Search results for: machine readable format

Authors: Elias Nemer, Greg Vines

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Time-frequency transformation and spectral representations of audio signals are commonly used in various machine learning applications. Training networks on frequency features such as the Mel-Spectrogram or Cochleogram have been proven more effective and convenient than training on-time samples. In practical realizations, these features are created on a different processor and/or pre-computed and stored on disk, requiring additional efforts and making it difficult to experiment with different features. In this paper, we provide a PyTorch framework for creating various spectral features as well as time-frequency transformation and time-domain filter-banks using the built-in trainable conv1d() layer. This allows computing these features on the fly as part of a larger network and enabling easier experimentation with various combinations and parameters. Our work extends the work in the literature developed for that end: First, by adding more of these features and also by allowing the possibility of either starting from initialized kernels or training them from random values. The code is written as a template of classes and scripts that users may integrate into their own PyTorch classes or simply use as is and add more layers for various applications.

Keywords: neural networks Mel-Spectrogram, chromagram, cochleogram, discrete Fourrier transform, PyTorch conv1d()

Procedia PDF Downloads 214

Authors: Aofan Liu, Qianqian Tan, Burra Venkata Durga Kumar

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Data security is of the utmost importance in the healthcare area, as sensitive patient information is constantly sent around and analyzed by many different parties. The use of federated learning, which enables data to be evaluated locally on devices rather than being transferred to a central server, has emerged as a potential solution for protecting the privacy of user information. To protect against data breaches and unauthorized access, federated learning alone might not be adequate. In this context, the application of blockchain technology could provide the system extra protection. This study proposes a distributed federated learning system that is built on blockchain technology in order to enhance security in healthcare. This makes it possible for a wide variety of healthcare providers to work together on data analysis without raising concerns about the confidentiality of the data. The technical aspects of the system, including as the design and implementation of distributed learning algorithms, consensus mechanisms, and smart contracts, are also investigated as part of this process. The technique that was offered is a workable alternative that addresses concerns about the safety of healthcare while also fostering collaborative research and the interchange of data.

Keywords: data privacy, distributed system, federated learning, machine learning

Procedia PDF Downloads 95

Authors: Jamal A. Hassan, Ali Q. Abdulrazzaq, Sadiq J. Abass

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In this study, both the photoelastic, as well as the finite element methods, are used to study the stress distribution within human vertebra (L4) under forces similar to those that occur during normal life. Two & three dimensional models of vertebra were created by the software AutoCAD. The coordinates obtained were fed into a computer numerical control (CNC) tensile machine to fabricate the models from photoelastic sheets. Completed models were placed in a transmission polariscope and loaded with static force (up to 1500N). Stresses can be quantified and localized by counting the number of fringes. In both methods the Principle stresses were calculated at different regions. The results noticed that the maximum von-mises stress on the area of the extreme superior vertebral body surface and the facet surface with high normal stress (σ) and shear stress (τ). The facets and other posterior elements have a load-bearing function to help support the weight of the upper body and anything that it carries, and are also acted upon by spinal muscle forces. The numerical FE results have been compared with the experimental method using photoelasticity which shows good agreement between experimental and simulation results.

Keywords: photoelasticity, stress, load, finite element

Procedia PDF Downloads 269

Authors: Thota Sai Prakash, B. Yaswanth, Jhade Bhuvaneswar, Marreddy Divakar Reddy, Shyam Ji Gupta

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Across the globe, there are a lot of chronic diseases, and heart disease stands out as one of the most perilous. Sadly, many lives are lost to this condition, even though early intervention could prevent such tragedies. However, identifying heart disease in its initial stages is not easy. To address this challenge, we propose an automated system aimed at predicting the presence of heart disease using advanced techniques. By doing so, we hope to empower individuals with the knowledge needed to take proactive measures against this potentially fatal illness. Our approach towards this problem involves meticulous data preprocessing and the development of predictive models utilizing classification algorithms such as Support Vector Machines (SVM), Decision Tree, and Random Forest. We assess the efficiency of every model based on metrics like accuracy, ensuring that we select the most reliable option. Additionally, we conduct thorough data analysis to reveal the importance of different attributes. Among the models considered, Random Forest emerges as the standout performer with an accuracy rate of 96.04% in our study.

Keywords: support vector machines, decision tree, random forest

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Authors: Ke Ren, Huiruo Shi, Linsen Li, Baoshuai Wang, Yu Zhou

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Since some discriminative features are required for ground moving targets classification, we propose a new feature extraction method based on micro-Doppler signature. Firstly, the time-frequency analysis of measured data indicates that the time-frequency spectrograms of the three kinds of ground moving targets, i.e., single walking person, two people walking and a moving wheeled vehicle, are discriminative. Then, a three-dimensional time-frequency feature vector is extracted from the time-frequency spectrograms to depict these differences. At last, a Support Vector Machine (SVM) classifier is trained with the proposed three-dimensional feature vector. The classification accuracy to categorize ground moving targets into the three kinds of the measured data is found to be over 96%, which demonstrates the good discriminative ability of the proposed micro-Doppler feature.

Keywords: micro-doppler, time-frequency analysis, feature extraction, radar target classification

Procedia PDF Downloads 390

Authors: Pablo Oteiza, Ricardo Alvarez, Mehrdad Pirnia, Fuat Can

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To effectively combat climate change, many countries around the world have committed to a decarbonisation of their electricity, along with promoting a large-scale integration of renewable energy sources (RES). While this trend represents a unique opportunity to effectively combat climate change, achieving a sound and cost-efficient energy transition towards low-carbon power systems poses significant challenges for the multi-year Transmission Network Expansion Planning (TNEP) problem. The objective of the multi-year TNEP is to determine the necessary network infrastructure to supply the projected demand in a cost-efficient way, considering the evolution of the new generation mix, including the integration of RES. The rapid integration of large-scale RES increases the variability and uncertainty in the power system operation, which in turn increases short-term flexibility requirements. To meet these requirements, flexible generating technologies such as energy storage systems must be considered within the TNEP as well, along with proper models for capturing the operational challenges of future power systems. As a consequence, TNEP formulations are becoming more complex and difficult to solve, especially for its application in realistic-sized power system models. To meet these challenges, there is an increasing need for developing efficient algorithms capable of solving the TNEP problem with reasonable computational time and resources. In this regard, a promising research area is the use of artificial intelligence (AI) techniques for solving large-scale mixed-integer optimization problems, such as the TNEP. In particular, the use of AI along with mathematical optimization strategies based on decomposition has shown great potential. In this context, this paper presents an efficient algorithm for solving the multi-year TNEP problem. The algorithm combines AI techniques with Column Generation, a traditional decomposition-based mathematical optimization method. One of the challenges of using Column Generation for solving the TNEP problem is that the subproblems are of mixed-integer nature, and therefore solving them requires significant amounts of time and resources. Hence, in this proposal we solve a linearly relaxed version of the subproblems, and trained a binary classifier that determines the value of the binary variables, based on the results obtained from the linearized version. A key feature of the proposal is that we integrate the binary classifier into the optimization algorithm in such a way that the optimality of the solution can be guaranteed. The results of a study case based on the HRP 38-bus test system shows that the binary classifier has an accuracy above 97% for estimating the value of the binary variables. Since the linearly relaxed version of the subproblems can be solved with significantly less time than the integer programming counterpart, the integration of the binary classifier into the Column Generation algorithm allowed us to reduce the computational time required for solving the problem by 50%. The final version of this paper will contain a detailed description of the proposed algorithm, the AI-based binary classifier technique and its integration into the CG algorithm. To demonstrate the capabilities of the proposal, we evaluate the algorithm in case studies with different scenarios, as well as in other power system models.

Keywords: integer optimization, machine learning, mathematical decomposition, transmission planning

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Authors: Daniele Baldacci, Remo Pareschi

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Augmented Reality and Augmented Intelligence are radically changing information technology. The path that starts from the keyboard and then, passing through milestones such as Siri, Alexa and other vocal avatars, reaches a more fluid and natural communication with computers, thus converting the dichotomy between man and machine into a harmonious interaction, now heads unequivocally towards a new IT paradigm, where holographic computing will play a key role. The OLOS® platform contributes substantially to this trend in that it infuses computers with human features, by transferring the gestures and expressions of persons of flesh and bones to anthropomorphic holographic interfaces which in turn will use them to interact with real-life humans. In fact, we could say, boldly but with a solid technological background to back the statement, that OLOS® gives reality to an altogether new entity, placed at the exact boundary between nature and technology, namely the holographic human being. Holographic humans qualify as the perfect carriers for the virtual reincarnation of characters handed down from history and tradition. Thus, they provide for an innovative and highly immersive way of experiencing our cultural heritage as something alive and pulsating in the present.

Keywords: digital cinematography, human-computer interfaces, holographic simulation, interactive museum exhibits

Procedia PDF Downloads 103

Authors: Ghaemi Reza, Abdollahi Hamid, Dashti Elham

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Cloud computing poses a diversity of challenges in data mining operation arising out of the dynamic structure of data distribution as against the use of typical database scenarios in conventional architecture. Due to immense number of users seeking data on daily basis, there is a serious security concerns to cloud providers as well as data providers who put their data on the cloud computing environment. Big data analytics use compute intensive data mining algorithms (Hidden markov, MapReduce parallel programming, Mahot Project, Hadoop distributed file system, K-Means and KMediod, Apriori) that require efficient high performance processors to produce timely results. Data mining algorithms to solve or optimize the model parameters. The challenges that operation has to encounter is the successful transactions to be established with the existing virtual machine environment and the databases to be kept under the control. Several factors have led to the distributed data mining from normal or centralized mining. The approach is as a SaaS which uses multi-agent systems for implementing the different tasks of system. There are still some problems of data mining based on cloud computing, including design and selection of data mining algorithms.

Keywords: cloud computing, data mining, computing models, cloud services

Procedia PDF Downloads 458

Authors: Kaan Karaoglu, Raif Bayir

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In this study, a cartesian robot is developed to improve the performance and efficiency of wireless charging of electric vehicles. The cartesian robot has three axes, each of which moves linearly. Magnetic positioning is used to align the cartesian robot transmitter charging pad. There are two different wireless charging methods, static and dynamic, for charging electric vehicles. The current state of charge information (SOC State of Charge) and location information are received wirelessly from the electric vehicle. Based on this information, the power to be transmitted is determined, and the transmitter and receiver charging pads are aligned for maximum efficiency. With this study, a fully automated cartesian robot structure will be used to charge electric vehicles with the highest possible efficiency. With the wireless communication established between the electric vehicle and the charging station, the charging status will be monitored in real-time. The cartesian robot developed in this study is a fully automatic system that can be easily used in static wireless charging systems with vehicle-machine communication.

Keywords: electric vehicle, wireless charging systems, energy efficiency, cartesian robot, location detection, trajectory planning

Procedia PDF Downloads 51

Authors: Aravindhan Nepolean, Chidamabaranathan Bibin, Rajesh K., Gopinath S., Ashok Kumar R., Arun Kumar S., Sadasivan N.

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Wind turbine blades are an important parameter for surging renewable energy production. Optimizing blade profiles and developing new materials for wind turbine blades take a lot of time and effort. Even though many standards for wind turbine blades have been developed for large-scale applications, they are not more effective in small-scale applications. We used acrylonitrile-butadiene-styrene to make small-scale wind turbine blades in this study (ABS). We chose the material because it is inexpensive and easy to machine into the desired form. They also have outstanding chemical, stress, and creep resistance. The blade measures 332 mm in length and has a 664 mm rotor diameter. A modal study of blades is carried out, as well as a comparison with current e-glass fiber. They were able to balance the output with less vibration, according to the findings. Q blade software is used to simulate rotating output. The modal analysis testing and prototype validation of wind turbine blades were used for experimental validation.

Keywords: acrylonitrile-butadiene-styrene, e-glass fiber, modal, renewable energy, q-blade

Procedia PDF Downloads 141

Authors: Taisuke Sakaki, Toshihiko Shimokawa, Nobuhiro Ushimi, Koji Murakami, Yong-Kwun Lee, Kazuhiro Tsuruta, Kanta Aoki, Kaoru Fujiie, Ryuji Katamoto, Atsushi Sugyo

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Recently attention has been focused on incomplete spinal cord injuries (SCI) to the central spine caused by pressure on parts of the white matter conduction pathway, such as the pyramidal tract. In this paper, we focus on a training robot designed to assist with primary walking-pattern training. The target patient for this training robot is relearning the basic functions of the usual walking pattern; it is meant especially for those with incomplete-type SCI to the central spine, who are capable of standing by themselves but not of performing walking motions. From the perspective of human engineering, we monitored the operator’s actions to the robot and investigated the movement of joints of the lower extremities, the circumference of the lower extremities, and exercise intensity with the machine. The concept of the device was to provide mild training without any sudden changes in heart rate or blood pressure, which will be particularly useful for the elderly and disabled. The mechanism of the robot is modified to be simple and lightweight with the expectation that it will be used at home.

Keywords: training, rehabilitation, SCI patient, welfare, robot

Procedia PDF Downloads 409

Authors: Arya Prakash Padhi, Anupam Chakrabarti, Rajib Chowdhury

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Topology or layout optimization often gives better performing economic structures and is very helpful in the conceptual design phase. But traditionally it is being done in finite element-based optimization schemes which, although gives a good result, is very time-consuming especially in 3D structures. Among other alternatives machine learning, especially deep learning-based methods, have a very good potential in resolving this computational issue. Here convolutional neural network (3D-CNN) based variational auto encoder (VAE) is trained using a dataset generated from commercially available topology optimization code ABAQUS Tosca using solid isotropic material with penalization (SIMP) method for compliance minimization. The encoded data in latent space is then fed to a 3D generative adversarial network (3D-GAN) to generate the outcome in 64x64x64 size. Here the network consists of 3D volumetric CNN with rectified linear unit (ReLU) activation in between and sigmoid activation in the end. The proposed network is seen to provide almost optimal results with significantly reduced computational time, as there is no iteration involved.

Keywords: 3D generative adversarial network, deep learning, structural topology optimization, variational auto encoder

Procedia PDF Downloads 156

Authors: Ming-Ta Yang, Jyh-Cherng Gu, Chun-Wei Huang, Jin-Lung Guan

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Time base maintenance (TBM) is conventionally applied by the power utilities to maintain circuit breakers (CBs), transformers, bus bars and cables, which may result in under maintenance or over maintenance. As information and communication technology (ICT) industry develops, the maintenance policies of many power utilities have gradually changed from TBM to condition base maintenance (CBM) to improve system operating efficiency, operation cost and power supply reliability. This paper discusses the feasibility of using intelligent electronic devices (IEDs) to construct a CB CBM management platform. CBs in power substations can be monitored using IEDs with additional logic configuration and wire connections. The CB monitoring data can be sent through intranet to a control center and be analyzed and integrated by the Elipse Power Studio software. Finally, a human-machine interface (HMI) of supervisory control and data acquisition (SCADA) system can be designed to construct a CBM management platform to provide maintenance decision information for the maintenance personnel, management personnel and CB manufacturers.

Keywords: circuit breaker, condition base maintenance, intelligent electronic device, time base maintenance, SCADA

Procedia PDF Downloads 312

Authors: Madhu Babu Cherukuri, Tamoghna Ghosh

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Factory supply chain management spends billions of dollars every year to procure and manage equipment spare parts. Due to technology -and processes changes some of these spares become obsolete/dead inventory. Factories have huge dead inventory worth millions of dollars accumulating over time. This is due to lack of a scientific methodology to identify them and send the inventory back to the suppliers on a timely basis. The standard approach followed across industries to deal with this is: if a part is not used for a set pre-defined period of time it is declared dead. This leads to accumulation of dead parts over time and these parts cannot be sold back to the suppliers as it is too late as per contract agreement. Our main idea is the time period for identifying a part as dead cannot be a fixed pre-defined duration across all parts. Rather, it should depend on various properties of the part like historical consumption pattern, type of part, how many machines it is being used in, whether it- is a preventive maintenance part etc. We have designed a predictive algorithm which predicts part obsolescence well in advance with reasonable accuracy and which can help save millions.

Keywords: obsolete inventory, machine learning, big data, supply chain analytics, dead inventory

Procedia PDF Downloads 306

Authors: Youji Miyake

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In aircraft assembly, a large number of preparatory holes are required for screw and rivet joints. Currently, many holes are drilled manually because it is difficult to machine the holes using conventional computerized numerical control(CNC) machines. The application of industrial robots to drill the hole has been considered as an alternative to the CNC machines. However, the rigidity of robot arms is so low that vibration is likely to occur during drilling. In this study, it is proposed constant-load feed machining as a method to perform high-precision drilling while minimizing the thrust force, which is considered to be the cause of vibration. In this method, the drill feed is realized by a constant load applied onto the tool so that the thrust force is theoretically kept below the applied load. The performance of the proposed method was experimentally examined through the deep hole drilling of plastic and simultaneous drilling of metal/plastic stack plates. It was confirmed that the deep hole drilling and simultaneous drilling could be performed without generating vibration by controlling the tool feed rate in the appropriate range.

Keywords: constant load feed machining, robotic drilling, deep hole, simultaneous drilling

Procedia PDF Downloads 176

Authors: Kripesh Adhikari, Hamid Bouchachia, Hammadi Nait-Charif

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Falls are one of the major causes of injury and death among elderly people aged 65 and above. A support system to identify such kind of abnormal activities have become extremely important with the increase in ageing population. Pose estimation is a challenging task and to add more to this, it is even more challenging when pose estimations are performed on challenging poses that may occur during fall. Location of the body provides a clue where the person is at the time of fall. This paper presents a vision-based tracking strategy where available joints are grouped into three different feature points depending upon the section they are located in the body. The three feature points derived from different joints combinations represents the upper region or head region, mid-region or torso and lower region or leg region. Tracking is always challenging when a motion is involved. Hence the idea is to locate the regions in the body in every frame and consider it as the tracking strategy. Grouping these joints can be beneficial to achieve a stable region for tracking. The location of the body parts provides a crucial information to distinguish normal activities from falls.

Keywords: fall detection, machine learning, deep learning, pose estimation, tracking

Procedia PDF Downloads 172

Authors: Hongyu Chen, Li Jiang

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Ubiquitous anomalies endanger the security of our system con- stantly. They may bring irreversible damages to the system and cause leakage of privacy. Thus, it is of vital importance to promptly detect these anomalies. Traditional supervised methods such as Decision Trees and Support Vector Machine (SVM) are used to classify normality and abnormality. However, in some case, the abnormal status are largely rarer than normal status, which leads to decision bias of these methods. Generative adversarial network (GAN) has been proposed to handle the case. With its strong generative ability, it only needs to learn the distribution of normal status, and identify the abnormal status through the gap between it and the learned distribution. Nevertheless, existing GAN-based models are not suitable to process data with discrete values, leading to immense degradation of detection performance. To cope with the discrete features, in this paper, we propose an efficient GAN-based model with specifically-designed loss function. Experiment results show that our model outperforms state-of-the-art models on discrete dataset and remarkably reduce the overhead.

Keywords: GAN, discrete feature, Wasserstein distance, multiple intermediate layers

Procedia PDF Downloads 110

Authors: Henni Sid Ahmed, Belbachir Mohamed Faouzi, Jean Caelen

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Behavior recognition has been studied for realizing drivers assisting system and automated navigation and is an important studied field in the intelligent Building. In this paper, a recognition method of behavior recognition separated from a real image was studied. Images were divided into several categories according to the actual weather, distance and angle of view etc. SIFT was firstly used to detect key points and describe them because the SIFT (Scale Invariant Feature Transform) features were invariant to image scale and rotation and were robust to changes in the viewpoint and illumination. My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use SIFT represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for classification of images in order to classify people’s behaviors in the intelligent building in order to give maximum comfort with optimized energy consumption.

Keywords: video analysis, people behavior, intelligent building, classification

Procedia PDF Downloads 363

Authors: H. Özkan Gülsoy, Antonyraj Arockiasamy

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The application of powder injection molding technology for the fabrication of metallic and non-metallic components is of growing interest as the process considerably saves time and cost. Utilizing this fabrication method, full dense components are being prepared in various sizes. In this work, our effort is focused to study the densification behavior of the parts made using different size 316L stainless steel powders. The metal powders were admixed with an adequate amount of polymeric compounds and molded as standard tensile bars. Solvent and thermal debinding was carried out followed by sintering in ultra pure hydrogen atmosphere based on the differential scanning calorimetry (DSC) cycle. Mechanical property evaluation and microstructural characterization of the sintered specimens was performed using universal Instron tensile testing machine, Vicker’s microhardness tester, optical (OM) and scanning electron microscope (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction were used. The results are compared and analyzed to predict the strength and weakness of the test conditions.

Keywords: powder injection molding, sintering, particle size, stainless steels

Procedia PDF Downloads 346

Authors: Lule Basha, Eralda Gjika

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The exchange rate is one of the most important economic variables, especially for a small, open economy such as Albania. Its effect is noticeable in one country's competitiveness, trade and current account, inflation, wages, domestic economic activity, and bank stability. This study investigates the fluctuation of Albania’s exchange rates using monthly average foreign currency, Euro (Eur) to Albanian Lek (ALL) exchange rate with a time span from January 2008 to June 2021, and the macroeconomic factors that have a significant effect on the exchange rate. Initially, the Random Forest Regression algorithm is constructed to understand the impact of economic variables on the behavior of monthly average foreign currencies exchange rates. Then the forecast of macro-economic indicators for 12 months was performed using time series models. The predicted values received are placed in the random forest model in order to obtain the average monthly forecast of the Euro to Albanian Lek (ALL) exchange rate for the period July 2021 to June 2022.

Keywords: exchange rate, random forest, time series, machine learning, prediction

Procedia PDF Downloads 87

Authors: M. Akhlaque Ahmed, Maliha Afshan Siddiqui

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A study has been carried out to see the prospect of wind power potential of southern Sindh namely Karachi, Hawksbay, Norriabad, Hyderabad, Ketibander and Shahbander using local wind speed data. The monthly average wind speed for these area ranges from 4.5m/sec to 8.5m/sec at 30m height from ground. Extractable wind power, wind energy and Weibul parameter for above mentioned areas have been examined. Furthermore, the power output using fast and slow wind machine using different blade diameter along with the 4Kw and 20 Kw aero-generator were examined to see the possible use for deep well pumping and electricity supply to remote villages. The analysis reveals that in this wind corridor of southern Sindh Hawksbay, Ketibander and Shahbander belongs to wind power class-3 Hyderabad and Nooriabad belongs to wind power class-5 and Karachi belongs to wind power class-2. The result shows that the that higher wind speed values occur between June till August. It was found that considering maximum wind speed location, Hawksbay,Noriabad are the best location for setting up wind machines for power generation.

Keywords: wind energy generation, Southern Sindh, seasonal change, Weibull parameter, wind machines

Procedia PDF Downloads 134

Authors: Daniel James Pitchforth, Timothy James Rogers, Ulf Tyge Tygesen, Elizabeth Jane Cross

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Wave loading is a primary cause of fatigue within offshore structures and its quantification presents a challenging and important subtask within the SHM framework. The accurate representation of physics in such environments is difficult, however, driving the development of data-driven techniques in recent years. Within many industrial applications, empirical laws remain the preferred method of wave loading prediction due to their low computational cost and ease of implementation. This paper aims to develop an approach that combines data-driven Gaussian process models with physical empirical solutions for wave loading, including Morison’s Equation. The aim here is to incorporate physics directly into the covariance function (kernel) of the Gaussian process, enforcing derived behaviors whilst still allowing enough flexibility to account for phenomena such as vortex shedding, which may not be represented within the empirical laws. The combined approach has a number of advantages, including improved performance over either component used independently and interpretable hyperparameters.

Keywords: offshore structures, Gaussian processes, Physics informed machine learning, Kernel design

Procedia PDF Downloads 174

Authors: Eid Alharbi, Yaser Miaji

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The quality level for fresh fruits is very important for the fruit industries. In presents study, an automatic online sorting system according to the internal disorder for fresh apple fruit has developed by using near infrared (NIR) spectroscopic technology. The automatic conveyer belts system along with sorting mechanism was constructed. To check the internal quality of the apple fruit, apple was exposed to the NIR radiations in the range 650-1300nm and the data were collected in form of absorption spectra. The collected data were compared to the reference (data of known sample) analyzed and an electronic signal was pass to the sorting system. The sorting system was separate the apple fruit samples according to electronic signal passed to the system. It is found that absorption of NIR radiation in the range 930-950nm was higher in the internally defected samples as compared to healthy samples. On the base of this high absorption of NIR radiation in 930-950nm region the online sorting system was constructed.

Keywords: mechatronics, NIR, fruit quality, spectroscopic technology, mechatronic design

Procedia PDF Downloads 378

Authors: Denis V. Shutin, Alexander Yu. Babin, Leonid A. Savin

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One of the ways of reducing vibroactivity of rotor systems is to apply active hybrid bearings. Their design allows correction of the rotor’s location by means of separately controlling the supply pressure of the lubricant into the friction area. In a most simple case, the control system is based on a P-regulator. Increase of the gain coefficient allows decreasing the amplitude of rotor’s vibrations. The same effect can be achieved by means of increasing the pressure in the collector of a traditional passive hybrid bearing. However, these approaches affect the dynamic characteristics of the bearing differently. Theoretical studies show that the increase of the gain coefficient of an active bearing increases the stiffness of the bearing, as well as the increase of the pressure in the collector. Nevertheless, in case of a passive bearing, the damping properties deteriorate, whereas the active hybrid bearings obtain higher damping properties, which allow effectively providing the energy dissipation of the rotor vibrations and reducing the load on the constructional elements of a machine.

Keywords: active bearings, control system, damping, hybrid bearings, stiffness

Procedia PDF Downloads 363

Authors: Wen-Chung Wang, Xue-Lan Qiu

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Ipsative tests have been widely used in vocational and career counseling (e.g., the Jackson Vocational Interest Survey). Pairwise-comparison items are a typical item format of ipsative tests. When the two statements in a pairwise-comparison item measure two different constructs, the item is referred to as a multidimensional pairwise-comparison (MPC) item. A typical MPC item would be: Which activity do you prefer? (A) playing with young children, or (B) working with tools and machines. These two statements aim at the constructs of social interest and investigative interest, respectively. Recently, new item response theory (IRT) models for ipsative tests with MPC items have been developed. Among them, the Rasch ipsative model (RIM) deserves special attention because it has good measurement properties, in which the log-odds of preferring statement A to statement B are defined as a competition between two parts: the sum of a person’s latent trait to which statement A is measuring and statement A’s utility, and the sum of a person’s latent trait to which statement B is measuring and statement B’s utility. The RIM has been extended to polytomous responses, such as preferring statement A strongly, preferring statement A, preferring statement B, and preferring statement B strongly. To promote the new initiatives, in this study we developed computerized adaptive testing algorithms for MFC items and evaluated their performance using simulations and two real tests. Both the RIM and its polytomous extension are multidimensional, which calls for multidimensional computerized adaptive testing (MCAT). A particular issue in MCAT for MPC items is the within-person statement exposure (WPSE); that is, a respondent may keep seeing the same statement (e.g., my life is empty) for many times, which is certainly annoying. In this study, we implemented two methods to control the WPSE rate. In the first control method, items would be frozen when their statements had been administered more than a prespecified times. In the second control method, a random component was added to control the contribution of the information at different stages of MCAT. The second control method was found to outperform the first control method in our simulation studies. In addition, we investigated four item selection methods: (a) random selection (as a baseline), (b) maximum Fisher information method without WPSE control, (c) maximum Fisher information method with the first control method, and (d) maximum Fisher information method with the second control method. These four methods were applied to two real tests: one was a work survey with dichotomous MPC items and the other is a career interests survey with polytomous MPC items. There were three dependent variables: the bias and root mean square error across person measures, and measurement efficiency which was defined as the number of items needed to achieve the same degree of test reliability. Both applications indicated that the proposed MCAT algorithms were successful and there was no loss in measurement proficiency when the control methods were implemented, and among the four methods, the last method performed the best.

Keywords: computerized adaptive testing, ipsative tests, item response theory, pairwise comparison

Procedia PDF Downloads 237

Authors: M. Veeresham

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The microstructure, texture, phase stability, and tensile properties of annealed Ta_0.5Nb_0.5Hf_0.5ZrTi_1.5 alloy have been investigated in the present research. The alloy was severely hybrid-rolled up to 93.5% thickness reduction, subsequently rolled samples subjected to an annealing treatment at 800 °C and 1000 °C temperatures for 1 h. Consequently, the rolled condition and both annealed temperatures have a body-centered cubic (BCC) structure. Furthermore, quantitative texture measurements (orientation distribution function (ODF) analysis) and microstructural examinations (analytical electron backscatter diffraction (EBSD) maps) permitted to establish a good relationship between annealing texture and microstructure and universal testing machine (UTM) utilized for obtaining the mechanical properties. Impressive room temperature tensile properties combination with the tensile strength (1380 MPa) and (24.7%) elongation is achieved for the 800 °C heat-treated condition. The evolution of the coarse microstructure featured in the case of 1000 °C annealed temperature ascribed to the influence of high thermal energy.

Keywords: refractory high entropy alloys, hybrid-rolling, recrystallization, microstructure, tensile properties

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Authors: E. O. Egieyeh, N. Butler, R. Coetzee, M. Van Huyssteen, A. Bheekie

Abstract:

Background: High rate of maternal and child mortality is a global concern. Nationally, it constitutes one of South Africa’s quadruple burdens of diseases. Pharmacists have a crucial role in maternal and child health care delivery and as such should be equipped with adequate knowledge and skill required to contribute to maternal and child well-being. The International Pharmaceutical Federation statement of policy (2013) outlines pharmacist-led interventions in accordance with the World Health Organisation’s interventions in maternal, new-born and child health care. The South African Pharmacy Council’s guideline on Good Pharmacy Practice (2010) also stipulates the minimum standards required to participate in reproductive, maternal and child care. Pharmacy schools are obliged to train pharmacy students to meet priority health needs of the population so that graduates are ‘fit for purpose’. The purpose of the study is to evaluate the knowledge and skill of final year pharmacy students at a university in South Africa to determine their preparedness to contribute effectively to maternal and child health care. Method: A quantitative, descriptive, non-randomized baseline study was conducted among the final year students at the School of Pharmacy. Data was collected using a questionnaire designed in sections to assess knowledge of contraception, maternal and child health directed at the primary care level and framed within the scope of practice required of an entry-level generalist pharmacist. Participants’ skill in infant growth assessment was assessed in a section of the questionnaire in a written format. Participants ticked the topics they had been exposed to on a curriculum content assessment tool which was not graded. A pilot study examined the clarity and suitability of question items, and duration to complete the questionnaire. A score of 50% in each section of the questionnaire indicated a pass. The questionnaire was delivered in campus lecture venue. Results: Of the 102 students in final year, 53 (52%) students consented to participate in the study. Only 13.2% of participants scored above 50% in each section. Forty five (85%) participants scored above 50% in the contraception section while 40 (75%) scored less than 50% in the skills assessment. Less than half (45.3%) of the participants had a total score above 50%. Being a parent or working part-time as pharmacist assistance did not have any influence on the performance of the participants. Evaluation of participants’ curriculum content exposure showed differences in exposure to the various topics. Exposure to contraception teaching received the most recognition. Conclusion: Maternal and child health curriculum content should be reviewed at the university to enhance the knowledge and skill of pharmacy graduates.

Keywords: final year pharmacy students, knowledge and skill, maternal and child health, South Africa

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Authors: Hahido Samaras

Abstract:

In many cases, especially with the pandemic playing a major role in fast-tracking the growth of the digital industry, online learning has become a necessity or even a standard educational model nowadays, reliably overcoming barriers such as location, time and cost and frequently combined with a face-to-face format (e.g., in blended learning). This being the case, it is evident that students in many parts of the world, as well as their parents, will increasingly need to become aware of the pros and cons of online versus traditional courses. This fast-growing mode of learning, accelerated during the years of the pandemic, presents an abundance of exciting options especially matched for a large number of secondary school students in remote places of the world where access to stimulating educational settings and opportunities for a variety of learning alternatives are scarce, adding advantages such as flexibility, affordability, engagement, flow and personalization of the learning experience. However, online learning can also present several challenges, such as a lack of student motivation and social interactions in natural settings, digital literacy, and technical issues, to name a few. Therefore, educational researchers will need to conduct further studies focusing on the benefits and weaknesses of online learning vs. traditional learning, while instructional designers propose ways of enhancing student motivation and engagement in virtual environments. Similarly, teachers will be required to become more and more technology-capable, at the same time developing their knowledge about their students’ particular characteristics and needs so as to match them with the affordances the technology offers. And, of course, schools, education programs, and policymakers will have to invest in powerful tools and advanced courses for online instruction. By developing digital courses that incorporate intentional opportunities for community-building and interaction in the learning environment, as well as taking care to include built-in design principles and strategies that align learning outcomes with learning assignments, activities, and assessment practices, rewarding academic experiences can derive for all students. This paper raises various issues regarding the effectiveness of online learning on students by reviewing a large number of research studies related to the usefulness and impact of online learning following the COVID-19-induced digital education shift. It also discusses what students, teachers, decision-makers, and parents have reported about this mode of learning to date. Best practices are proposed for parties involved in the development of online learning materials, particularly for secondary school students, as there is a need for educators and developers to be increasingly concerned about the impact of virtual learning environments on student learning and wellbeing.

Keywords: blended learning, online learning, secondary schools, virtual environments

Procedia PDF Downloads 85

Authors: R. Kenova, K. Anguelov, R. Nikolova

Abstract:

The human factor plays a major role in boosting the competitive capacity of logistic enterprises. This is of particular importance when it comes to logistic companies. On the one hand they should be strictly compliant with legislation; on the other hand, they should be competitive in terms of pricing and of delivery timelines. Moreover, their policies should allow them to be as flexible as possible. All these circumstances are reason for very serious challenges for the qualification, motivation and experience of the human resources, working in logistic companies or in logistic departments of trade and industrial enterprises. The geographic place of Bulgaria puts it in position of a country with some specific competitive advantages in the goods transport from Europe to Asia and back. Along with it, there is a number of logistic companies, that operate in this sphere in Bulgaria. In the current paper, the authors aim to establish the condition of the administrative capacity and human resources in the logistic companies and logistic departments of trade and industrial companies in Bulgaria in order to propose some guidelines for improving of their effectiveness. Due to independent empirical research, conducted in Bulgarian logistic, trade and industrial enterprises, the authors investigate both the impact degree and the interdependence of various factors that characterize the administrative capacity. The study is conducted with a prepared questionnaire, in format of direct interview with the respondents. The volume of the poll is 50 respondents, representatives of: general managers of industrial or trade enterprises; logistic managers of industrial or trade enterprises; general managers of forwarding companies – either with own or with hired transport; experts from Bulgarian association of logistics; logistic lobbyist and scientists of the relevant area. The data are gathered for 3 months, then arranged by a specialized software program and analyzed by preset criteria. Based on the results of this methodological toolbox, it can be claimed that there is a correlation between the individual criteria. Also, a commitment between the administrative capacity and other factors that determine the competitiveness of the studied companies is established. In this paper, the authors present results of the empirical research that concerns the number and the workload in the logistic departments of the enterprises. Also, what is commented is the experience, related to logistic processes management and human resources competence. Moreover, the overload level of the logistic specialists is analyzed as one of the main threats for making mistakes and losing clients. The paper stands behind the thesis that there is indispensability of forming an effective and efficient administrative capacity, based on the number, qualification, experience and motivation of the staff in the logistic companies. The paper ends with recommendations about the qualification and experience of the specialists in logistic departments; providing effective and efficient administrative capacity in the logistic departments; interdependence of the human factor and the other factors that influence the enterprise competitiveness.

Keywords: administrative capacity, human resources, logistic competitiveness, staff qualification

Procedia PDF Downloads 134

Authors: Yumee Kim, Seung-Guk Hyeon, Kukjin Chun

Abstract:

An adjustable aperture using a liquid crystal is proposed for real-time range detection and obtaining images simultaneously. The adjustable aperture operates as two types of aperture stops which can create two different Depth of Field images. By analyzing these two images, the distance can be extracted from camera to object. Initially, the aperture stop has large size with zero voltage. When the input voltage is applied, the aperture stop transfer to smaller size by orientational transition of liquid crystal molecules in the device. The diameter of aperture stop is 1.94mm and 1.06mm. The proposed device has low driving voltage of 7.0V and fast response time of 6.22m. Compact size aperture of 6×6×1.1 mm3 is assembled in conventional camera which contain 1/3” HD image sensor and focal length of 3.3mm that can be used in autonomous. The measured range was up to 5m. The adjustable aperture has high stability due to no mechanically moving parts. This range sensor can be applied to the various field of 3D depth map application which is the Advanced Driving Assistance System (ADAS), drones and manufacturing machine.

Keywords: adjustable aperture, dual aperture, liquid crystal, ranging and imaging, ADAS, range sensor

Procedia PDF Downloads 367