Search results for: induction machine

Authors: Engin Eyceyurt, Josko Zec

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The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.

Keywords: drive test, LTE, machine learning, uplink throughput prediction

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Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

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Authors: Nisha Khatik, Ramesh Joshi

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An in vitro plant regeneration system was developed via direct somatic embryogenesis from different seedling explants of an important medicinal plant Murraya koenigii (L) Spreng. Cotyledons (COT), Hypocotyle (HYP)(10 to 15 mm) and Root (RT) segments (10 to 20 mm) were excised from 60 days old seedlings as explants. The somatic embryos induction was achieved on MS basal medium augmented with different concentrations of BAP 1.33 to 8.40 µM and TDZ 1.08 to 9.82 µM. The globular embryos originated from cut ends and entire surface of the root, hypocotyle explants and margins of cotyledons within 30-40days. The percentage of somatic embryos induction per explant was significantly higher in HYP explants (94.21±5.77%) in the MS basal medium supplemented with 6.20 µM BAP and 8.64 µM TDZ. The highest rate of conversion of torpedo, heart and cotyledonary stages from globular stage was obtained in MS medium supplemented with 8.64 µM TDZ. The matured somatic embryos were transferred to the MS basal medium without PGRs. Highest 88% of the matured embryos were germinated on transfer to the PGR free medium where they grew for a further 3-4 weeks. Out of seventy six hardened plants seventy (92%) plantlets were found healthy under field conditions.

Keywords: Murraya koenigii, somatic embryogenesis, thidiazuron, regeneration, rutaceae

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Authors: M. R. Phadatare, J. V. Meshram, S. H. Pawar

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Conversion of electromagnetic energy into heat by nanoparticles (NPs) has the potential to be a powerful, non-invasive technique for biomedical applications such as magnetic fluid hyperthermia, drug release, disease treatment and remote control of single cell functions, but poor conversion efficiencies have hindered practical applications so far. In this paper, an attempt has been made to increase the efficiency of magnetic, thermal induction by NPs. To increase the efficiency of magnetic, thermal induction by NPs, one can take advantage of the exchange coupling between a magnetically hard core and magnetically soft shell to tune the magnetic properties of the NP and maximize the specific absorption rate, which is the gauge of conversion efficiency. In order to examine the tunability of magnetocrystalline anisotropy and its magnetic heating power, a representative magnetically hard material (CoFe₂O₄) has been coupled to a soft material (Ni₀.₅Zn₀.₅Fe₂O₄). The synthesized NPs show specific absorption rates that are of an order of magnitude larger than the conventional one.

Keywords: magnetic nanoparticles, surface functionalization of magnetic nanoparticles, magnetic fluid hyperthermia, specific absorption rate

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Authors: Melody Yin

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Concussions impact a large amount of adolescents; they make up as much as half of the diagnosed concussions in America. This research proposes a hybrid machine learning model based on the combination of human/knowledge-based domains and computer-generated feature rankings to improve the accuracy of diagnosing sports related concussion (SRC). Using a data set of symptoms collected on the sideline post-SRC events, the symptom selection criteria method has been developed by using Google AutoML's important score function to identify the top 10 symptom features. In addition, symptom domains have been introduced as another parameter, categorizing the symptoms into physical, cognitive, sleep, and emotional domains. The hybrid machine learning model has been trained with a combination of the top 10 symptoms and 4 domains. From the results, the hybrid model was the best performer for symptom resolution time prediction in 2 and 4-week thresholds. This research is a proof of concept study in the use of domains along with machine learning in order to improve concussion prediction accuracy. It is also possible that the use of domains can make the model more efficient due to reduced training time. This research examines the use of a hybrid method in predicting sports-related concussion. This achievement is based on data preprocessing, using a hybrid method to select criteria to achieve high performance.

Keywords: hybrid model, machine learning, sports related concussion, symptom resolution time

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Authors: Sudhir Kumar Singh, Debashish Chakravarty

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Slope stability analysis is an important aspect in the field of geotechnical engineering. It is also important from safety, and economic point of view as any slope failure leads to loss of valuable lives and damage to property worth millions. This paper aims at mitigating the risk of slope failure by studying the effect of different joint sets on the stability of mine pit slopes under the influence of various external factors, namely degree of saturation, rainfall intensity, and seismic coefficients. Supervised machine learning approach has been utilized for making accurate and reliable predictions regarding the stability of slopes based on the value of Factor of Safety. Numerous cases have been studied for analyzing the stability of slopes using the popular Finite Element Method, and the data thus obtained has been used as training data for the supervised machine learning models. The input data has been trained on different supervised machine learning models, namely Random Forest, Decision Tree, Support vector Machine, and XGBoost. Distinct test data that is not present in training data has been used for measuring the performance and accuracy of different models. Although all models have performed well on the test dataset but Random Forest stands out from others due to its high accuracy of greater than 95%, thus helping us by providing a valuable tool at our disposition which is neither computationally expensive nor time consuming and in good accordance with the numerical analysis result.

Keywords: finite element method, geotechnical engineering, machine learning, slope stability

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Authors: Shah Krupa Rajendra, Amit Kumar

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Increasing use of traditional vehicles driven by internal combustion engine is responsible for the environmental pollution. Further, it leads to depletion of limited energy resources. Therefore, it is required to explore alternative energy sources for the transportation. The promising solution is to use electric vehicle. However, it suffers from limited driving range. Regenerative braking increases the range of the electric vehicle to a certain extent. In this paper, a novel methodology utilizing regenerative braking is described. The model comprising of DC machine, feedback based boost converter and micro-controller is proposed. The suggested method is very simple and reliable. The proposed model successfully shows the energy being saved into during regenerative braking process.

Keywords: boost converter, DC machine, electric vehicle, micro-controller, regenerative braking

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Authors: H. Mekki, A. Djerioui, S. Zeghlache, L. Chrifi-Alaoui

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Induction motor (IM)Induction motor (IM) are nowadays widely used in industrial applications specially in electric vehicles (EVs) and traction locomotives, due to their high efficiency high speed and lifetime. However, since EV motors are easily influenced by un-certainties parameter variations and external load disturbance, both robust control techniques have received considerable attention during the past few decades. This paper present a robust controller design based sliding mode control (SMC) and high gain flux observer (HGO) for induction motor (IM) based Electric Vehicles (EV) drives. This control technique is obtained by the combination between the field oriented and the sliding mode control strategy and present remarkable dynamic performances just as a good robustness with respect to EV drives load torque. A high gain flux observer is also presented and associated in order to design sensorless control by estimating the rotor flux only using measurements of the stator voltages and currents. Simulations results are provided to evaluate the consistency and to show the effectiveness of the proposed SMC strategy also the performance of the HGO for Electric Vehicles system are nowadays widely used in industrial applications specially in electric vehicles (EVs) and traction locomotives, due to their high efficiency high speed and lifetime. However, since EV motors are easily influenced by un-certainties parameter variations and external load disturbance, both robust control techniques have received considerable attention during the past few decades. This paper present a robust controller design based sliding mode control (SMC) and high gain flux observer (HGO) for induction motor (IM) based Electric Vehicles (EV) drives. This control technique is obtained by the combination between the field oriented and the sliding mode control strategy and present remarkable dynamic performances just as a good robustness with respect to EV drives load torque. A high gain flux observer is also presented and associated in order to design sensorless control by estimating the rotor flux only using measurements of the stator voltages and currents. Simulations results are provided to evaluate the consistency and to show the effectiveness of the proposed SMC strategy also the performance of the HGO for Electric Vehicles system.

Keywords: electric vehicles, sliding mode control, induction motor drive, high gain observer

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Authors: Dawoon Choi, Jian Li, Yunhyun Cho

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Axial Flux Permanent Magnet (AFPM) machines have been widely used in various applications due to their important merits, such as compact structure, high efficiency and high torque density. This paper presents one of the most important characteristics in the design process of the AFPM device, which is a recent issue. To design AFPM machine, the predicting electromagnetic forces between the permanent magnets and stator is important. Because of the magnitude of electromagnetic force affects many characteristics such as machine size, noise, vibration, and quality of output power. Theoretically, this force is canceled by the equilibrium of force when it is in the middle of the gap, but it is inevitable to deviate due to manufacturing problems in actual machine. Such as large scale wind generator, because of the huge attractive force between rotor and stator disks, this is more serious in getting large power applications such as large. This paper represents the characteristics of Double-Stator Inner –Rotor AFPM machines when it has rotor eccentricity. And, unbalanced air-gap and inclined air-gap condition which is caused by rotor offset and tilt in a double-stator single inner-rotor AFPM machine are each studied in electromagnetic and mechanical aspects. The output voltage and cogging torque under un-normal air-gap condition of AF machines are firstly calculated using a combined analytical and numerical methods, followed by a structure analysis to study the effect to mechanical stress, deformation and bending forces on bearings. Results and conclusions given in this paper are instructive for the successful development of AFPM machines.

Keywords: axial flux permanent magnet machine, inclined air gap, unbalanced air gap, rotor eccentricity

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Authors: Neerav Kumar, Naumaan Nayyar, Sharath Kashyap

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Most companies see an increase in the adoption of machine learning (ML) applications across internal and external-facing use cases. ML applications vend output either in batch or real-time patterns. A complete batch ML pipeline architecture comprises data sourcing, feature engineering, model training, model deployment, model output vending into a data store for downstream application. Due to unclear role expectations, we have observed that scientists specializing in building and optimizing models are investing significant efforts into building the other components of the architecture, which we do not believe is the best use of scientists’ bandwidth. We propose a system architecture created using AWS services that bring industry best practices to managing the workflow and simplifies the process of model deployment and end-to-end data integration for an ML application. This narrows down the scope of scientists’ work to model building and refinement while specialized data engineers take over the deployment, pipeline orchestration, data quality, data permission system, etc. The pipeline infrastructure is built and deployed as code (using terraform, cdk, cloudformation, etc.) which makes it easy to replicate and/or extend the architecture to other models that are used in an organization.

Keywords: data pipeline, machine learning, AWS, architecture, batch machine learning

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Authors: Amar Al-Obaidi, Verena Kräusel, Dirk Landgrebe

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Induction assisted single point incremental forming (IASPIF) is a flexible method and can be simply utilized to form a high strength alloys. Due to the interaction between the mechanical and thermal properties during IASPIF an evaluation for the process is necessary to be performed analytically. Therefore, a numerical simulation was carried out in this paper. The numerical analysis was operated at both room and elevated temperatures then compared with experimental results. Fully coupled dynamic temperature displacement explicit analysis was used to simulated the hot single point incremental forming. The numerical analysis was indicating that during hot single point incremental forming were a combination between complicated compression, tension and shear stresses. As a result, the equivalent plastic strain was increased excessively by rising both the formed part depth and the heating temperature during forming. Whereas, the forming forces were decreased from 5 kN at room temperature to 0.95 kN at elevated temperature. The simulation shows that the maximum true strain was occurred in the stretching zone which was the same as in experiment.

Keywords: induction heating, single point incremental forming, FE modeling, advanced high strength steel

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Authors: Itamir de Morais Barroca Filho, Cephas A. S. Barreto, Ramon Malaquias, Cezar Miranda Paula de Souza, Arthur Costa Gorgônio, João C. Xavier-Júnior, Mateus Firmino, Fellipe Matheus Costa Barbosa

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Information and Communication Technologies (ICT) in healthcare are crucial for efficiently delivering medical healthcare services to patients. These ICTs are also known as e-health and comprise technologies such as electronic record systems, telemedicine systems, and personalized devices for diagnosis. The focus of e-health is to improve the quality of health information, strengthen national health systems, and ensure accessible, high-quality health care for all. All the data gathered by these technologies make it possible to help clinical staff with automated decisions using machine learning. In this context, we collected patient data, such as heart rate, oxygen saturation (SpO2), blood pressure, respiration, and others. With this data, we were able to develop machine learning models for patients’ risk of death and estimate the length of stay in ICU beds. Thus, this paper presents the methodology for applying machine learning techniques to develop these models. As a result, although we implemented these models on an IoT healthcare platform, helping clinical staff in healthcare in an ICU, it is essential to create a robust clinical validation process and monitoring of the proposed models.

Keywords: ICT, e-health, machine learning, ICU, healthcare

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Authors: Jiang Niu, Yue Jiang

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With the ultimate goal to automatically generate translated texts with high quality, machine translation has made tremendous improvements. However, its translations of literary works are still plagued with problems in coherence, esp. the translation between distant language pairs. One of the causes of the problems is probably the lack of linguistic knowledge to be incorporated into the training of machine translation systems. In order to enable readers to better understand the problems of machine translation in coherence, to seek out the potential knowledge to be incorporated, and thus to improve the quality of machine translation products, this study applies Theme-Rheme structure to examine how a machine-translated literary text is organized and developed in terms of coherence. Theme-Rheme structure in Systemic Functional Linguistics is a useful tool for analysis of textual coherence. Theme is the departure point of a clause and Rheme is the rest of the clause. In a text, as Themes and Rhemes may be connected with each other in meaning, they form thematic and rhematic progressions throughout the text. Based on this structure, we can look into how a text is organized and developed in terms of coherence. Methodologically, we chose Chinese and English as the language pair to be studied. Specifically, we built a comparable corpus with two modes of English translations, viz. machine translation (MT) and human translation (HT) of one Chinese literary source text. The translated texts were annotated with Themes, Rhemes and their progressions throughout the texts. The annotated texts were analyzed from two respects, the different types of Themes functioning differently in achieving coherence, and the different types of thematic and rhematic progressions functioning differently in constructing texts. By analyzing and contrasting the two modes of translations, it is found that compared with the HT, 1) the MT features “pseudo-coherence”, with lots of ill-connected fragments of information using “and”; 2) the MT system produces a static and less interconnected text that reads like a list; these two points, in turn, lead to the less coherent organization and development of the MT than that of the HT; 3) novel to traditional and previous studies, Rhemes do contribute to textual connection and coherence though less than Themes do and thus are worthy of notice in further studies. Hence, the findings suggest that Theme-Rheme structure be applied to measuring and assessing the coherence of machine translation, to being incorporated into the training of the machine translation system, and Rheme be taken into account when studying the textual coherence of both MT and HT.

Keywords: coherence, corpus-based, literary translation, machine translation, Theme-Rheme structure

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Authors: Jose Melena, Rosa Santander, Tanya Gonzalez, Richard Duque, Juan Illanes

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Ecuador is one of the countries with the greatest aquatic biodiversity worldwide. In particular, there are at least a dozen native marine species with great aquaculture potential locally. This research concerns one of those species. It has proposed to implement experimental protocols in order to induce spawning and to generate the early larval development of the giant reef clam P. multicostata under controlled conditions. Bioassays were carried out with one adult batch (n= 8) with an average valvar length of 118,4 ± 5,8 mm, which were collected near of the Puerto Santa Rosa (2° 12' 30'' S, 80° 58' 28'' W), Santa Elena Province. During a short acclimation stage, the eight adults of giant reef clam P. multicostata were exposed to thermal stress. Briefly, the experimental protocol for spawning induction was based on the application of 20°C for 1 h and 30°C for 1 h on P. multicostata broodstock at least three consecutive times by one day. After spawning, collected sexual material was released for external fertilization process. After the delivery of gametes, it was achieved 3,25 × 10⁶ viable zygotes. As results, fertilized eggs had 56 µm diameter; while first and second cell divisions were observed to 2,5 h post-fertilization, with individual average length of 68 ± 5 µm and polar body. Latter cell divisions, including gastrula stage, appeared at 9 h post-fertilization, with individual average length of 73 ± 4 µm and trochophore stage at 15 h post-fertilization with individual average length of 75 ± 4 µm. In addition, veliger stage was registered at 20 h post-fertilization with individual average length of 82 ± 6 µm. Umboned larvae appeared at day 8 post-fertilization, with individual average length of 148 ± 6 µm. These pioneering results worldwide can strengthen the local conservation process of the overexploited P. multicostata and to encourage its production for commercial purposes.

Keywords: Ecuador, larval development, Periglypta multicostata, spawning induction

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Authors: Hatim Laalej, Jon Stammers

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In machining, monitoring of tool wear is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Currently, the task of monitoring the wear on the cutting tool is carried out by the operator who performs manual inspections of the cutting tool, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from loss of productivity. The cutting tool consumable costs may also be higher than necessary when tools are changed before the end of their useful life. Furthermore, damage can be caused to the workpiece when tools are not changed soon enough leading to a significant increase in the costs of manufacturing. The present study is concerned with the development of break sensor printed on the flank surface of poly-crystalline diamond (PCD) cutting to perform on-line condition monitoring of the cutting tool used to machine Titanium Ti-6al-4v bar. The results clearly show that there is a strong correlation between the break sensor measurements and the amount of wear in the cutting tool. These findings are significant in that they help the user/operator of the machine tool to determine the condition of the cutting tool without the need of performing manual inspection, thereby reducing the manufacturing costs such as the machine down time.

Keywords: machining, manufacturing, tool wear, signal processing

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Authors: M. S. Nikitenko, S. A. Kizilov, D. Y. Khudonogov

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The paper considers an approach to a machine vision operating system combined with using a grid of light markers. This approach is used to solve several scientific and technical problems, such as measuring the capability of an apron feeder delivering coal from a lining return port to a conveyor in the technology of mining high coal releasing to a conveyor and prototyping an autonomous vehicle obstacle detection system. Primary verification of a method of calculating bulk material volume using three-dimensional modeling and validation in laboratory conditions with relative errors calculation were carried out. A method of calculating the capability of an apron feeder based on a machine vision system and a simplifying technology of a three-dimensional modelled examined measuring area with machine vision was offered. The proposed method allows measuring the volume of rock mass moved by an apron feeder using machine vision. This approach solves the volume control issue of coal produced by a feeder while working off high coal by lava complexes with release to a conveyor with accuracy applied for practical application. The developed mathematical apparatus for measuring feeder productivity in kg/s uses only basic mathematical functions such as addition, subtraction, multiplication, and division. Thus, this fact simplifies software development, and this fact expands the variety of microcontrollers and microcomputers suitable for performing tasks of calculating feeder capability. A feature of an obstacle detection issue is to correct distortions of the laser grid, which simplifies their detection. The paper presents algorithms for video camera image processing and autonomous vehicle model control based on obstacle detection machine vision systems. A sample fragment of obstacle detection at the moment of distortion with the laser grid is demonstrated.

Keywords: machine vision, machine vision operating system, light markers, measuring capability, obstacle detection system, autonomous transport

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Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe

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The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.

Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning

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Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly

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Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.

Keywords: data science, machine learning, data analytics, artificial intelligence

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Authors: Rim Messaoudi, Nogaye-Gueye Gning, François Azelart

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Automatic text classification applies mostly natural language processing (NLP) and other AI-guided techniques to automatically classify text in a faster and more accurate manner. This paper discusses the subject of using predictive maintenance to manage incident tickets inside the sociality. It focuses on proposing a tool that treats and analyses comments and notes written by administrators after resolving an incident ticket. The goal here is to increase the quality of these comments. Additionally, this tool is based on NLP and machine learning techniques to realize the textual analytics of the extracted data. This approach was tested using real data taken from the French National Railways (SNCF) company and was given a high-quality result.

Keywords: machine learning, text classification, NLP techniques, semantic representation

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Authors: Sahriye Sonmez, Salih Ulger, Mustafa Kaplan, Mustafa Karhan

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The objective of this study was to investigate the effects of external ALAR application on nutrients contents in leaf and node in ‘on (bearing)’ and ‘off (non-bearing)’ years in Memecik olive trees. For this purpose; 2000 mg L-1 ALAR was externally applied to Memecik olive trees, and leaf and node samples from olive trees were taken during the induction, initiation and differentiation periods in ‘on’ and ‘off’ years. Nutrients contents (N, P, K, Ca, Mg, Fe, Mn, Zn and Cu) in leaf and node samples were determined. The K, Ca, Mg, Fe, Mn, Zn and Cu contents were determined by atomic absorption spectrophotometry, Nitrogen by Kjeldahl procedure, and P by a spectrophotometric method. The results showed that the N, Ca, Mg, Fe, Mn, Zn and Cu contents in ‘on’ year were higher than ‘off’ year while the K contents in ‘on’ year were lower than ‘off ‘ year, but the P content was not different. The N, Ca, Mg, Fe and Mn contents in leaf samples were higher in the node samples except for K while the P, Zn and Cu contents were not different. The N, K, Ca, Fe, Mn, Zn and Cu contents were lowest during the initiation period while the P content was highest in this period. The Mg content was not different in all period.

Keywords: bearing, differentiation period, induction period, initiation period, non bearing, olive

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Authors: Yi-Min Lan, Hung-Wen Cheng, Hsuan-Ling Chang, Jou-Ping Yu

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The study applies machine learning models to construct momentum strategies and utilizes the information coefficient as an indicator for selecting stocks with strong and weak momentum characteristics. Through this approach, the study has built investment portfolios capable of generating superior returns and conducted a thorough analysis. Compared to existing research on momentum strategies, machine learning is incorporated to capture non-linear interactions. This approach enhances the conventional stock selection process, which is often impeded by difficulties associated with timeliness, accuracy, and efficiency due to market risk factors. The study finds that implementing bidirectional momentum strategies outperforms unidirectional ones, and momentum factors with longer observation periods exhibit stronger correlations with returns. Optimizing the number of stocks in the portfolio while staying within a certain threshold leads to the highest level of excess returns. The study presents a novel framework for momentum strategies that enhances and improves the operational aspects of asset management. By introducing innovative financial technology applications to traditional investment strategies, this paper can demonstrate significant effectiveness.

Keywords: information coefficient, machine learning, momentum, portfolio, return prediction

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Authors: Hong Li, Tingxian Li, Xudong Wang, Fengliang Zhao

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Unreliable flowering of chilling-required longan (Dimocarpus longan) due to increased air-temperatures have been the common concerns in the tropical areas. Our objectives were to assess the efficiency of chemicals in longan tree flowering and bearing using Growing Degree Days (GDD). The 2-year study was contacted in the tropical Haihan Island during 2012-2013. At pruning (August) the GDD values were started to count. The KClO3 treatments were applied to the root zones under the canopies at GDD 1300ºC while KH2PO4 rates were applied to the leaves at fruit setting at GDD 3000ºC and GDD 4000ºC. The results showed that total cumulative GDD was 6050ºC for longan. The GDD-guided KClO3 applications induced significant tree budding and flowering. The GDD-guided KH2PO4 applications stimulated higher leaf photosynthesis, carbonxylation efficiency, marketable fruit yield and quality (K+ and sugar) (P<0.05). It was concluded that the GDD-based model could efficiently support longan reliable flowering and bearing.

Keywords: canopy nutrition, flowering induction, growing degree days, longan, oxidant KClO3, tree physiology

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Authors: Y. Kim, H. Lee, M. Park, B. Lee

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Planning is understood as the determination of what has to be performed, how, in which sequence, when, what resources are needed, and their cost within the organization before execution. In most construction project, it is evident that the inherent nature of planning is dynamic, and initial planning is subject to be changed due to various uncertain conditions of construction project. Planners take a continuous revision process during the course of a project and until the very end of project. However, current practice lacks reliable, systematic tool for setting variance thresholds to determine when and what corrective actions to be taken. Rather it is heavily dependent on the level of experience and knowledge of the planner. Thus, this paper introduces a machine learning approach to evaluate project control threshold reliability incorporating project-specific data and presents a method to automate the process. The results have shown that the model improves the efficiency and accuracy of the monitoring process as an early warning.

Keywords: machine learning, project control, project progress monitoring, schedule

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Authors: Adinarayana S., Sudhakar I.

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Energy conversion is one of the inevitable parts of any industries. It involves either conversion of mechanical energy in to electrical or vice versa. The later conversion of energy i.e. electrical to mechanical emphasizes the need of motor. Statistics reveals, about 8 % of industries’ annual turnover met on maintenance. Thus substantial numbers of efforts are required to minimize in incurring expenditure met towards break down maintenance. Condition monitoring is one of such techniques based on vibration widely used to recognize premature failures and paves a way to minimize cumbersome involved during breakdown of machinery. The present investigation involves a case study of squirrel cage induction motor (frequently in the electro machines) has been chosen for the conditional monitoring to predict its soundness on the basis of results of FFT analyser. Accelerometer which measures the acceleration converts in to impulses by FFT analyser generates vibration spectrum and time spectrum has been located at various positions on motor under different conditions. Results obtained from the FFT analyser are compared to that of ISO standard vibration severity charts are taken to predict the preventative condition of considered machinery. Initial inspection of motor revealed that stator faults, broken end rings in rotor, eccentricity faults and misalignment between bearings are trouble shootings areas for present investigation. From the results of the shaft frequencies, it can be perceived that there is a misalignment between the bearings at both the ends. The higher order harmonics of FTF shows the presence of cracks on the race of the bearings at both the ends which are in the incipient stage. Replacement of the bearings at both the drive end (6306) and non drive end (6206) and the alignment check between the bearings in the shaft are suggested as the constructive measures towards preventive maintenance of considered squirrel cage induction motor.

Keywords: FFT analyser, condition monitoring, vibration spectrum, time wave form

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Authors: Kairat Uteulin, Azhar Iskakova, Serik Mukhambetzhanov, Bayan Yesbolayeva, Gabit Bari, Aslan Zheksenbai, Kabyl Zhambakin, Chingis Dzhabykbayev, Vladimir Piven, Izbasar Rakhimbaiev

Abstract:

To explore the potential for in vitro rapid regeneration of Russian dandelion (Taraxacum kok-saghyz Rodin), different concentrations of 6-Benzylaminopurine (BAP), 2,4-Dichlorophenoxyacetic acid (2.4-D) and BAP combined with Indole-3-acetic acid (IAA) were evaluated for their effects on the induction of somatic embryos from leaf, seed stem and root explants. Different explants were cultured on MS medium supplemented with various concentrations (0, 0.5, 1, 1.5, 2, 2.5 and 3 mg/l) of each kind of hormone. Callus induction percentage, fresh weight, color and texture of the callus were assessed after 14 and 28 days of culture. The optimum medium for the proliferation of embryogenic calli from leaf and root explants was MS supplemented with 2.5 mg/L BAP and 0.5 mg/L 2.4-D. Concentrations of 2.5 mg/L BAP and 1.5 mg/L IAA also had a remarkable effect on root and stem explants. The best concentration to produce callus from stem explants was 0.5 mg/L BAP and 1 mg/L IAA. Results of mean comparison showed that BAP and 2.4-D were more effective on different explants than BAP and IAA. Results of the double staining method proved that somatic embryogenesis occurred in the most concentrations of BAP and 2.4-D. Under microscopic observations, the different developmental stages of the embryos (globular, heart, torpedo and cotyledonary) were revealed together in callus cells, indicating that the most tested hormone combinations were effective for somatic embryogenesis formation in this species. Seed explants formed torpedo and cotyledonary stages faster than leaf and root explants in the most combinations. Most calli from seed explants were cream colored and friable, while calli were compact and light green from leaf and root explants. Some combinations gave direct regeneration and (3 mg/L BAP and 2 mg/L IAA) in seed explants and (0.5 mg/L BAP and 2.5 mg/L IAA) in leaf explants had the highest number of shoots with average of 21 and 27 shoots per callus. The developed protocol established the production of different callus types from seed, leaf, and root explants and plant regeneration through somatic embryogenesis.

Keywords: taraxacum kok-saghyz Rodin, callus, somatic embryogenesis

Procedia PDF Downloads 349

Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu

Abstract:

This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.

Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence

Procedia PDF Downloads 57

Authors: Adi Narayana S Sudhakar. I

Abstract:

Energy conversion is one of the inevitable parts of any industries. It involves either conversion of mechanical energy in to electrical or vice versa. The later conversion of energy i.e. electrical to mechanical emphasizes the need of motor .Statistics reveals, about 8 % of industries’ annual turnover met on maintenance. Thus substantial numbers of efforts are required to minimize in incurring expenditure met towards break down maintenance. Condition monitoring is one of such techniques based on vibration widely used to recognize premature failures and paves a way to minimize cumbersome involved during breakdown of machinery. The present investigation involves a case study of squirrel cage induction motor (frequently in the electro machines) has been chosen for the conditional monitoring to predict its soundness on the basis of results of FFT analyser. Accelerometer which measures the acceleration converts in to impulses by FFT analyser generates vibration spectrum and time spectrum has been located at various positions on motor under different conditions. Results obtained from the FFT analyzer are compared to that of ISO standard vibration severity charts are taken to predict the preventative condition of considered machinery. Initial inspection of motor revealed that stator faults, broken end rings in rotor, eccentricity faults and misalignment between bearings are trouble shootings areas for present investigation. From the results of the shaft frequencies, it can be perceived that there is a misalignment between the bearings at both the ends. The higher order harmonics of FTF shows the presence of cracks on the race of the bearings at both the ends which are in the incipient stage. Replacement of the bearings at both the drive end (6306) and non-drive end (6206) and the alignment check between the bearings in the shaft are suggested as the constructive measures towards preventive maintenance of considered squirrel cage induction motor.

Keywords: FFT analyser, condition monitoring, vibration spectrum, time spectrum accelerometer

Procedia PDF Downloads 422

Authors: Bessaad Taieb, Benbouali Abderrahmen

Abstract:

Fuzzy controllers are a powerful tool for controlling complex processes. However, its robustness capacity remains moderately limited because it loses its property for large ranges of parametric variations. In this paper, the proposed control method is designed, based on a fuzzy adaptive controller used as a remedy for this problem. For increase the robustness of the vector control and to maintain the performance of the five-phase asynchronous machine despite the presence of disturbances (variation of rotor resistance, rotor inertia variations, sudden variations in the load etc.), by applying the method of behaviour model control (BMC). The results of simulation show that the fuzzy adaptive control provides best performance and has a more robustness as the fuzzy (FLC) and as a conventional (PI) controller.

Keywords: fuzzy adaptive control, behaviour model control, vector control, five-phase asynchronous machine

Procedia PDF Downloads 69

Authors: Sadixa Sanjel, Ahmed Sadek, Naseef Mansoor, Zelalem Denekew

Abstract:

Collegiate ice hockey (NCAA) sports analytics is different from the national level hockey (NHL). We apply and compare multiple machine learning models such as Linear Regression, Random Forest, and Neural Networks to predict the win ratio for a team based on their statistics. Data exploration helps determine which statistics are most useful in increasing the win ratio, which would be beneficial to coaches and team managers. We ran experiments to select the best model and chose Random Forest as the best performing. We conclude with how to bridge the gap between the college and national levels of sports analytics and the use of machine learning to enhance team performance despite not having a lot of metrics or budget for automatic tracking.

Keywords: NCAA, NHL, sports analytics, random forest, regression, neural networks, game predictions

Procedia PDF Downloads 95

Authors: Jing Zhang, Jie Chen

Abstract:

In this paper, we deal with the optimal I/O point location in an automated parking system. In this system, the S/R machine (storage and retrieve machine) travels independently in vertical and horizontal directions. Based on the characteristics of the parking system and the basic principle of AS/RS system (Automated Storage and Retrieval System), we obtain the continuous model in units of time. For the single command cycle using the randomized storage policy, we calculate the probability density function for the system travel time and thus we develop the travel time model. And we confirm that the travel time model shows a good performance by comparing with discrete case. Finally in this part, we establish the optimal model by minimizing the expected travel time model and it is shown that the optimal location of the I/O point is located at the middle of the left-hand above corner.

Keywords: parking system, optimal location, response time, S/R machine

Procedia PDF Downloads 390