Pub Date : 2016-10-22DOI: 10.1109/I2CACIS.2016.7885316
N. Ahamed, Z. Yusof, Z. Hamedon, M. Rabbi, Tasriva Sikandar, R. Palaniappan, Md. Asraf Ali, S. M. Rahman, K. Sundaraj
An intelligent drilling system can be commercially very profitable in terms of reduction in crude material and labor involvement. The use of fuzzy logic based controller in the intelligent cutting and drilling operations has become a popular practice in the ever growing manufacturing industry. In this paper, a fuzzy logic controller has been designed to select the cutting parameter more precisely for the drilling operation. Specifically, different input criterion of machining parameters are considered such as the tool and material hardness, the diameter of drilling hole and the flow rate of cutting fluid. Unlike the existing fuzzy logic based methods, which use only two input parameters, the proposed system utilizes more input parameters to provide spindle speed and feed rate information more precisely for the intelligent drilling operation.
{"title":"Fuzzy logic controller design for intelligent drilling system","authors":"N. Ahamed, Z. Yusof, Z. Hamedon, M. Rabbi, Tasriva Sikandar, R. Palaniappan, Md. Asraf Ali, S. M. Rahman, K. Sundaraj","doi":"10.1109/I2CACIS.2016.7885316","DOIUrl":"https://doi.org/10.1109/I2CACIS.2016.7885316","url":null,"abstract":"An intelligent drilling system can be commercially very profitable in terms of reduction in crude material and labor involvement. The use of fuzzy logic based controller in the intelligent cutting and drilling operations has become a popular practice in the ever growing manufacturing industry. In this paper, a fuzzy logic controller has been designed to select the cutting parameter more precisely for the drilling operation. Specifically, different input criterion of machining parameters are considered such as the tool and material hardness, the diameter of drilling hole and the flow rate of cutting fluid. Unlike the existing fuzzy logic based methods, which use only two input parameters, the proposed system utilizes more input parameters to provide spindle speed and feed rate information more precisely for the intelligent drilling operation.","PeriodicalId":399080,"journal":{"name":"2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125359463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/I2CACIS.2016.7885314
N. Ahamed, Z. Taha, I. Khairuddin, M. Rabbi, Tasriva Sikandar, R. Palaniappan, Md. Asraf Ali, S. M. Rahman, K. Sundaraj
In this paper, a fuzzy inference system has been developed for automatic tea making process. The system takes five inputs and gives two output which determines the grade of black tea and milk tea. Specifically, the proposed system considers five important characteristics of hot tea beverage such as water temperature, sugar, milk, brewing time and tea leaves quantity for grading the standard of the drink according to the consumer's requirement. Both black tea and milk tea can be rated with a grade based on the human expert judgment which is according to the taste and aroma of the tea. This automatic tea making system can let the users choose their preferred type of tea without figuring out the complicated process to making a cup of hot tea beverage.
{"title":"Development of fuzzy inference system for automatic tea making","authors":"N. Ahamed, Z. Taha, I. Khairuddin, M. Rabbi, Tasriva Sikandar, R. Palaniappan, Md. Asraf Ali, S. M. Rahman, K. Sundaraj","doi":"10.1109/I2CACIS.2016.7885314","DOIUrl":"https://doi.org/10.1109/I2CACIS.2016.7885314","url":null,"abstract":"In this paper, a fuzzy inference system has been developed for automatic tea making process. The system takes five inputs and gives two output which determines the grade of black tea and milk tea. Specifically, the proposed system considers five important characteristics of hot tea beverage such as water temperature, sugar, milk, brewing time and tea leaves quantity for grading the standard of the drink according to the consumer's requirement. Both black tea and milk tea can be rated with a grade based on the human expert judgment which is according to the taste and aroma of the tea. This automatic tea making system can let the users choose their preferred type of tea without figuring out the complicated process to making a cup of hot tea beverage.","PeriodicalId":399080,"journal":{"name":"2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126187627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/I2CACIS.2016.7885284
Nurul Fadhilah Binti Ismail, Z. Sharif
The Quran is a script consisting of 114 Surah. The Quran is known to have positive effects on human, aids the stress healing process. Each Surah is provided for a different understanding and meaning of its own. This paper investigates the subject's reaction towards listening to two different Surahs. The electroencephalogram (EEG) machine was used to observe and record the subject brain activity. By using the EEG, brain signals of the subject taken with 2 sessions. The first session the subject listens Surah Al-Mulk, while second session the subject listens Surah Al-Hasyr. Results indicate that on average the subjects are more relax while listening to Surah Al-Hasyr compared to Surah Al-Mulk. In addition, the subject's brain induced alpha right brainwaves the both of Surahs.
{"title":"The comparison between listening to Surah Al-Mulk and Surah Al-Hasyr using EEG","authors":"Nurul Fadhilah Binti Ismail, Z. Sharif","doi":"10.1109/I2CACIS.2016.7885284","DOIUrl":"https://doi.org/10.1109/I2CACIS.2016.7885284","url":null,"abstract":"The Quran is a script consisting of 114 Surah. The Quran is known to have positive effects on human, aids the stress healing process. Each Surah is provided for a different understanding and meaning of its own. This paper investigates the subject's reaction towards listening to two different Surahs. The electroencephalogram (EEG) machine was used to observe and record the subject brain activity. By using the EEG, brain signals of the subject taken with 2 sessions. The first session the subject listens Surah Al-Mulk, while second session the subject listens Surah Al-Hasyr. Results indicate that on average the subjects are more relax while listening to Surah Al-Hasyr compared to Surah Al-Mulk. In addition, the subject's brain induced alpha right brainwaves the both of Surahs.","PeriodicalId":399080,"journal":{"name":"2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124235686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/I2CACIS.2016.7885281
C. Jing, Dwi Pebrianti
Fault Detection and Identification (FDI) monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A Quadrotor robot is used to represent a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. This dynamic model is based on the first principles of the Quadrotor: Propeller model and its force as well as moments generation. The Quadrotor controller is designed such that it can be controlled using both the attitude control (inner loop) and position control (outer loop). PD controller used the Phi, Theta, Psi, x, y and z as a reference to adjust the attitude and position of the Quadrotor. The proposed method for the fault identification is a hybrid technique which combined both the Kalman filter and Artificial Neural Network (ANN). Kalman filter recognized data from the system sensors and can indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. The information will then be fed to Artificial Neural Network (ANN), which consist of a bank of parameter estimation that generates the failure state. This Artificial Neural Network (ANN) is an algorithm that is used to determine the type of fault and the severity level as well as isolate the fault from the system. The ANN is designed based on the back-propagation technique so that it can be trained to generate output based on the data. Based on the result comparison of the residual signal before filter and after filter, the algorithm of FDI is able to identify parts of the system that experience failure and the fault can be solved immediately allowing the Quadrotor to be back to its normal operation. It is also capable to acknowledge the user on the parts of the system which experienced failure and can provide user with the best instructions or solutions for the situation. It is also capable to cater a safe landing.
{"title":"Fault detection and identification in Quadrotor system (Quadrotor robot)","authors":"C. Jing, Dwi Pebrianti","doi":"10.1109/I2CACIS.2016.7885281","DOIUrl":"https://doi.org/10.1109/I2CACIS.2016.7885281","url":null,"abstract":"Fault Detection and Identification (FDI) monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A Quadrotor robot is used to represent a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. This dynamic model is based on the first principles of the Quadrotor: Propeller model and its force as well as moments generation. The Quadrotor controller is designed such that it can be controlled using both the attitude control (inner loop) and position control (outer loop). PD controller used the Phi, Theta, Psi, x, y and z as a reference to adjust the attitude and position of the Quadrotor. The proposed method for the fault identification is a hybrid technique which combined both the Kalman filter and Artificial Neural Network (ANN). Kalman filter recognized data from the system sensors and can indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. The information will then be fed to Artificial Neural Network (ANN), which consist of a bank of parameter estimation that generates the failure state. This Artificial Neural Network (ANN) is an algorithm that is used to determine the type of fault and the severity level as well as isolate the fault from the system. The ANN is designed based on the back-propagation technique so that it can be trained to generate output based on the data. Based on the result comparison of the residual signal before filter and after filter, the algorithm of FDI is able to identify parts of the system that experience failure and the fault can be solved immediately allowing the Quadrotor to be back to its normal operation. It is also capable to acknowledge the user on the parts of the system which experienced failure and can provide user with the best instructions or solutions for the situation. It is also capable to cater a safe landing.","PeriodicalId":399080,"journal":{"name":"2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125808350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/I2CACIS.2016.7885291
F. Yakub, P. Muhamad, H. T. Toh, N. Fawazi, S. Sarip, Mohamed Sukri Mat Ali, S. A. Zaki
The research presented in this paper is carried out to investigate the performance of a suspension systems either an active or passive type. Controllers that are used in this study are proposed fuzzy logic controller and proportional integral derivative controller as a benchmarking comparison. The simulations in this research have been carried out using Simulink of MATLAB. The parameters in the simulation model for the suspension system under study include car body mass, wheel mass, spring and damping elements of shock absorber, and tire. The block model of the suspension system has been designed to represent the equation of motion of the sedan car suspension system. The road disturbance for the active suspension system is modelled in two different ways, namely, unit step input signal and sine wave input signal. The simulation results indicate that fuzzy logic control of an active car suspension system has better performance compared to the passive system.
{"title":"Enhancing vehicle ride comfort through intelligent based control","authors":"F. Yakub, P. Muhamad, H. T. Toh, N. Fawazi, S. Sarip, Mohamed Sukri Mat Ali, S. A. Zaki","doi":"10.1109/I2CACIS.2016.7885291","DOIUrl":"https://doi.org/10.1109/I2CACIS.2016.7885291","url":null,"abstract":"The research presented in this paper is carried out to investigate the performance of a suspension systems either an active or passive type. Controllers that are used in this study are proposed fuzzy logic controller and proportional integral derivative controller as a benchmarking comparison. The simulations in this research have been carried out using Simulink of MATLAB. The parameters in the simulation model for the suspension system under study include car body mass, wheel mass, spring and damping elements of shock absorber, and tire. The block model of the suspension system has been designed to represent the equation of motion of the sedan car suspension system. The road disturbance for the active suspension system is modelled in two different ways, namely, unit step input signal and sine wave input signal. The simulation results indicate that fuzzy logic control of an active car suspension system has better performance compared to the passive system.","PeriodicalId":399080,"journal":{"name":"2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131329240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/I2CACIS.2016.7885322
Lindokuhle J. Mpanza, J. Pedro
A tuning mechanism for a sliding mode controller (SMC) used for a 2-DOF hydraulic servo system is proposed. In this paper we aim to develop techniques for optimally tuning the SMC parameters for a system that tracks the vertical displacement and angular orientation of the parallel manipulator. We propose an ant colony optimization (ACO) algorithm to tune four SMC parameters. The performance of ACO is compared to the manually-tuned and genetic algorithm (GA)-tuned SMC. The results from simulation showed that the ACO-SMC performance is comparable to that of GA-SMC, for tracking the heave and the pitch of the system when evaluating tracking error and the actuator action required. The GA-SMC exhibits high frequency chattering, while the ACO-SMC does not. From the simulated results we conclude that, overall, the application of ACO to SMC parameter tuning improves the systems performance.
{"title":"Sliding mode control parameter tuning using ant colony optimization for a 2-DOF hydraulic servo system","authors":"Lindokuhle J. Mpanza, J. Pedro","doi":"10.1109/I2CACIS.2016.7885322","DOIUrl":"https://doi.org/10.1109/I2CACIS.2016.7885322","url":null,"abstract":"A tuning mechanism for a sliding mode controller (SMC) used for a 2-DOF hydraulic servo system is proposed. In this paper we aim to develop techniques for optimally tuning the SMC parameters for a system that tracks the vertical displacement and angular orientation of the parallel manipulator. We propose an ant colony optimization (ACO) algorithm to tune four SMC parameters. The performance of ACO is compared to the manually-tuned and genetic algorithm (GA)-tuned SMC. The results from simulation showed that the ACO-SMC performance is comparable to that of GA-SMC, for tracking the heave and the pitch of the system when evaluating tracking error and the actuator action required. The GA-SMC exhibits high frequency chattering, while the ACO-SMC does not. From the simulated results we conclude that, overall, the application of ACO to SMC parameter tuning improves the systems performance.","PeriodicalId":399080,"journal":{"name":"2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"14 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120856715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/I2CACIS.2016.7885295
S. Mohamad, A. A. Jamaludin, K. Isa
This project creates a speech semantic recognition system that can be applied in the assistive robotic application by using the meaning of speech for people who suffer a permanent disability that cannot move around normally. The user interface of this speech semantic recognition system of this project are capable to receive the speech input of the user and an application interface transfers the input content of the user to an application. This speech semantic recognition system consists of input part that is speech signal. Based on the input of speech signal, the speech recognizer is used to recognize a corresponding word, the word semantic model representing the connection between a semantic database with the meaning of a word and a registered word belonging to a speech recognizer. The speech signal is recognized the word by using speech recognizer then is converted to the corresponding word-semantic model by the feature extraction approach. The converted word-semantic model is stored in the database and trained. The speech recognizer will recognize the speech signal again and notified to the application via comparing with the word-semantic model stored in the database based on feature matching method before send to the application interface.
{"title":"Speech semantic recognition system for an assistive robotic application","authors":"S. Mohamad, A. A. Jamaludin, K. Isa","doi":"10.1109/I2CACIS.2016.7885295","DOIUrl":"https://doi.org/10.1109/I2CACIS.2016.7885295","url":null,"abstract":"This project creates a speech semantic recognition system that can be applied in the assistive robotic application by using the meaning of speech for people who suffer a permanent disability that cannot move around normally. The user interface of this speech semantic recognition system of this project are capable to receive the speech input of the user and an application interface transfers the input content of the user to an application. This speech semantic recognition system consists of input part that is speech signal. Based on the input of speech signal, the speech recognizer is used to recognize a corresponding word, the word semantic model representing the connection between a semantic database with the meaning of a word and a registered word belonging to a speech recognizer. The speech signal is recognized the word by using speech recognizer then is converted to the corresponding word-semantic model by the feature extraction approach. The converted word-semantic model is stored in the database and trained. The speech recognizer will recognize the speech signal again and notified to the application via comparing with the word-semantic model stored in the database based on feature matching method before send to the application interface.","PeriodicalId":399080,"journal":{"name":"2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127194584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/I2CACIS.2016.7885294
Nurliyana Kafli, M. Z. Othman, K. Isa
Environmental monitoring plays an important role in human life, so this project aims to provide a spatial-temporal resolution of data collected inland water storages and lakes through the development of a unique floating platform. Several challenges in monitoring environment especially the accessibility to the site and representativesness of data cause the quality of water and air become worse. Hence, an unsupervised floating platform was developed in order to monitor the environment. The system focused on doing a precision measurement of water quality and air quality. With the help of several sensors that act as an input that is Real-Time Clock, Global Positioning System (GPS) sensor, humidity and temperature sensor, pH sensor and carbon monoxide sensor. Through this sensor, the floating platform collects the data and save it for every 10 minutes to perform real-time data collection. The data are saved into SD card, which consists of time, date, longitude, latitude, carbon monoxide, water pH value, temperature, and humidity. The results obtained then used to evaluate the quality of the air and water of the lake.
{"title":"Unsupervised floating platform for environmental monitoring","authors":"Nurliyana Kafli, M. Z. Othman, K. Isa","doi":"10.1109/I2CACIS.2016.7885294","DOIUrl":"https://doi.org/10.1109/I2CACIS.2016.7885294","url":null,"abstract":"Environmental monitoring plays an important role in human life, so this project aims to provide a spatial-temporal resolution of data collected inland water storages and lakes through the development of a unique floating platform. Several challenges in monitoring environment especially the accessibility to the site and representativesness of data cause the quality of water and air become worse. Hence, an unsupervised floating platform was developed in order to monitor the environment. The system focused on doing a precision measurement of water quality and air quality. With the help of several sensors that act as an input that is Real-Time Clock, Global Positioning System (GPS) sensor, humidity and temperature sensor, pH sensor and carbon monoxide sensor. Through this sensor, the floating platform collects the data and save it for every 10 minutes to perform real-time data collection. The data are saved into SD card, which consists of time, date, longitude, latitude, carbon monoxide, water pH value, temperature, and humidity. The results obtained then used to evaluate the quality of the air and water of the lake.","PeriodicalId":399080,"journal":{"name":"2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127338962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/I2CACIS.2016.7885283
Siti Arpah Ahmad, N. Khalid, M. Taib, H. Taib
Subjective evaluation of the abnormalities in dental images faces a few challenges such as low contrast. The problem arises due to the fix regulation of small X-ray dosage. Thus there are applications of image enhancement on the dental images and it is an acceptable technique to improve image quality and better diagnosis. Low contrast images could hinder the subjective evaluation and contribute to disagreement between the evaluators. This work investigates the performance of original and enhanced dental images towards the disagreement issues of evaluating image quality and detecting dental abnormalities. The abnormalities of interest are periapical radiolucency (PA), widen periodontal ligament space (widen PDLs) and loss of lamina dura (Loss of LD). The work begins with collecting the raw intra-oral dental images. Then sharpening based contrast image enhancement algorithms were applied to the images. After that, the images were evaluated by dentists towards the image quality and the abnormalities mentioned. The disagreements among the evaluators were determined using standard deviation formula. Results show that disagreement issue among the evaluators do exists to the extent of above 90%. Comparing to the original images show that enhanced images are able to slightly reduced the subjective evaluation disagreement in image quality and abnormalities.
{"title":"Sharpening based image enhancement algorithms in reducing the disagreement of medical images subjective evaluation","authors":"Siti Arpah Ahmad, N. Khalid, M. Taib, H. Taib","doi":"10.1109/I2CACIS.2016.7885283","DOIUrl":"https://doi.org/10.1109/I2CACIS.2016.7885283","url":null,"abstract":"Subjective evaluation of the abnormalities in dental images faces a few challenges such as low contrast. The problem arises due to the fix regulation of small X-ray dosage. Thus there are applications of image enhancement on the dental images and it is an acceptable technique to improve image quality and better diagnosis. Low contrast images could hinder the subjective evaluation and contribute to disagreement between the evaluators. This work investigates the performance of original and enhanced dental images towards the disagreement issues of evaluating image quality and detecting dental abnormalities. The abnormalities of interest are periapical radiolucency (PA), widen periodontal ligament space (widen PDLs) and loss of lamina dura (Loss of LD). The work begins with collecting the raw intra-oral dental images. Then sharpening based contrast image enhancement algorithms were applied to the images. After that, the images were evaluated by dentists towards the image quality and the abnormalities mentioned. The disagreements among the evaluators were determined using standard deviation formula. Results show that disagreement issue among the evaluators do exists to the extent of above 90%. Comparing to the original images show that enhanced images are able to slightly reduced the subjective evaluation disagreement in image quality and abnormalities.","PeriodicalId":399080,"journal":{"name":"2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122631583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/I2CACIS.2016.7885312
A. F. M. Sampian, H. Hashim, M. Kamal, N. E. Abdullah, Ummu Raihan Yussuf, N. A. Khairuzzaman, A. F. M. Azmi
This paper presents the findings of Visions System performance for the detection of White root disease infection based on capacitance properties. A number of 100 latex samples representing healthy and white root infected rubber tree is tested for its capacitance value using Prototype Console Unit (PCU) developed. An optimized model for ANN using Levenberg Marquardt was designed. It is found that the hidden layer size of neuron 2 gave the best optimized ANN model with 77% sensitivity, 88% specificity, 82.5% accuracy, and uses 5 numbers of connections. A vision system based on this optimized model is developed and has the performance of 78.34% total accuracy.
{"title":"Vision system for detection of white root disease infection based on capacitance properties","authors":"A. F. M. Sampian, H. Hashim, M. Kamal, N. E. Abdullah, Ummu Raihan Yussuf, N. A. Khairuzzaman, A. F. M. Azmi","doi":"10.1109/I2CACIS.2016.7885312","DOIUrl":"https://doi.org/10.1109/I2CACIS.2016.7885312","url":null,"abstract":"This paper presents the findings of Visions System performance for the detection of White root disease infection based on capacitance properties. A number of 100 latex samples representing healthy and white root infected rubber tree is tested for its capacitance value using Prototype Console Unit (PCU) developed. An optimized model for ANN using Levenberg Marquardt was designed. It is found that the hidden layer size of neuron 2 gave the best optimized ANN model with 77% sensitivity, 88% specificity, 82.5% accuracy, and uses 5 numbers of connections. A vision system based on this optimized model is developed and has the performance of 78.34% total accuracy.","PeriodicalId":399080,"journal":{"name":"2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116524326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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