Search results for: distributed algorithms

Authors: Alla Levina, Sergey Taranov

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The classical approach to the providing noise immunity and integrity of information that process in computing devices and communication channels is to use linear codes. Linear codes have fast and efficient algorithms of encoding and decoding information, but this codes concentrate their detect and correct abilities in certain error configurations. To protect against any configuration of errors at predetermined probability can robust codes. This is accomplished by the use of perfect nonlinear and almost perfect nonlinear functions to calculate the code redundancy. The paper presents the error-correcting coding scheme using biorthogonal wavelet transform. Wavelet transform applied in various fields of science. Some of the wavelet applications are cleaning of signal from noise, data compression, spectral analysis of the signal components. The article suggests methods for constructing linear codes based on wavelet decomposition. For developed constructions we build generator and check matrix that contain the scaling function coefficients of wavelet. Based on linear wavelet codes we develop robust codes that provide uniform protection against all errors. In article we propose two constructions of robust code. The first class of robust code is based on multiplicative inverse in finite field. In the second robust code construction the redundancy part is a cube of information part. Also, this paper investigates the characteristics of proposed robust and linear codes.

Keywords: robust code, linear code, wavelet decomposition, scaling function, error masking probability

Procedia PDF Downloads 465

Authors: Youngjae Jin, Daeshik Kim

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This paper describes a cycle accurate simulation results of weight values learned by an auto-encoder behavior model in terms of pre-route simulation. Given the results we visualized the first layer representations with natural images. Many common deep learning threads have focused on learning high-level abstraction of unlabeled raw data by unsupervised feature learning. However, in the process of handling such a huge amount of data, the learning method’s computation complexity and time limited advanced research. These limitations came from the fact these algorithms were computed by using only single core CPUs. For this reason, parallel-based hardware, FPGAs, was seen as a possible solution to overcome these limitations. We adopted and simulated the ready-made auto-encoder to design a behavior model in Verilog HDL before designing hardware. With the auto-encoder behavior model pre-route simulation, we obtained the cycle accurate results of the parameter of each hidden layer by using MODELSIM. The cycle accurate results are very important factor in designing a parallel-based digital hardware. Finally this paper shows an appropriate operation of behavior model based pre-route simulation. Moreover, we visualized learning latent representations of the first hidden layer with Kyoto natural image dataset.

Keywords: auto-encoder, behavior model simulation, digital hardware design, pre-route simulation, Unsupervised feature learning

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Authors: Witthaya Mekhum, Jinjutha Srihera

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This research aims to study customer satisfaction towards the services of Baan Bueng Resort in Nongprue Subdistrict, Baanlamung District, Chonburi Province. 108 sample were drawn by random sampling from Thai and foreign tourists at Baan Bueng Resort. Questionnaires were distributed. Data were analyzed using frequency, percentage, mean (X) and standard deviation (S.D.). The tool used in this research was questionnaire on satisfaction towards the services of Baan Bueng Resort in Nongprue Subdistrict, Baanlamung District, Chonburi Province. The questionnaire can be divided into 3 parts; i.e. Part 1: General information i.e. gender, age, educational level, occupation, income, and nationality, Part 2: Customer satisfaction towards the services of Baan Bueng Resort; and Part 3: Suggestions of respondents. It can be concluded that most of the respondents are male, aged between 25 – 35 years old with bachelor degree. Most of them are private company employees with income 10,000–20,000 Baht per month. The majority of customers are satisfied with the services at Baan Beung Resort. Overall satisfaction is at good level. Considering each item, the item with the highest satisfaction level is personality and manner of employees and promptness and accuracy of cashier staff. Overall satisfaction towards the cleanliness of the rooms is at very good level. When considering each item, the item with the highest satisfaction level is that the guest room is cleaned everyday, while the satisfaction towards the quality of food and beverages at Baan Bueng Resort in Nongprue Subdistrict, Baanlamung District, Chonburi Province is at very good level. The item with the highest satisfaction is hotel facilities.

Keywords: satisfaction study, service, hotel, customer

Procedia PDF Downloads 312

Authors: Myungjin Lee, Daegun Han, Jongsung Kim, Soojun Kim, Hung Soo Kim

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Recently, the localized heavy rainfall and typhoons are frequently occurred due to the climate change and the damage is becoming bigger. Therefore, we may need a more accurate prediction of the rainfall and runoff. However, the gauge rainfall has the limited accuracy in space. Radar rainfall is better than gauge rainfall for the explanation of the spatial variability of rainfall but it is mostly underestimated with the uncertainty involved. Therefore, the ensemble of radar rainfall was simulated using error structure to overcome the uncertainty and gauge rainfall. The simulated ensemble was used as the input data of the rainfall-runoff models for obtaining the ensemble of runoff hydrographs. The previous studies discussed about the accuracy of the rainfall-runoff model. Even if the same input data such as rainfall is used for the runoff analysis using the models in the same basin, the models can have different results because of the uncertainty involved in the models. Therefore, we used two models of the SSARR model which is the lumped model, and the Vflo model which is a distributed model and tried to simulate the optimum runoff considering the uncertainty of each rainfall-runoff model. The study basin is located in Han river basin and we obtained one integrated runoff hydrograph which is an optimum runoff hydrograph using the blending methods such as Multi-Model Super Ensemble (MMSE), Simple Model Average (SMA), Mean Square Error (MSE). From this study, we could confirm the accuracy of rainfall and rainfall-runoff model using ensemble scenario and various rainfall-runoff model and we can use this result to study flood control measure due to climate change. Acknowledgements: This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 18AWMP-B083066-05).

Keywords: radar rainfall ensemble, rainfall-runoff models, blending method, optimum runoff hydrograph

Procedia PDF Downloads 251

Authors: Ji-Seok Koo, Hee‑Yong Kwon, Hui-Young Yun, Kyung-Hui Wang, Youn-Seo Koo

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This paper presents a forecasting system for PM2.5 levels in Seoul, South Korea, leveraging a combination of chemical transport modeling and ConvLSTM-DNN machine learning technology. Exposure to PM2.5 has known detrimental impacts on public health, making its prediction crucial for establishing preventive measures. Existing forecasting models, like the Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting (WRF), are hindered by their reliance on uncertain input data, such as anthropogenic emissions and meteorological patterns, as well as certain intrinsic model limitations. The system we've developed specifically addresses these issues by integrating machine learning and using carefully selected input features that account for local and distant sources of PM2.5. In South Korea, the PM2.5 concentration is greatly influenced by both local emissions and long-range transport from China, and our model effectively captures these spatial and temporal dynamics. Our PM2.5 prediction system combines the strengths of advanced hybrid machine learning algorithms, convLSTM and DNN, to improve upon the limitations of the traditional CMAQ model. Data used in the system include forecasted information from CMAQ and WRF models, along with actual PM2.5 concentration and weather variable data from monitoring stations in China and South Korea. The system was implemented specifically for Seoul's PM2.5 forecasting.

Keywords: PM2.5 forecast, machine learning, convLSTM, DNN

Procedia PDF Downloads 39

Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Rupali, Anil Kumar Jaiswal

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Cloud computing can be defined as one of the prominent technologies that lets a user change, configure and access the services online. it can be said that this is a prototype of computing that helps in saving cost and time of a user practically the use of cloud computing can be found in various fields like education, health, banking etc.  Cloud computing is an internet dependent technology thus it is the major responsibility of Cloud Service Providers(CSPs) to care of data stored by user at data centers. Scheduling in cloud computing environment plays a vital role as to achieve maximum utilization and user satisfaction cloud providers need to schedule resources effectively.  Job scheduling for cloud computing is analyzed in the following work. To complete, recreate the task calculation, and conveyed scheduling methods CloudSim3.0.3 is utilized. This research work discusses the job scheduling for circulated processing condition also by exploring on this issue we find it works with minimum time and less cost. In this work two load balancing techniques have been employed: ‘Throttled stack adjustment policy’ and ‘Active VM load balancing policy’ with two brokerage services ‘Advanced Response Time’ and ‘Reconfigure Dynamically’ to evaluate the VM_Cost, DC_Cost, Response Time, and Data Processing Time. The proposed techniques are compared with Round Robin scheduling policy.

Keywords: physical machines, virtual machines, support for repetition, self-healing, highly scalable programming model

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Authors: P. Byrnes, F. A. DiazDelaO

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The Support Vector Machine (SVM) has become widely recognised as one of the leading algorithms in machine learning for both regression and binary classification. It expresses predictions in terms of a linear combination of kernel functions, referred to as support vectors. Despite its popularity amongst practitioners, SVM has some limitations, with the most significant being the generation of point prediction as opposed to predictive distributions. Stemming from this issue, a probabilistic model namely, Probabilistic Classification Vector Machines (PCVM), has been proposed which respects the original functional form of SVM whilst also providing a predictive distribution. As physical system designs become more complex, an increasing number of classification tasks involving industrial applications consist of more than two classes. Consequently, this research proposes a framework which allows for the extension of PCVM to a multi class setting. Additionally, the original PCVM framework relies on the use of type II maximum likelihood to provide estimates for both the kernel hyperparameters and model evidence. In a high dimensional multi class setting, however, this approach has been shown to be ineffective due to bad scaling as the number of classes increases. Accordingly, we propose the application of Markov Chain Monte Carlo (MCMC) based methods to provide a posterior distribution over both parameters and hyperparameters. The proposed framework will be validated against current multi class classifiers through synthetic and real life implementations.

Keywords: probabilistic classification vector machines, multi class classification, MCMC, support vector machines

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Authors: Ebrahim Panah, Muhammad Yasir Babar

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Thanks to the development of online technology and social networks, people are able to communicate as well as learn. WhatsApp is a popular social network which is growingly gaining popularity. This app can be used for communication as well as education. It can be used for instructor-learner, learner-learner, and learner-content interactions; however, very little knowledge is available on these potentials of WhatsApp. The current study was undertaken to investigate university students’ perceptions of WhatsApp used as a tool for instructor-learner dialogue, learner-content dialogue, and learner-learner dialogue. The study adopted a survey approach and distributed the questionnaire developed by Google Forms to 54 (11 males and 43 females) university students. The obtained data were analyzed using SPSS version 20. The result of data analysis indicates that students have positive attitudes towards WhatsApp as a tool for Instructor-Learner Dialogue: it easy to reach the lecturer (4.07), the instructor gives me valuable feedback on my assignment (4.02), the instructor is supportive during course discussion and offers continuous support with the class (4.00). Learner-Content Dialogue: WhatsApp allows me to academically engage with lecturers anytime, anywhere (4.00), it helps to send graphics such as pictures or charts directly to the students (3.98), it also provides out of class, extra learning materials and homework (3.96), and Learner-Learner Dialogue: WhatsApp is a good tool for sharing knowledge with others (4.09), WhatsApp allows me to academically engage with peers anytime, anywhere (4.07), and we can interact with others through the use of group discussion (4.02). It was also found that there are significant positive correlations between students’ perceptions of Instructor-Learner Dialogue (ILD), Learner-Content Dialogue (LCD), Learner-Learner Dialogue (LLD) and WhatsApp Application in classroom. The findings of the study have implications for lectures, policy makers and curriculum developers.

Keywords: instructor-learner dialogue, learners-contents dialogue, learner-learner dialogue, whatsapp application

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Authors: Palanisamy Sivanandy, Tan Tyng Wei, Tan Wee Loon, Lim Chong Yee

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Background: Patient safety has become a major concern over recent years with elevated medication errors; particularly prescribing and dispensing errors. Meticulous prescription screening and diligent drug dispensing is therefore important to prevent drug-related adverse events from inflicting harm to patients. Hence, pharmacists play a significant role in this scenario. The evaluation of patient safety in a pharmacy setup is crucial to contemplate current practices, attitude and perception of pharmacists towards patient safety. Method: The questionnaire for Pharmacy Survey on Patient Safety Culture developed by the Agency for Healthcare and Research Quality (AHRQ) was used to assess patient safety. Main objectives of the study was to evaluate the attitude and perception of pharmacists towards patient safety in retail pharmacies setup in Malaysia. Results: 417 questionnaire were distributed via convenience sampling in three different states of Malaysia, where 390 participants were responded and the response rate was 93.52%. The overall positive response rate (PRR) was ranged from 31.20% to 87.43% and the average PRR was found to be 67%. The overall patient safety grade for our pharmacies was appreciable and it ranges from good to very good. The study found a significant difference in the perception of senior and junior pharmacists towards patient safety. The internal consistency of the questionnaire contents /dimensions was satisfactory (Cronbach’s alpha - 0.92). Conclusion: Our results reflect that there was positive attitude and perception of retail pharmacists towards patient safety. Despite this, various efforts can be implemented in the future to amplify patient safety in retail pharmacies setup.

Keywords: patient safety, attitude, perception, positive response rate, medication errors

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Authors: Arvind Srivastav, Tarun Kanti Bhattacharyya

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The north-eastern, Himalayan, and Eastern Ghats Belt of India comprise of earthquake-prone, remote, and hilly terrains. Earthquakes have caused enormous damages in these regions in the past. A wireless sensor network based earthquake early warning system (EEWS) is being developed to mitigate the damages caused by earthquakes. It consists of sensor nodes, distributed over the region, that perform majority voting of the output of the seismic sensors in the vicinity, and relay a message to a base station to alert the residents when an earthquake is detected. At the heart of the EEWS is a low-power two-stage seismic sensor that continuously tracks seismic events from incoming three-axis accelerometer signal at the first-stage, and, in the presence of a seismic event, triggers the second-stage P-wave detector that detects the onset of P-wave in an earthquake event. The parameters of the P-wave detector have been optimized for minimizing detection time and maximizing the accuracy of detection.Working of the sensor scheme has been verified with seven earthquakes data retrieved from IRIS. In all test cases, the scheme detected the onset of P-wave accurately. Also, it has been established that the P-wave onset detection time reduces linearly with the sampling rate. It has been verified with test data; the detection time for data sampled at 10Hz was around 2 seconds which reduced to 0.3 second for the data sampled at 100Hz.

Keywords: earthquake early warning system, EEWS, STA/LTA, polarization, wavelet, event detector, P-wave detector

Procedia PDF Downloads 160

Authors: Hayley Boal, Chloe Bromley, John Fairfield

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Novel Psychoactive Substances (NPS) are synthetic compounds designed to reproduce effects of illicit drugs. Cheap, potent, and readily available on UK highstreets from so-called ‘head shops’, in recent years their use has surged and with it have emerged side effects including seizures, aggression, palpitations, coma, and death. Rapid development of new substances has vastly outpaced pre-existing drug legislation but the Psychoactive Substances Act 2016 rendered all but tobacco, alcohol, and amyl nitrates, illegal. Drug use has long been rife within prisons, but the absence of a reliable screening tool alongside the availability of NPS makes them ideal for prison use. Here we examine the occurrence of NPS-related adverse side effects within HMP Leeds, comparing May-September of 2015 and 2017 using daily reports distributed amongst prison staff summarising medical and behavioural incidents of the previous day. There was a statistically-significant rise of over 200% in the use of NPS between 2015 and 2017: 0.562 and 1.149 incidents per day respectively. In 2017, 38.46% incidents required ambulances, fallen from 51.02% in 2015. Although the most common descriptions in both years were ‘seizure’ and ‘unresponsive’, by 2017 ‘inhalation by staff’ had emerged. Patterns of NPS consumption mirrored the prison regime, peaking when cell doors opened, and prisoners could socialise. Despite limited data, the Psychoactive Substances Act has clearly been an insufficient deterrent to the prison population; more must be done to understand and address substance misuse in prison. NPS remains a significant risk to prisoners’ health and wellbeing.

Keywords: legislation, novel psychoactive substances, prison, spice

Procedia PDF Downloads 168

Authors: Mohammad Bakhshandeh

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Structural Health Monitoring (SHM) is a crucial and necessary practice that plays a vital role in ensuring the safety and integrity of critical structures, and in particular, bridges. The continuous monitoring of bridges for signs of damage or degradation through Bridge Health Monitoring (BHM) enables early detection of potential problems, allowing for prompt corrective action to be taken before significant damage occurs. Although all monitoring techniques aim to provide accurate and decisive information regarding the remaining useful life, safety, integrity, and serviceability of bridges, understanding the development and propagation of damage is vital for maintaining uninterrupted bridge operation. Over the years, extensive research has been conducted on BHM methods, and experts in the field have increasingly adopted new methodologies. In this article, we provide a comprehensive exploration of the various BHM approaches, including sensor-based, non-destructive testing (NDT), model-based, and artificial intelligence (AI)-based methods. We also discuss the challenges associated with BHM, including sensor placement and data acquisition, data analysis and interpretation, cost and complexity, and environmental effects, through an extensive review of relevant literature and research studies. Additionally, we examine potential solutions to these challenges and propose future research ideas to address critical gaps in BHM.

Keywords: structural health monitoring (SHM), bridge health monitoring (BHM), sensor-based methods, machine-learning algorithms, and model-based techniques, sensor placement, data acquisition, data analysis

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Authors: Chiagoziem A. Otuechere, Ebenezer O. Farombi

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The protective effect of dichloromethane: methanol extract of Pterocarpus mildbraedii (PME), a widely consumed Nigerian leafy vegetable, on the toxicity of propanil was investigated in male rats. Animals were distributed into eight groups of five each. Group 1 served as control and received normal saline while rats in groups 2, 3, and 4 received 100 mg/kg, 200 mg/kg, and 400 mg/kg extract doses respectively. Group 5 rats were orally administered 200 mg/kg propanil while groups 6, 7, and 8 rats were given propanil plus extract. Oral administration of propanil elicited a 14.8%, 5%, 122%, and 78% increase in the activity of serum enzymes; alanine aminotransferase (AST), alanine aminotransferase(ALT), Alkaline phoshatase (ALP) and Gamma glutamyl transferase (ﻻGT). There were also increase in Lactate dehydrogenase (LDH) activity, direct bilirubin and lipid peroxidation levels. Furthermore, PME significantly attenuated the marked hepatic oxidative damage that accompanied propanil treatment. The extract significantly decreased LDH activity and bilirubin levels following propanil treatment. Furthermore, propanil-induced alterations in the activities of antioxidant enzymes: Superoxide dismutase (SOD), catalase (CAT) and glutathione s-transferase (GST) in these rats were modulated by the extract. The percentage DPPH Radical Scavenging Activity of the extract was determined as 55% and compared to those of Gallic acid (49%). Hepatic histology examination further confirmed the damage to the liver as it revealed severe periportal cellular infiltration of the hepatocytes. These biochemical and morphological alterations were attenuated in rats pre-treated with 100 mg/kg and 200 mg/kg doses of the extract. These results suggest that PME possesses protective effect against propanil-induced hepatotoxicity.

Keywords: antioxidant, hepatoprotection, Pterocarpus mildbraedii, propanil

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Authors: Vasanth C. Shunmugasamy, Bilal Mansoor

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Magnesium alloys are increasingly being considered for structural systems across different industrial sectors, including precision components of biomedical devices, owing to their high specific strength, stiffness and biodegradability. However, Mg alloys exhibit a high corrosion rate that restricts their application as a biomaterial. For safe use as biomaterial, it is essential to control their corrosion rates. Mg alloy corrosion is influenced by several factors, such as grain size, precipitates and texture. In Mg alloys, microgalvanic coupling between the α-Mg matrix and secondary precipitates can exist, which results in an increased corrosion rate. The present research addresses this challenge by engineering the microstructure of a biodegradable Mg–Zn–RE–Zr alloy by friction stir processing (FSP), a severe plastic deformation process. The FSP-processed Mg alloys showed improved corrosion resistance and mechanical properties. FSPed Mg alloy showed refined grains, a strong basal texture and broken and uniformly distributed secondary precipitates in the stir zone. Mg, alloy base material, exposed to In vitro corrosion medium showed micro galvanic coupling between precipitate and matrix, resulting in the unstable passive layer. However, FS processed alloy showed uniform corrosion owing to stable surface film formation. The stable surface film is attributed to refined grains, preferred texture and distribution of precipitates. The research results show promising potential for Mg alloy to be developed as a biomaterial.

Keywords: biomaterials, severe plastic deformation, magnesium alloys, corrosion

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Authors: P. Suresh, K. Gunasekaran, R. Thanigaivelan

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Radio frequency identification (RFID) technology has been attracting considerable attention with the expectation of improved supply chain visibility for consumer goods, apparel, and pharmaceutical manufacturers, as well as retailers and government procurement agencies. It is also expected to improve the consumer shopping experience by making it more likely that the products they want to purchase are available. Recent announcements from some key retailers have brought interest in RFID to the forefront. A modified K- Means Cluster based Heuristic approach, Hybrid Genetic Algorithm (GA) - Simulated Annealing (SA) approach, Hybrid K-Means Cluster based Heuristic-GA and Hybrid K-Means Cluster based Heuristic-GA-SA for Open Loop Supply Chain Network problem are proposed. The study incorporated uniform crossover operator and combined crossover operator in GAs for solving open loop supply chain distribution network problem. The algorithms are tested on 50 randomly generated data set and compared with each other. The results of the numerical experiments show that the Hybrid K-means cluster based heuristic-GA-SA, when tested on 50 randomly generated data set, shows superior performance to the other methods for solving the open loop supply chain distribution network problem.

Keywords: RFID, supply chain distribution network, open loop supply chain, genetic algorithm, simulated annealing

Procedia PDF Downloads 139

Authors: Aya Belal, Ahmed Maher Eltair, Maggie Ahmed Mashaly

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Geopolymer concrete is gaining recognition as a sustainable alternative to conventional Portland Cement concrete due to its environmentally friendly nature, which is a key goal for Smart City initiatives. It has demonstrated its potential as a reliable material for the design of structural elements. However, the production of Geopolymer concrete is hindered by batch-to-batch variations, which presents a significant challenge to the widespread adoption of Geopolymer concrete. To date, Machine learning has had a profound impact on various fields by enabling models to learn from large datasets and predict outputs accurately. This paper proposes an integration between the current drift to Artificial Intelligence and the composition of Geopolymer mixtures to predict their mechanical properties. This study employs Python software to develop machine learning model in specific Decision Trees. The research uses the percentage oxides and the chemical composition of the Alkali Solution along with the curing conditions as the input independent parameters, irrespective of the waste products used in the mixture yielding the compressive strength of the mix as the output parameter. The results showed 90 % agreement of the predicted values to the actual values having the ratio of the Sodium Silicate to the Sodium Hydroxide solution being the dominant parameter in the mixture.

Keywords: decision trees, geopolymer concrete, machine learning, smart cities, sustainability

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Authors: Bagul Abhijeet

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Background: Client-Server model is the backbone of today’s internet communication. In which normal user can not have control over particular website or server? By using the same processing model one can have unauthorized access to particular server. In this paper, we discussed about application scenario of hacking for simple website or server consist of unauthorized way to access the server database. This application emerges to autonomously take direct access of simple website or server and retrieve all essential information maintain by administrator. In this system, IP address of server given as input to retrieve user-id and password of server. This leads to breaking administrative security of server and acquires the control of server database. Whereas virus helps to escape from server security by crashing the whole server. Objective: To control malicious attack and preventing all government website, and also find out illegal work to do hackers activity. Results: After implementing different hacking as well as non-hacking techniques, this system hacks simple web sites with normal security credentials. It provides access to server database and allow attacker to perform database operations from client machine. Above Figure shows the experimental result of this application upon different servers and provides satisfactory results as required. Conclusion: In this paper, we have presented a to view to hack the server which include some hacking as well as non-hacking methods. These algorithms and methods provide efficient way to hack server database. By breaking the network security allow to introduce new and better security framework. The terms “Hacking” not only consider for its illegal activities but also it should be use for strengthen our global network.

Keywords: Hacking, Vulnerabilities, Dummy request, Virus, Server monitoring

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Authors: Mahsa Habibzadeh

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The function is defined as the process of systematic and systematic measurement of the components of how each task is performed and determining their potential for improvement in accordance with the specific standards of each component. Hence, evaluation is the basis for the improvement of organizations' functional excellence and the move towards performance excellence depends on performance improvement planning. Because of the past two decades, the public sector system has undergone dramatic changes. The purpose of such developments is often to overcome the barriers of the bureaucratic system, which impedes the efficient use of limited resources. Implementing widespread changes in the public sector of developed and even developing countries has led the process of developments to be addressed by many researchers. In this regard, the present paper has been carried out with the approach of analyzing the components that affect the performance of the public sector in Iran. To achieve this goal, indicators that affect the performance of the public sector and the factors affecting the improvement of its accountability have been identified. The research method in this research is descriptive and analytical. A statistical population of 120 people consists of managers and employees of the public sector in Iran. The questionnaires were distributed among them and analyzed using SPSS and LISREL software. The obtained results indicate that the results of the research findings show that between responsibilities there is a significant relationship between participation of managers and employees, legality, justice and transparency of specialty and competency, participation in public sector functions. Also, the significant coefficient for the liability variable is 3.31 for justice 2.89 for transparency 1.40 for legality of 2.27 for specialty and competence 2.13 and 5.17 for participation 5.17. Implementing indicators that affect the performance of the public sector can lead to satisfaction of the audience.

Keywords: performance, accountability system, public sector, components

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Authors: Raneem Osama Salem, Ayesha Nuzhat, Fatimah Nasser Al Shehri, Nasser Al Hamdan

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Background: Recently, most medical schools around the globe are using simulation for teaching and assessing students’ clinical skills and competence. There are many obstacles that could face students and faculty when simulation sessions are introduced into undergraduate curriculum. Objective: The aim of this study is to obtain the opinion of undergraduate medical students and our faculty regarding the role of simulation in undergraduate curriculum, the simulation modalities used, and perceived barriers in implementing stimulation sessions. Methods: To address the role of simulation, modalities used, and perceived challenges to implementation of simulation sessions, a self-administered pilot tested questionnaire with 18 items using a 5 point Likert scale was distributed. Participants included undergraduate male medical students (n=125) and female students (n=70) as well as the faculty members (n=14). Result: Various learning outcomes are achieved and improved through the technology enhanced simulation sessions such as communication skills, diagnostic skills, procedural skills, self-confidence, and integration of basic and clinical sciences. The use of high fidelity simulators, simulated patients and task trainers was more desirable by our students and faculty for teaching and learning as well as an evaluation tool. According to most of the students,' institutional support in terms of resources, staff and duration of sessions was adequate. However, motivation to participate in the sessions and provision of adequate feedback by the staff was a constraint. Conclusion: The use of simulation laboratory is of great benefit to the students and a great teaching tool for the staff to ensure students learning of the various skills.

Keywords: simulators, medical students, skills, simulated patients, performance, challenges, skill laboratory

Procedia PDF Downloads 381

Authors: Wan-Ling Chang, Hsiu-Ju Huang, Jui-Hsiu Chang

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In 2004, the Ministry of Foreign Affairs Taiwan launched ‘An Arrangement on Working Holiday Scheme’ with 15 countries including New Zealand, Japan, Canada, Germany, South Korea, Britain, Australia and others. The aim of the scheme is to allow young people to work and study English or other foreign languages. Each year, there are 30,000 Taiwanese youths applied for participating in the working holiday schemes. However, frequent accidents could cause huge medical expenses and post-delivery fee, which are usually unaffordable for most families. Therefore, this study explored the relationship among perceived risk toward working holiday, insurance product knowledge, brand image and insurance purchase intention for Taiwanese youths who plan to apply for working holiday. A survey questionnaire was distributed for data collection. A total of 316 questionnaires were collected for data analyzed. Data were analyzed using descriptive statistics, independent samples T-test, one-way ANOVA, correlation analysis, regression analysis and hierarchical regression methods of analysis and hypothesis testing. The results of this research indicate that perceived risk has a negative influence on insurance purchase intention. On the opposite, product knowledge has brand image has a positive influence on the insurance purchase intention. According to the mentioned results, practical implications were further addressed for insurance companies when developing a future marketing plan.

Keywords: insurance product knowledges, insurance purchase intention, perceived risk, working holiday

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Authors: Eugene Mashapa

Abstract:

The age of information has witnessed countless unprecedented technological developments, which signal the articulation of succinct technological capabilities that can match these cutting-edge technological trends. These changes have impacted patterns in the production, distribution, and consumption of media content, a space that the Film and Publication Board (FPB) is concerned with. Consequently, the FPB is keen to understand the nature and impact of these technological changes on media content regulation. This exploratory study sought to investigate how content regulators in high and middle-income economies have adapted to the changes in this space, seeking insights into innovations, technological and operational, that facilitate continued relevance during this fast-changing environment. The study is aimed at developing recommendations that could assist and inform the organisation in regulating media content as it evolves. Thus, the overall research strategy in this analysis is applied research, and the analytical model adopted is a mixed research design guided by both qualitative and quantitative research instruments. It was revealed in the study that the FPB was significantly impacted by the unprecedented technological advancements in the media regulation space. Additionally, there exists a need for the FPB to understand the current and future penetrations of 4IR technology in the industry and its impact on media governance and policy implementation. This will range from reskilling officials to align with the technological skills to developing technological innovations as well as adopting co-regulatory or self-regulatory arrangements together with content distributors, where more content is distributed in higher volumes and with increased frequency. Importantly, initiating an interactive learning process for both FPB employees and the general public can assist the regulator and improve FPB’s operational efficiency and effectiveness.

Keywords: media, regulation, technology, film and publications board

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Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

Abstract:

Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition

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Authors: M A. Habit, S. A. Syed-Sahil, A. Bahari

Abstract:

Abstract—Laser cutting is a method of manufacturing process that uses laser in order to cut materials. It provides and ensures extreme accuracy which has a clean cut effect, CO2 laser dominate this application due to their good- quality beam combined with high output power. It comes with a small scale and it has a limitation in cutting sizes of materials, therefore it is more appropriate for custom- made products. The same laser cutting machine is also capable in cutting fine material such as fine silk, cotton, leather, polyester, etc. Lack of explorations and knowledge besides being unaware about this technology had caused many of the designers not to use this laser cutting method in their collections. The objectives of this study are: 1) To identify the potential of laser cutting technique in Custom-Made Garments for men’s casual wear: 2) To experiment the laser cutting technique in custom made garments: 3) To offer guidelines and formula for men’s custom- made casualwear designs with aesthetic value. In order to achieve the objectives, this research has been conducted by using mixed methods which are interviews with two (2) local experts in the apparel manufacturing industries and interviews via telephone with five (5) local respondents who are local emerging fashion designers, the questionnaires were distributed to one hundred (100) respondents around Klang Valley, in order to gain the information about their understanding and awareness regarding laser cutting technology. The experiment was conducted by using natural and man- made fibers. As a conclusion, all of the objectives had been achieved in producing custom-made men’s casualwear and with the production of these attires it will help to educate and enhance the innovation in fine technology. Therefore, there will be a good linkage and collaboration between the design experts and the manufacturing companies.

Keywords: custom-made, fashion, laser cut, men’s wear

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Authors: Shishir Lamichhane, Saurav Dulal, Bibek Gautam, Madan Thapa Magar, Indraman Tamrakar

Abstract:

Renewable energies such as wind turbines and solar photovoltaic have gained significance as a result of global environmental pollution and energy crises. These sources of energy are converted into electrical energy and delivered to end-users through the utility system. As a result of the widespread use of power electronics-based grid-interfacing technologies to accommodate renewable sources of energy, the prevalence of converters has expanded as well. As a result, the power system's rotating inertia is decreasing, endangering the utility grid's stability. The use of Virtual Synchronous Machine (VSM) technology has been proposed to overcome the grid stability problem due to low rotating inertia. The grid-connected inverter used in VSM can be controlled to emulate inertia, which replicates the external features of a synchronous generator. As a result, the rotating inertia is increased to support the power system's stability. A power angle control strategy is proposed in this paper and its model is simulated in MATLAB/Simulink to study the effects of parameter disturbances on the active power and frequency for a VSM. The system consists of a synchronous generator, which is modeled in such a way that the frequency drops to an unacceptable region during transient conditions due to a lack of inertia when VSM is not used. Then, the suggested model incorporating VSM emulates rotating inertia, injecting a controllable amount of energy into the grid during frequency transients to enhance transient stability.

Keywords: damping constant, inertia–constant, ROCOF, transient stability, distributed sources

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

Abstract:

The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

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Authors: Luiz Drude de Lacerda, Kevin Luiz Cordeiro Ferrer do Carmo, Victor Lacerda Moura, Rayone Wesley Santos de Oliveira, Moisés Fernandes Bezerra

Abstract:

Fish is a food source well recognized for its health benefits. However, the consumption of fish, especially carnivorous species, is the main path of human exposure to Hg, a widely distributed pollutant on the planet and that accumulates along food chains. Studies on the impacts on public health by fish intake show existing toxic risks even when at low concentrations. This study quantifies, for the first time, the concentrations of Hg in muscle tissue of the nine most commercialized fish species in the fish market of São Luís (MA) in north Brazil and estimates the consequent human exposure through consumption. Concentrations varied according to trophic level, with the highest found in the larger carnivorous species; the Yellow hake (Cynoscion acoupa) (296.4 ± 241.2 ng/g w.w) and the Atlantic croaker (Micropogonias undulatus) (262.8 ± 89.1 ng/g w.w.), whereas the lowest concentrations were recorded in iliophagous Mullets (Mugil curema) (20.5 ± 9.6 ng/g w.w.). Significant correlations were observed between Hg concentrations and individual length in only two species: the Flaming catfish (Bagre marinus) and the Atlantic bumper (Chloroscombrus crysurus). Given the relatively uniform size of individuals of the other species and/or the small number of samples, this relationship was not found for the other species. The estimated risk coefficients, despite the relatively low concentrations of Hg, suggest that yellow hake and Whitemouth croaker (Micropogonias furnieri), fish most consumed by the local population, present some risk to human health (> 1) HQ and THQ, depending on the frequency of their consumption.

Keywords: contamination, fish, human exposure, risk assessment

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Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

Abstract:

In this paper, we propose an approach of detecting the behavior of the viewers of a TV program in a non-controlled environment. The experiment we propose is based on the use of three types of connected objects (smartphone, smart watch, and a connected remote control). 23 participants were observed while watching their TV programs during three phases: before, during and after watching a TV program. Their behaviors were detected using an approach based on The Dempster Shafer Theory (DST) in two phases. The first phase is to approximate dynamically the mass functions using an approach based on the correlation coefficient. The second phase is to calculate the approximate mass functions. To approximate the mass functions, two approaches have been tested: the first approach was to divide each features data space into cells; each one has a specific probability distribution over the behaviors. The probability distributions were computed statistically (estimated by empirical distribution). The second approach was to predict the TV-viewing behaviors through the use of classifiers algorithms and add uncertainty to the prediction based on the uncertainty of the model. Results showed that mixing the fusion rule with the computation of the initial approximate mass functions using a classifier led to an overall of 96%, 95% and 96% success rate for the first, second and third TV-viewing phase respectively. The results were also compared to those found in the literature. This study aims to anticipate certain actions in order to maintain the attention of TV viewers towards the proposed TV programs with usual connected objects, taking into account the various uncertainties that can be generated.

Keywords: Iot, TV-viewing behaviors identification, automatic classification, unconstrained environment

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Authors: Malinwo Estone Ayikpa

Abstract:

With the rapid increase of grid-connected PV systems over the last decades, genuine challenges have arisen for engineers and professionals of energy field in the planning and operation of existing distribution networks with the integration of new generation sources. However, the conventional distribution network, in its design was not expected to receive other generation outside the main power supply. The tools generally used to analyze the networks become inefficient and cannot take into account all the constraints related to the operation of grid-connected PV systems. Some of these constraints are voltage control difficulty, reverse power flow, and especially voltage unbalance which could be due to the poor distribution of single-phase PV systems in the network. In order to analyze the impact of the connection of small and large number of PV systems to the distribution networks, this paper presents an efficient optimization tool that minimizes voltage unbalance in three-phase distribution networks with active and reactive power injections from the allocation of single-phase and three-phase PV plants. Reactive power can be generated or absorbed using the available capacity and the adjustable power factor of the inverter. Good reduction of voltage unbalance can be achieved by reactive power control of the PV systems. The presented tool is based on the three-phase current injection method and the PV systems are modeled via an equivalent circuit. The primal-dual interior point method is used to obtain the optimal operating points for the systems.

Keywords: Photovoltaic system, Primal-dual interior point method, Three-phase optimal power flow, Voltage unbalance

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Authors: Devanjan Bhattacharya, Jitka Komarkova

Abstract:

The research describes the implementation of a novel and stand-alone system for dynamic hazard warning. The system uses all existing infrastructure already in place like mobile networks, a laptop/PC and the small installation software. The geospatial dataset are the maps of a region which are again frugal. Hence there is no need to invest and it reaches everyone with a mobile. A novel architecture of hazard assessment and warning introduced where major technologies in ICT interfaced to give a unique WebGIS based dynamic real time geohazard warning communication system. A never before architecture introduced for integrating WebGIS with telecommunication technology. Existing technologies interfaced in a novel architectural design to address a neglected domain in a way never done before–through dynamically updatable WebGIS based warning communication. The work publishes new architecture and novelty in addressing hazard warning techniques in sustainable way and user friendly manner. Coupling of hazard zonation and hazard warning procedures into a single system has been shown. Generalized architecture for deciphering a range of geo-hazards has been developed. Hence the developmental work presented here can be summarized as the development of internet-SMS based automated geo-hazard warning communication system; integrating a warning communication system with a hazard evaluation system; interfacing different open-source technologies towards design and development of a warning system; modularization of different technologies towards development of a warning communication system; automated data creation, transformation and dissemination over different interfaces. The architecture of the developed warning system has been functionally automated as well as generalized enough that can be used for any hazard and setup requirement has been kept to a minimum.

Keywords: geospatial, web-based GIS, geohazard, warning system

Procedia PDF Downloads 380