it difficult to detect in attacking IoT devices. Therefore, there is a need to have detection systems which use non-traditional approaches in detecting such attacks. This paper presents an artificial intelligence method based on neural networks in detecting ARP-Spoofing in IoT networks. This method showed more than 90% accuracy rate in detecting ARP-Spoofing in IoT networks while it was difficult to detecting ARP-Spoofing with ARIMA statistical method.","PeriodicalId":404477,"journal":{"name":"Mechanical Engineering eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130439124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series","authors":"Sidra Mehtab, Jaydip Sen","doi":"10.36227/techrxiv.15088734","DOIUrl":"https://doi.org/10.36227/techrxiv.15088734","url":null,"abstract":"Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modelled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of four years: 2015 – 2018. Based on the NIFTY data during 2015 – 2018, we build various predictive models using machine learning approaches, and then use those models to predict the “Close” value of NIFTY 50 for the year 2019, with a forecast horizon of one week, i.e., five days. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual “Close” values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network (CNN) with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.","PeriodicalId":404477,"journal":{"name":"Mechanical Engineering eJournal","volume":"11 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113980000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tailoring Thermal Properties of Ebcs in High Entropy Rare Earth Monosilicates","authors":"Mackenzie J. Ridley, J. Gaskins, P. Hopkins, E. Opila","doi":"10.2139/ssrn.3525134","DOIUrl":"https://doi.org/10.2139/ssrn.3525134","url":null,"abstract":"This work explores the possibility of tailoring the thermal conductivity and thermal expansion of rare earth monosilicates through the introduction of multiple rare earth cations in solid solution. Six rare earth monosilicates are studied: Sc2SiO5, Y2SiO5, Nd2SiO5, Dy2SiO5, Er2SiO5, and Yb2SiO5. Four equimolar binary cation mixtures and a high entropy five-cation equimolar mixture were characterized. Thermal expansion was measured up to 1200 ˚C with X-Ray Diffraction (XRD) and bulk thermal conductivity was measured by Hot Disk technique. The linear coefficient of thermal expansion (CTE) of mixed-cation systems followed a rule of mixtures, with average linear CTE between 6 - 9x10-6 /˚C. Scandium monosilicate showed a lower linear CTE value as well as a notably lower degree of CTE anisotropy than other rare earth monosilicates. Thermal conductivity was found to decrease below rule of mixtures values through increasing heterogeneity in rare earth cation mass and ionic radii, as expected for the thermal conductivity of solid-solutions. The high entropy mixture RE2SiO5 (RE=Sc, Y, Dy, Er, and Yb) shows a thermal conductivity of 1.06 W/mK at room temperature, demonstrating that high entropy rare earth silicates are strong candidates for novel dual-purpose thermal and environmental barrier coatings.","PeriodicalId":404477,"journal":{"name":"Mechanical Engineering eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129472444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Sleep/Awake Schedule with Multi-Hop Hierarchical Routing Algorithm for Wireless Sensor Networks","authors":"N. Srikanth, N. Neha, K. Mamatha, P. Adithya, P. Rutvik","doi":"10.2139/ssrn.3624701","DOIUrl":"https://doi.org/10.2139/ssrn.3624701","url":null,"abstract":"Non-uniform deployment of sensor nodes, energy constraints, dead nodes, interference links are the major limitations of WSN performance. Energy consumption, life time improvement are the key parameters to increase the throughput of WSN. For limiting the energy consumption of sensor nodes, they are driven into sleep mode after they ends sensing. Efficient utilization of energy resources by clustered nodes is heavily affected by cluster head selection. This work focuses on reducing the energy consumption by proposing an efficient enhanced energy aware multi-hop hierarchical routing algorithm. In order to overcome energy holes drainage problem due to non-uniform node distribution in the network, an energy hole removing mechanism keeps a node into sleep mode if the node energy is less than the threshold. The comparison results shows proposed algorithm is more efficient than other existing algorithms.","PeriodicalId":404477,"journal":{"name":"Mechanical Engineering eJournal","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121879404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Arduino Controlled Agribot For Multi-Purpose Cultivation","authors":"G. Vijaykumar, K. Vijayalakshmi, Ch. V. Pujitha, DN Sandhya Rani","doi":"10.2139/ssrn.3643592","DOIUrl":"https://doi.org/10.2139/ssrn.3643592","url":null,"abstract":"Agribot is a robot and mainly designed for agricultural purposes. The agribot can be able to do all forming techniques.It is an autonomous proto type robot that will help farmers in the farmland.This is an arduino controlled robot that will be able to plough, sow and water the farmland. And it can be water the farmland with the help of the moisture sensor through identifies how much amount water required to the soil.It can be do the farming techniques with the help of the supply of single switch. The robot will perform farming using the analogy of ultrasonic detection in order to change its position from one farming strip to another within the few sec. This can be gives the buzzer signals to the farmers when the water level is empty in tank. The robot Thus, will contribute greatly in developing the farming strategies and reduce farmers cost of cultivation and will also increase their profit margins.","PeriodicalId":404477,"journal":{"name":"Mechanical Engineering eJournal","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132135637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation of Flow Parameters and Magnetic Forces in Machining of Internal Cam through Viscoelastic Magnetic Abrasive Flow Finishing Process","authors":"K. Srinivas, Q. Murtaza, A. Aggarwal","doi":"10.2139/ssrn.3569445","DOIUrl":"https://doi.org/10.2139/ssrn.3569445","url":null,"abstract":"This paper consists of the simulation of flow parameters for an internal cam finished through viscoelastic magnetic abrasive finishing process. Viscoelastic medium comprises Silicone oil and other additives which exhibit viscoelastic properties throughout the finishing process as the change in the temperature does not affect the viscosity of the medium. Carbonyl particles along with Silicon carbide of grit size 800 are thoroughly mixed with the medium prepared. Due to the cohesiveness in the viscoelastic medium, it does not stick to the surface of the internal cam. As the Viscoelastic magnetic abrasive medium flows through the internal cam surface, it is subjected to Magnetic forces generated by arc magnets made of NdFe 35 material, kept at 90o phase difference. Due to this Carbonyl particles, which are Ferro magnetic in nature re orient themselves and the abrasive particles would be thrown to the internal surface of the cam. As the abrasives slide tangentially to the internal surface of the cam and the forces involved during the flow are much higher, the machining of the surface takes place. In the present paper simulation has been made for finding the flow parameters using Ansys 16 and Magnetic field distribution is found using Ansys Maxwell simulation software. The result so obtains claims that the process is suitable for machining the internal cams and the complex shaped object.","PeriodicalId":404477,"journal":{"name":"Mechanical Engineering eJournal","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124337639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Siting and Sizing of Grid-Connected Microgrid System for an Educational Institution Using HOMER Software","authors":"Robert Bhadra Naorem, Parul Singh, T. Singh","doi":"10.2139/ssrn.3517384","DOIUrl":"https://doi.org/10.2139/ssrn.3517384","url":null,"abstract":"This paper analyses the sizing of PV system installation in a selected location for optimal operation using Hybrid Optimization Model for Electric Renewable, HOMER software. The main objective is to reduce the total net present cost (NPC) and the size of the system. A grid-connected mode of operation is considered in which the main grid is used as a backup power system. The hourly real-time electric load demands, monthly temperature, and solar irradiance data are collected and used as input for the analysis. A centralized and distributed type of load demand is compared and found the optimal solution which gives the optimal size and costs of the system.","PeriodicalId":404477,"journal":{"name":"Mechanical Engineering eJournal","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132910552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}