Search results for: autonomous beach cleaning machine

Authors: John Peter Dacanay, Edison Ramos, Cristopher James Dicang

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Waste management is a global concern due to increasing waste production from changing consumption patterns and population growth. Proper waste disposal management is a critical aspect of public health and environmental protection. In the healthcare industry, medical waste is generated in large quantities, and if not disposed of properly, it poses a significant threat to human health and the environment. Efficient waste management conserves natural resources and prevents harm to human health, and implementing an effective waste management system can save human lives. The study aimed to assess the level of awareness and practices on waste disposal management, highlighting the understanding of proper disposal, potential hazards, and environmental implications among Medical Technology students. This would help to provide more recommendations for improving waste management practices in healthcare settings as well as for better waste management practices in educational institutions. From the collected data, a female of 21 years of age stands out among the respondents. With the frequency and percentage of medical technology students' knowledge of laboratory waste management being high, it indicates that all respondents demonstrated a solid understanding of proper disposal methods, regulations, risks, and handling procedures related to laboratory waste. That said, the findings emphasize the significance of education and awareness programs in equipping individuals involved in laboratory practices with the necessary knowledge to handle and dispose of hazardous and infectious waste properly. Most respondents demonstrate positive practices or are highly mannered in laboratory waste management, including proper segregation and disposal in designated containers. However, there are concerns about the occasional mixing of waste types, emphasizing the reiteration of proper waste segregation. Students show a strong commitment to using personal protective equipment and promptly cleaning up spills. Some students admit to improper disposal due to rushing, highlighting the importance of time management and safety prioritization. Overall, students follow protocols for hazardous waste disposal, indicating a responsible approach. The school's waste management system is perceived as adequate, but continuous assessment and improvement are necessary. Encouraging reporting of issues and concerns is crucial for ongoing improvement and risk mitigation. The analysis reveals a moderate positive relationship between the respondents' knowledge and practices regarding laboratory waste management. The statistically significant correlation with a p-value of 0.26 (p-value 0.05) suggests that individuals with higher levels of knowledge tend to exhibit better practices. These findings align with previous research emphasizing the pivotal role of knowledge in influencing individuals' behaviors and practices concerning laboratory waste management. When individuals possess a comprehensive understanding of proper procedures, regulations, and potential risks associated with laboratory waste, they are more inclined to adopt appropriate practices. Therefore, fostering knowledge through education and training is essential in promoting responsible and effective waste management in laboratory settings.

Keywords: waste disposal management, knowledge, attitude, practices

Procedia PDF Downloads 92

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 477

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 68

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 155

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 424

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 170

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 324

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 315

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 191

Authors: Yitian Ren, Liyin Shen, Tao Zhou, Xiao Li

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The past urbanization process in China has brought out series of problems, the Chinese government has then positioned small towns in essential roles for implementing the strategy 'The National New-type Urbanization Plan (2014-2020)'. As the connector and transfer station of cities and countryside, small towns are important force to narrow the gap between urban and rural area, and to achieve the mission of new-type urbanization in China. The sustainable development of small towns plays crucial role because cities are not capable enough to absorb the surplus rural population. Nevertheless, there are various types of barriers hindering the sustainable development of small towns, which led to the limited development of small towns and has presented a bottleneck in Chinese urbanization process. Therefore, this paper makes deep understanding of these barriers, thus effective actions can be taken to address them. And this paper chooses the perspective of Southwest China (refers to Sichuan province, Yunnan province, Guizhou province, Chongqing Municipality City and Tibet Autonomous Region), cause the urbanization rate in Southwest China is far behind the average urbanization level of the nation and the number of small towns accounts for a great proportion in mainland China, also the characteristics of small towns in Southwest China are distinct. This paper investigates the barriers of sustainable development of small towns which located in Southwest China by using the content analysis method, combing with the field work and interviews in sample small towns, then identified and concludes 18 barriers into four dimensions, namely, institutional barriers, economic barriers, social barriers and ecological barriers. Based on the research above, questionnaire survey and data analysis are implemented, thus the key barriers hinder the sustainable development of small towns in Southwest China are identified by using fuzzy set theory, those barriers are, lack of independent financial power, lack of construction land index, financial channels limitation, single industrial structure, topography variety and complexity, which mainly belongs to institutional barriers and economic barriers. In conclusion part, policy suggestions are come up with to improve the politic and institutional environment of small town development, also the market mechanism are supposed to be introduced to the development process of small towns, which can effectively overcome the economic barriers, promote the sustainable development of small towns, accelerate the in-situ urbanization by absorbing peasants in nearby villages, and achieve the mission of new-type urbanization in China from the perspective of people-oriented.

Keywords: barrier analysis, sustainable development, small town, Southwest China

Procedia PDF Downloads 339

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 185

Authors: Rajendar Kandan, Mohammad Zakaria Alli, Hong Ong

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Cloud computing is a business model which provides an easier management of computing resources. Cloud users can request virtual machine and install additional softwares and configure them if needed. However, user can also request virtual appliance which provides a better solution to deploy application in much faster time, as it is ready-built image of operating system with necessary softwares installed and configured. Large numbers of virtual appliances are available in different image format. User can download available appliances from public marketplace and start using it. However, information published about the virtual appliance differs from each providers leading to the difficulty in choosing required virtual appliance as it is composed of specific OS with standard software version. However, even if user choses the appliance from respective providers, user doesn’t have any flexibility to choose their own set of softwares with required OS and application. In this paper, we propose a referenced architecture for dynamically customizing virtual appliance and provision them in an easier manner. We also add our experience in integrating our proposed architecture with public marketplace and Mi-Cloud, a cloud management software.

Keywords: cloud computing, marketplace, virtualization, virtual appliance

Procedia PDF Downloads 291

Authors: Catharina Lindberg, Cecilia Fagerstrom, Ania Willman

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Patients in high-tech care environments are usually dependent on both formal/informal caregivers and technology, highlighting their vulnerability and challenging their autonomy. Autonomy presumes that a person has education, experience, self-discipline and decision-making capacity. Reference to autonomy in relation to patients in high-tech care environments could, therefore, be considered paradoxical, as in most cases these persons have impaired physical and/or metacognitive capacity. Therefore, to understand the prerequisites for patients to experience autonomy in high-tech care environments and to support them, there is a need to enhance knowledge and understanding of the concept of patient autonomy in this care context. The development of concepts and theories in a practice discipline such as nursing helps to improve both nursing care and nursing education. Theoretical development is important when clarifying a discipline, hence, a theoretical framework could be of use to nurses in high-tech care environments to support and defend the patient’s autonomy. A meta-synthesis was performed with the intention to be interpretative and not aggregative in nature. An amalgamation was made of the results from three previous studies, carried out by members of the same research group, focusing on the phenomenon of patient autonomy from a patient perspective within a caring context. Three basic approaches to theory development: derivation, synthesis, and analysis provided an operational structure that permitted the researchers to move back and forth between these approaches during their work in developing a theoretical framework. The results from the synthesis delineated that patient autonomy in a high-tech care context is: To be in control though trust, co-determination, and transition in everyday life. The theoretical framework contains several components creating the prerequisites for patient autonomy. Assumptions and propositional statements that guide theory development was also outlined, as were guiding principles for use in day-to-day nursing care. Four strategies used by patients to remain or obtain patient autonomy in high-tech care environments were revealed: the strategy of control, the strategy of partnership, the strategy of trust, and the strategy of transition. This study suggests an extended knowledge base founded on theoretical reasoning about patient autonomy, providing an understanding of the strategies used by patients to achieve autonomy in the role of patient, in high-tech care environments. When possessing knowledge about the patient perspective of autonomy, the nurse/carer can avoid adopting a paternalistic or maternalistic approach. Instead, the patient can be considered to be a partner in care, allowing care to be provided that supports him/her in remaining/becoming an autonomous person in the role of patient.

Keywords: autonomy, caring, concept development, high-tech care, theory development

Procedia PDF Downloads 204

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 128

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 374

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 359

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 99

Authors: Agastya Pratap Singh

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This review paper examines the emerging applications of generative adversarial networks (GANs) in the fields of image recognition and anomaly detection. With the rapid growth of digital image data, the need for efficient and accurate methodologies to identify and classify images has become increasingly critical. GANs, known for their ability to generate realistic data, have gained significant attention for their potential to enhance traditional image recognition systems and improve anomaly detection performance. The paper systematically analyzes various GAN architectures and their modifications tailored for image recognition tasks, highlighting their strengths and limitations. Additionally, it delves into the effectiveness of GANs in detecting anomalies in diverse datasets, including medical imaging, industrial inspection, and surveillance. The review also discusses the challenges faced in training GANs, such as mode collapse and stability issues, and presents recent advancements aimed at overcoming these obstacles.

Keywords: generative adversarial networks, image recognition, anomaly detection, synthetic data generation, deep learning, computer vision, unsupervised learning, pattern recognition, model evaluation, machine learning applications

Procedia PDF Downloads 11

Authors: M. Abu Hanif, Anika Tabassum, Fuad Hasan Ovi, Ishrat Islam

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Water is essential for life. Enormous quantities of water are cycled each year through hydrologic cycle but only a fraction of circulated water is available each year for human use. Dhaka, the capital of Bangladesh is the 19th mega city in the world with a population of over 14 million (World City Information, 2011). As a result the growth of urban population is increasing rapidly; the city is not able to manage with altering situations due to resource limitations and management capacity. Water crisis has become an acute problem faced by the inhabitants of Dhaka city. It is found that total water demand in Dhaka city is 2,240 million liter per day (MLD) whereas supply is 2,150 (MLD). According to Dhaka Water Supply and Sewerage Authority about 87 percent of this supply comes from groundwater resources and rest 13 percent from surface water. According to Dhaka Water Supply and Sewerage Authority it has been found that the current groundwater depletion rate is 3.52 meter per year. Such a fast depletion of the water table will result in intrusion of southern saline water into the groundwater reservoir, depriving this mega city of pure drinking water. This study mainly focus on the potential of Rainwater Harvesting System(RWHS) in Kazipara area of Dhaka city, determine the perception level of local people in installation of rainwater harvesting system in their building and identify the factors regarding willingness of owner in installing rainwater harvesting system. As most of the residential area of Dhaka city is unplanned with small plots, Kazipara area has been chosen as study area which depicts similar characteristics. In this study only roof top area is considered as catchment area and potential of rainwater harvesting has been calculated. From the calculation it is found that harvested rainwater can serve the 66% of demand of water for toilet flushing and cleaning purposes for the people of Kazipara. It is also observed that if only rooftop rainwater harvesting applied to all the structures of the study area then two third of surface runoff would be reduced than present surface runoff. In determining the perception of local people only owners of the buildings were. surveyed. From the questionnaire survey it is found that around 75% people have no idea about the rainwater harvesting system. About 83% people are not willing to install rainwater harvesting system in their dwelling. The reasons behind the unwillingness are high cost of installation, inadequate space, ignorance about the system, etc. Among 16% of the willing respondents who are interested in installing RWHS system, it was found that higher income, bigger size of buildings are important factors in willingness of installing rainwater harvesting system. Majority of the respondents demanded for both technical and economical support to install the system in their buildings. Government of Bangladesh has taken some initiatives to promote rainwater harvesting in urban areas. It is very much necessary to incorporate rainwater harvesting device and artificial recharge system in every building of Dhaka city to make Dhaka city self sufficient in water supply management and to solve water crisis problem of megacity like as Dhaka city.

Keywords: rainwater harvesting, water table, willingness, storm water

Procedia PDF Downloads 239

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 147

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 186

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 388

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 380

Authors: Mabel Y. C. Yau, Roy C. F. Lai, Hugo Y. H. Or

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This study is a pilot surveillance of hygiene compliance and food microbial safety of both licensed and mobile vendors selling Chinese ready–to-eat snack foods in Hong Kong. The study reflects similar situations in running mobile food vending business on trucks. Hong Kong is about to launch the Food Truck Pilot Scheme by the end of 2016 or early 2017. Technically, selling food on the vehicle is no different from hawking food on the street or vending food on the street. Each type of business bears similar food safety issues and cast the same impact on public health. Present findings demonstrate exemplarily situations that also apply to food trucks. 9 types of Cantonese style snacks of 32 samples in total were selected for microbial screening. A total of 16 vending sites including supermarkets, street markets, and snack stores were visited. The study finally focused on a traditional snack, the steamed rice cake with red beans called Put Chai Ko (PCK). PCK is a type of classical Cantonese pastry sold on push carts on the street. It used to be sold at room temperature and served with bamboo sticks in the old days. Some shops would have them sold steam fresh. Microbial examinations on aerobic counts, yeast, and mould, coliform, salmonella as well as Staphylococcus aureus detections were carried out. Salmonella was not detected in all samples. Since PCK does not contain ingredients of beef, poultry, eggs or dairy products, the risk of the presence of Salmonella in PCK was relatively lower although other source of contamination might be possible. Coagulase positive Staphylococcus aureus was found in 6 of the 14 samples sold at room temperature. Among these 6 samples, 3 were PCK. One of the samples was in an unacceptable range of total colony forming units higher than 105. The rest were only satisfactory. Observational evaluations were made with checklists on personal hygiene, premises hygiene, food safety control, food storage, cleaning and sanitization as well as waste disposals. The maximum score was 25 if total compliance were obtained. The highest score among vendors was 20. Three stores were below average, and two of these stores were selling PCK. Most of the non-compliances were on food processing facilities, sanitization conditions and waste disposal. In conclusion, although no food poisoning outbreaks happened during the time of the investigation, the risk of food hazard existed in these stores, especially among street vendors. Attention is needed in the traditional practice of food selling, and that food handlers might not have sufficient knowledge to properly handle food products. Variations in food qualities existed among supply chains or franchise eateries or shops. It was commonly observed that packaging and storage conditions are not properly enforced in the retails. The same situation could be reflected across the food business. It did indicate need of food safety training in the industry and loopholes in quality control among business.

Keywords: cantonese snacks, food safety, microbial, hygiene, street food

Procedia PDF Downloads 299

Authors: M. Veeresham

Abstract:

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: Jiawei Chen, Jia Qu, Dianwei Ju

Abstract:

316L stainless steel has good mechanical and technological properties, has been widely used in shipbuilding and aerospace manufacturing. In order to understand the effect of strain rate on the yield limit of 316L stainless steel and the constitutive relationship of the materials at different strain rates, this paper used the INSTRON-4505 electronic universal testing machine to study the mechanical properties of the tensile specimen under quasi-static conditions. Meanwhile, the Zwick-Roell RKP450 intelligent oscillometric impact tester was used to test the tensile specimens at different strain rates. Through the above two kinds of experimental researches, the relationship between the true stress-strain and the engineering stress-strain at different strain rates is obtained. The result shows that the tensile yield point of 316L stainless steel increases with the increase of strain rate, and the real stress-strain curve of the 316L stainless steel has a better normalization than that of the engineering stress-strain curve. The real stress-strain curves can be used in the practical engineering of impact stretch to improve its safety.

Keywords: impact stretch, 316L stainless steel, strain rate, real stress-strain, normalization

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Authors: M. Mueller, M. Kuehn, M. Voelker

Abstract:

In small and medium-sized enterprises (SMEs), the challenge is to create a well-grounded and reliable basis for process analysis, optimization and planning due to a lack of data. SMEs have limited access to methods with which they can effectively and efficiently analyse processes and identify cause-and-effect relationships in order to generate the necessary database and derive optimization potential from it. The implementation of digitalization within the framework of Industry 4.0 thus becomes a particular necessity for SMEs. For these reasons, the abstract presents an analysis methodology that is subject to the objective of developing an SME-appropriate methodology for efficient, temporarily feasible data collection and evaluation in flexible production and logistics systems as a basis for process analysis and optimization. The overall methodology focuses on retrospective, event-based tracing and analysis of material flow objects. The technological basis consists of Bluetooth low energy (BLE)-based transmitters, so-called beacons, and smart mobile devices (SMD), e.g. smartphones as receivers, between which distance data can be measured and derived motion profiles. The distance is determined using the Received Signal Strength Indicator (RSSI), which is a measure of signal field strength between transmitter and receiver. The focus is the development of a software-based methodology for interpretation of relative movements of transmitters and receivers based on distance data. The main research is on selection and implementation of pattern recognition methods for automatic process recognition as well as methods for the visualization of relative distance data. Due to an existing categorization of the database regarding process types, classification methods (e.g. Support Vector Machine) from the field of supervised learning are used. The necessary data quality requires selection of suitable methods as well as filters for smoothing occurring signal variations of the RSSI, the integration of methods for determination of correction factors depending on possible signal interference sources (columns, pallets) as well as the configuration of the used technology. The parameter settings on which respective algorithms are based have a further significant influence on result quality of the classification methods, correction models and methods for visualizing the position profiles used. The accuracy of classification algorithms can be improved up to 30% by selected parameter variation; this has already been proven in studies. Similar potentials can be observed with parameter variation of methods and filters for signal smoothing. Thus, there is increased interest in obtaining detailed results on the influence of parameter and factor combinations on data quality in this area. The overall methodology is realized with a modular software architecture consisting of independently modules for data acquisition, data preparation and data storage. The demonstrator for initialization and data acquisition is available as mobile Java-based application. The data preparation, including methods for signal smoothing, are Python-based with the possibility to vary parameter settings and to store them in the database (SQLite). The evaluation is divided into two separate software modules with database connection: the achievement of an automated assignment of defined process classes to distance data using selected classification algorithms and the visualization as well as reporting in terms of a graphical user interface (GUI).

Keywords: event-based tracing, machine learning, process classification, parameter settings, RSSI, signal smoothing

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Authors: Igwenagu Chinelo Mercy

Abstract:

This study is on the concept of Neurostatistics in relation to neuroscience. Neuroscience also known as neurobiology is the scientific study of the nervous system. In the study of neuroscience, it has been noted that brain function and its relations to the process of acquiring knowledge and behaviour can be better explained by the use of various interrelated methods. The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales. On the other hand, Neurostatistics based on this study is viewed as a statistical concept that uses similar techniques of neuron mechanisms to solve some problems especially in the field of life science. This study is imperative in this era of Artificial intelligence/Machine leaning in the sense that clear understanding of the technique and its proper application could assist in solving some medical disorder that are mainly associated with the nervous system. This will also help in layman’s understanding of the technique of the nervous system in order to overcome some of the health challenges associated with it. For this concept to be well understood, an illustrative example using a brain associated disorder was used for demonstration. Structural equation modelling was adopted in the analysis. The results clearly show the link between the techniques of statistical model and nervous system. Hence, based on this study, the appropriateness of Neurostatistics application in relation to neuroscience could be based on the understanding of the behavioural pattern of both concepts.

Keywords: brain, neurons, neuroscience, neurostatistics, structural equation modeling

Procedia PDF Downloads 69

Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

Abstract:

Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet

Procedia PDF Downloads 329

Authors: Huayuan Lu, Chunfang Yang, Ma Zhu, Baojun Qi, Yaqiong Qiao, Jiangqian Xu

Abstract:

Image geolocalization has great application prospects in fields such as autonomous driving and virtual/augmented reality. In practical application scenarios, the size of the image to be located is not fixed; it is impractical to train different networks for all possible sizes. When its size does not match the size of the input of the descriptor extraction model, existing image geolocalization methods usually directly scale or crop the image in some common ways. This will result in the loss of some information important to the geolocalization task, thus affecting the performance of the image geolocalization method. For example, excessive down-sampling can lead to blurred building contour, and inappropriate cropping can lead to the loss of key semantic elements, resulting in incorrect geolocation results. To address this problem, this paper designs a learnable image resizer and proposes an arbitrary-sized image geolocation method. (1) The designed learnable image resizer employs the self-attention mechanism to enhance the geometric features of the resized image. Firstly, it applies bilinear interpolation to the input image and its feature maps to obtain the initial resized image and the resized feature maps. Then, SKNet (selective kernel net) is used to approximate the best receptive field, thus keeping the geometric shapes as the original image. And SENet (squeeze and extraction net) is used to automatically select the feature maps with strong contour information, enhancing the geometric features. Finally, the enhanced geometric features are fused with the initial resized image, to obtain the final resized images. (2) The proposed image geolocalization method embeds the above image resizer as a fronting layer of the descriptor extraction network. It not only enables the network to be compatible with arbitrary-sized input images but also enhances the geometric features that are crucial to the image geolocalization task. Moreover, the triplet attention mechanism is added after the first convolutional layer of the backbone network to optimize the utilization of geometric elements extracted by the first convolutional layer. Finally, the local features extracted by the backbone network are aggregated to form image descriptors for image geolocalization. The proposed method was evaluated on several mainstream datasets, such as Pittsburgh30K, Tokyo24/7, and Places365. The results show that the proposed method has excellent size compatibility and compares favorably to recently mainstream geolocalization methods.

Keywords: image geolocalization, self-attention mechanism, image resizer, geometric feature

Procedia PDF Downloads 212