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Evaluation System for Car Engine Performance

Evaluation System for Car Engine Performance

International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Teeravisit Laohapensaeng Roungsan Chaisricharoen Phakphoom Boonyanant School of Information Technology Mae Fah Luang University Chiang Rai Thailand Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Mae Fah Luang University Chiang Rai Thailand

Detailed engine performance measurements and problematic identification are helpful to drivers in reducing communication errors between customer and maintenance operators as well as the cost of operations. Therefore, this study aims to develop an evaluation system for assessing car performance by using an embedded system that consists of an engine performance application and display. It can solve such problems by applying embedded technology in combination with ultra-wideband communication technology that interacted with the Proportional-Integral-Derivative (PID). This wireless communication system resists the interference of the interior engine compartment and processes the retrieval data from PID before transferring it to display as current performance.

关键词： Wireless communication Embedded systems Resists Maintenance engineering Safety Automobiles Engines

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Ontology Model for Automatic Duplication Reducing for Industrial Standard Assessment

Ontology Model for Automatic Duplication Reducing for Indust...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Roungsan Chaisricharoen Wanus Srimaharaj Phakphoom Boonyanant Teeravisit Laohapensaeng Kanchit Pamanee School of Information Technology Mae Fah Luang University Chiang Rai Thailand Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Mae Fah Luang University Chiang Rai Thailand Epsilon Solution & Consultant Co. Ltd. Bangkok Thailand

The entrepreneurs found that data science was redundant and wasteful of management resources in several industry-standard assessments which are duplicated. Due to the lack of advanced technology professionals, the application of highly complex technology is a barrier to implementation. Ontology models and artificial intelligence are effective means of solving such problems. Therefore, this study aims to address the ontological problems that can interpret and rule out complex artificial intelligence issues and ambiguous interpretations. It allows quick and repeatable actions to be performed automatically without an administrator. The proposed ontology model reflects the requirements in the industry-standard while K-Nearest Neighbor (K-NN) algorithm separates the data groups and integrated assessment suite that minimizes the redundancy of requirements from standards.

关键词： Redundancy Digital art Ontologies Media Data science Data models communications technology

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Survey and Experimental Comparison of Machine Learning Models for Motorcycle Detection 5

Survey and Experimental Comparison of Machine Learning Model...

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5th International Conference on Information Technology, InCIT 2020

作者： Jarunakarint, Vattiya Uttama, Surapong Rueangsirarak, Worasak Mobile Device and Human Interface Laboratory School of Information Technology Mae Fah Luang University Chiang Rai Thailand Mobile Device and Human Interface Laboratory Center of Excellence in Ai Emerging Technology School of Information Technology Mae Fah Luang University Chiang Rai Thailand Mobile Device and Human Interface Laboratory Computer and Communication Engineering for Capacity Building Research Unit School of Information Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (纸本)9781728166940

Road accidents are largely related to motorcycles and many cases resulted in severe injuries and fatalities. Automatic detection of motorcycles is considered a useful measure for monitoring evaluating predicting and eventually planning to prevent further motorcycle-related accidents. Many researchers proposed techniques to detect and track vehicles but only few focused on detecting motorcycles. In this paper we reviewed and conducted experiments on 20 different machine learning models with different sizes and views (front rear top) of traffic images to evaluate motorcycle detection's accuracy and time. By setting 60% threshold of accuracy on images of 480270 pixels the best detection model is rfcn-resnet101-coco which provided average accuracy detection time and efficiency at 65.85% 1.91 frames/second and 34.55 respectively. © 2020 IEEE.

关键词： Convolutional neural networks

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Smart Pill Box System for Bipolar Disorder Patients

Smart Pill Box System for Bipolar Disorder Patients

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Jiraporn Kanhasinwattana Narisa Yawila Thanawadee Tithada Chayapol Kamyod School of Information Technology Mae Fah Luang University School of Information Technology Mae Fah Luang University Chiang Rai Thailand Computer and Communication Engineering for Capacity Building Research School of Information Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (数字)9781728163987

ISBN: (纸本)9781728163994

The main purpose of the Smart Pill Box System project is helping patients with bipolar disorder or other illnesses to create their motivation to take medicine. Moreover, caregivers can take care of the patients more conveniently. Patients can use the application to setup the notification time for medication. Then the application will send the preset value to the pillbox system. The application will give an alert when it is time to take the medicine. In addition, when patients pick up the medicine from the pillbox system, they will receive points which will represent by the growing tree. Nevertheless, if the patient does not pick up the medicine within limited time, a notification will be sent to the caregiver to take further actions.

关键词： Mobile applications Digital art Media Telecommunications Information technology Image color analysis Light emitting diodes

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Classification of Elderly Group with Hypertension for Preventing Cardiovascular Disease Complication

Classification of Elderly Group with Hypertension for Preven...

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International Symposium on Wireless Personal Multimedia communications (WPMC)

作者： ChuanHui He Supansa Chaising Punnarumol Temdee School of Information Technology Mae Fah Luang University Chiang Rai Thailand Computer and Communication Engineering for Capacity Building Research Unit School of Information Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (数字)9781728154190

ISBN: (纸本)9781728154206

Nowadays, the amount of elderly people in the world population is increasing dramatically, so their health problem is the main concern. Cardiovascular Disease (CVD) is one of the major health problems for elderly people. Especially, hypertension is the main risk factor causing CVD. Consequently, recognizing the ability to control hypertension is very important for providing appropriate treatment recommendations. Therefore, this paper proposes a classification of an elderly group respecting the controllability of hypertension for preventing CVD complications. Decision tree, artificial neuron network, and K-nearest neighbors are employed and compared for classifying elderly group. Moreover, this paper also aims to find the most suitable classifier for the datasets used in this study. Total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, body mass index, and smoking are considered in this paper as the significant risk factors of hypertension development. All data of these factors are collected from secondary data related to elderly people having hypertension with the number of 748 datasets. Finally, the elderly people are classified into two groups including potential controllable group and potential uncontrollable group. The results show that the decision tree provides the highest accuracy of classification for this dataset with 99.11%.

关键词： Senior citizens Hypertension Diseases Discrete cosine transforms Heart Blood Controllability

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International Symposium on Wireless Personal Multimedia communications (WPMC)

作者： Worasak Rueangsirarak Teeravisit Laohapensaeng Suppakarn Chansareewittaya Anusorn Yodjaiphet Computer and Communication Engineering for Capacity Building Research Unit School of Information Technology Mae Fah Luang University Chiang Rai Thailand Department of Electrical Engineering Faculty of Engineering Rajamangala University of Technology Chiang Rai Lanna ChiangRai Thailand

ISBN: (数字)9781728154190

ISBN: (纸本)9781728154206

The k-nearest neighbor algorithm is one of the basic and simple classification algorithms that share a common limitation of the algorithm which requires more computation cost when the size of training data is enlarged. To solve this problem, a new method applied to the cosine similarity for reducing the size of the training data set is proposed. This method reduces the data points that close to a decision boundary and retains the important points which affect classification accuracy. For the data far from the decision boundary and not affect the classification, these points will be removed from the training data set. The proposed method is evaluated its accuracy and reduction performance on the state of the art mechanisms, categorized as prototype selection algorithms. The 20 real-world data set are used to evaluate the proposed method. The experimental results are compared with 21 existing methods. As a result, our proposed method performs the best with 89.95% accuracy but has only a fair reduction ratio, when compared to other methods.

关键词： Prototypes Classification algorithms Training data Training buildings Information technology Clustering algorithms

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Survey and Experimental Comparison of Machine Learning Models for Motorcycle Detection

Survey and Experimental Comparison of Machine Learning Model...

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International Conference on Information Technology (INCIT)

作者： Vattiya Jarunakarint Surapong Uttama Worasak Rueangsirarak Mobile Device and Human Interface Laboratory School of Information Technology Mae Fah Luang University Chiang Rai Thailand Mobile Device and Human Interface Laboratory Center of Excellence in AI & Emerging Technology School of Information Technology Mae Fah Luang University Chiang Rai Thailand Mobile Device and Human Interface Laboratory Computer and Communication Engineering for Capacity Building Research Unit School of Information Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (数字)9781728166940

ISBN: (纸本)9781728193212

Road accidents are largely related to motorcycles and many cases resulted in severe injuries and fatalities. Automatic detection of motorcycles is considered a useful measure for monitoring evaluating predicting and eventually planning to prevent further motorcycle-related accidents. Many researchers proposed techniques to detect and track vehicles but only few focused on detecting motorcycles. In this paper we reviewed and conducted experiments on 20 different machine learning models with different sizes and views (front rear top) of traffic images to evaluate motorcycle detection's accuracy and time. By setting 60% threshold of accuracy on images of 480×270 pixels the best detection model is rfcn_resnet101_coco which provided average accuracy detection time and efficiency at 65.85% 1.91 frames/second and 34.55 respectively.

关键词： Integrated circuits Roads Indium tin oxide Automobiles Radio frequency Optical imaging Machine learning

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Classification of Thai Elderly People Based on Control Ability of Sugar Consumption

Classification of Thai Elderly People Based on Control Abili...

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International Symposium on Wireless Personal Multimedia communications (WPMC)

作者： Punnarumol Temdee ChuanHui He Marzia Hoque Tania Computer and Communication Engineering for Capacity Building Research Unit Mae Fah Luang University Chiang Rai Thailand School of Information Technology Mae Fah Luang University Chiang Rai Thailand Medical Technology Research Center Anglia Ruskin University Chelmsford United Kingdom

ISBN: (数字)9781728154190

ISBN: (纸本)9781728154206

Nowadays, the number of Thai elders is rapidly increasing among world elderly population, how to keep their health is a major concern. Cardiovascular Diseases (CVDs) which are severe diseases for Thai have higher mortality than cancers, and elderly people have a higher possibility to predispose CVDs. Hence, the risk factors for CVDs should be addressed. Obesity, as one of the risk factors of CVDs, seriously affects Thai elders' wellbeing; excessive sugar consumption is a way leading to overweight and obesity. The amount of consumed sugar by Thai is much higher than the standard sugar consumption, and it also could cause many other diseases. Therefore, this paper proposes a classification method for the elderly group who have the potential to control their blood sugar in order to prevent them from sugar overconsumption. This paper explored machine learning algorithms to find an appropriate classification method for elderly data. Artificial neuron network and K-nearest neighbors are applied for classifying elderly groups. Glycated hemoglobin (HbA1c) and fasting plasma glucose (FPG) are the noninvasive measurements of evaluating blood sugar, based on the two measurements, the 242 data from 121 elderly people are divided into two groups which are controllable group and uncontrollable group. The result indicates that the artificial neuron network is more suitable for the dataset with 70.59% accuracy as compared to the accuracy of K-nearest neighbors.

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National identity predicts public health support during a global pandemic (vol 13, 517, 2022)

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NATURE communicationS 2022年第1期13卷 517-517页

作者： Van Bavel, Jay J. Cichocka, Aleksandra Capraro, Valerio Sjastad, Hallgeir Nezlek, John B. Pavlovic, Tomislav Alfano, Mark Gelfand, Michele J. Azevedo, Flavio Birtel, Michele D. Cislak, Aleksandra Lockwood, Patricia L. Ross, Robert Malcolm Abts, Koen Agadullina, Elena Aruta, John Jamir Benzon Besharati, Sahba Nomvula Bor, Alexander Choma, Becky L. Crabtree, Charles David Cunningham, William A. De, Koustav Ejaz, Waqas Elbaek, Christian T. Findor, Andrej Flichtentrei, Daniel Franc, Renata Gjoneska, Biljana Gruber, June Gualda, Estrella Horiuchi, Yusaku Huynh, Toan Luu Duc Ibanez, Agustin Imran, Mostak Ahamed Israelashvili, Jacob Jasko, Katarzyna Kantorowicz, Jaroslaw Kantorowicz-Reznichenko, Elena Krouwel, Andre Laakasuo, Michael Lamm, Claus Leygue, Caroline Lin, Ming-Jen Mansoor, Mohammad Sabbir Marie, Antoine Mayiwar, Lewend Mazepus, Honorata McHugh, Cillian Minda, John Paul Mitkidis, Panagiotis Olsson, Andreas Otterbring, Tobias Packer, Dominic J. Perry, Anat Petersen, Michael Bang Puthillam, Arathy Riano-Moreno, Julian C. Rothmund, Tobias Santamaria-Garcia, Hernando Schmid, Petra C. Stoyanov, Drozdstoy Tewari, Shruti Todosijevic, Bojan Tsakiris, Manos Tung, Hans H. Umbres, Radu G. Vanags, Edmunds Vlasceanu, Madalina Vonasch, Andrew Yucel, Meltem Zhang, Yucheng Abad, Mohcine Adler, Eli Akrawi, Narin Mdarhri, Hamza Alaoui Amara, Hanane Amodio, David M. Antazo, Benedict G. Apps, Matthew Ay, F. Ceren Ba, Mouhamadou Hady Barbosa, Sergio Bastian, Brock Berg, Anton Bernal-Zarate, Maria P. Bernstein, Michael Bialek, Michal Bilancini, Ennio Bogatyreva, Natalia Boncinelli, Leonardo Booth, Jonathan E. Borau, Sylvie Buchel, Ondrej Cameron, C. Daryl Carvalho, Chrissie F. Celadin, Tatiana Cerami, Chiara Chalise, Hom Nath Cheng, Xiaojun Cian, Luca Cockcroft, Kate Conway, Jane Cordoba-Delgado, Mateo Andres Crespi, Chiara Crouzevialle, Marie Cutler, Jo Cypryanska, Marzena Dabrowska, Justyna Daniels, Michael A. Davis, Victoria H. Dayley, Pamala N. Delouvee, Sylvain Denkovski, Ognjan Dezecache, Guillaume Dhaliwal, Nathan A. Diato, Department of Psychology and Neural Science New York University New York NY USA School of Psychology University of Kent Canterbury England Department of Economics Middlesex University London London England Department of Strategy and Management Norwegian School of Economics Bergen Norway SWPS University of Social Sciences and Humanities Poznań Poland Department of Psychological Sciences College of William and Mary Williamsburg VA USA Institute of Social Sciences Ivo Pilar Zagreb Croatia Department of Philosophy Macquarie University Sydney NSW Australia Stanford Graduate School of Business Stanford University Stanford CA USA Institute of Communication Science Friedrich-Schiller University Jena Jena Germany School of Human Sciences Institute for Lifecourse Development University of Greenwich London England Department of Experimental Psychology University of Oxford Oxford England Center for Human Brain Health School of Psychology University of Birmingham Birmingham England Department of Psychology Macquarie University Sydney NSW Australia KU Leuven Leuven Belgium National Research University Higher School of Economics (HSE) Moscow Russia De La Salle University Manila Philippines Department of Psychology University of the Witwatersrand Johannesburg South Africa Department of Political Science Aarhus University Aarhus Denmark X University Toronto Canada Department of Government Dartmouth College Hanover NH USA Department of Psychology University of Toronto Toronto ON Canada Gatton College of Business and Economics University of Kentucky Lexington KY USA Department of Mass Communication National University of Science and Technology (NUST) Islamabad Pakistan Department of Management Aarhus University Aarhus Denmark Faculty of Social and Economic Sciences Comenius University Bratislava Slovakia IntraMed Buenos Aires Argentina Macedonian Academy of Sciences and Arts North Macedonia Republic of North Macedonia University of Colorado Boulder Bould

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics. Understanding collective behaviour is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviours and support for public health policies in the context of the pandemic.

关键词： Viral infection Human behaviour

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Social and moral psychology of COVID-19 across 69 countries

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Scientific data 2023年第1期10卷 272页

作者： Flavio Azevedo Tomislav Pavlović Gabriel G Rêgo F Ceren Ay Biljana Gjoneska Tom W Etienne Robert M Ross Philipp Schönegger Julián C Riaño-Moreno Aleksandra Cichocka Valerio Capraro Luca Cian Chiara Longoni Ho Fai Chan Jay J Van Bavel Hallgeir Sjåstad John B Nezlek Mark Alfano Michele J Gelfand Michèle D Birtel Aleksandra Cislak Patricia L Lockwood Koen Abts Elena Agadullina John Jamir Benzon Aruta Sahba Nomvula Besharati Alexander Bor Becky L Choma Charles David Crabtree William A Cunningham Koustav De Waqas Ejaz Christian T Elbaek Andrej Findor Daniel Flichtentrei Renata Franc June Gruber Estrella Gualda Yusaku Horiuchi Toan Luu Duc Huynh Agustin Ibanez Mostak Ahamed Imran Jacob Israelashvili Katarzyna Jasko Jaroslaw Kantorowicz Elena Kantorowicz-Reznichenko André Krouwel Michael Laakasuo Claus Lamm Caroline Leygue Ming-Jen Lin Mohammad Sabbir Mansoor Antoine Marie Lewend Mayiwar Honorata Mazepus Cillian McHugh John Paul Minda Panagiotis Mitkidis Andreas Olsson Tobias Otterbring Dominic J Packer Anat Perry Michael Bang Petersen Arathy Puthillam Tobias Rothmund Hernando Santamaría-García Petra C Schmid Drozdstoy Stoyanov Shruti Tewari Bojan Todosijević Manos Tsakiris Hans H Tung Radu G Umbres Edmunds Vanags Madalina Vlasceanu Andrew Vonasch Meltem Yucel Yucheng Zhang Mohcine Abad Eli Adler Narin Akrawi Hamza Alaoui Mdarhri Hanane Amara David M Amodio Benedict G Antazo Matthew Apps Mouhamadou Hady Ba Sergio Barbosa Brock Bastian Anton Berg Maria P Bernal-Zárate Michael Bernstein Michał Białek Ennio Bilancini Natalia Bogatyreva Leonardo Boncinelli Jonathan E Booth Sylvie Borau Ondrej Buchel C Daryl Cameron Chrissie F Carvalho Tatiana Celadin Chiara Cerami Hom Nath Chalise Xiaojun Cheng Kate Cockcroft Jane Conway Mateo Andres Córdoba-Delgado Chiara Crespi Marie Crouzevialle Jo Cutler Marzena Cypryańska Justyna Dabrowska Michael A Daniels Victoria H Davis Pamala N Dayley Sylvain Delouvée Ognjan Denkovski Guillaume Dezecache Nathan A Dhaliwal Alelie B Diato Roberto Di Paolo Marianna Drosinou Uwe Dulleck Jānis Ekmanis Department of Psychology University of Cambridge Cambridge England. fa441@cam.ac.uk. Institute of Communication Science Friedrich-Schiller University Jena Jena Germany. fa441@cam.ac.uk. Institute of Social Sciences Ivo Pilar Zagreb Croatia. Social and Cognitive Neuroscience Laboratory Mackenzie Presbyterian University São Paulo Brazil. Department of Economics Norwegian School of Economics Bergen Norway. Telenor Research Oslo Norway. Macedonian Academy of Sciences and Arts Skopje Republic of North Macedonia. Kieskompas - Election Compass Amsterdam Netherlands. Department of Political Science & Annenberg School for Communication University of Pennsylvania Philadelphia PA USA. Department of Psychology Macquarie University Sydney NSW Australia. Department of Philosophy University of St Andrews St Andrews Scotland. School of Economics and Finance University of St Andrews St Andrews Scotland. Medicine Faculty Cooperative University of Colombia Villavicencio Colombia. Department of Bioethics El Bosque University Bogotá Colombia. School of Psychology University of Kent Canterbury England. Department of Economics Middlesex University London London England. Darden School of Business University of Virginia Charlottesville VA USA. Questrom School of Business Boston University Boston MA USA. School of Economics and Finance Queensland University of Technology Brisbane QLD Australia. Center for Behavioural Economics Society and Technology Queensland University of Technology Brisbane QLD Australia. Department of Psychology and Neural Science New York University New York NY USA. Department of Strategy and Management Norwegian School of Economics Bergen Norway. SWPS University of Social Sciences and Humanities Warsaw Poland. Department of Psychological Sciences College of William and Mary Williamsburg VA USA. Department of Philosophy Macquarie University Sydney NSW Australia. Stanford Graduate School of Business Stanford University Stanford CA USA.

The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.

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