Pub Date : 1900-01-01DOI: 10.1109/SPC.2013.6735143
Mohd Erwan Mohd Ussdek, S. A. Al Junid, Z. A. Majid, F. N. Osman, Z. Othman
This study attempted to investigate and design a new mechanism in monitoring safety by detecting gas concentration levels to prevent an explosion during welding activities at a high-risk site. The objective of this project was to design and develop a new safety precaution monitoring system during welding activity using a low-cost microcontroller to improve the current practice. The study was conducted by dividing the project into three stages: sensing, controlling, and notification. At the sensing stage, different gas concentration levels have been tested and analyzed to determine the safety range or level. However, the controller part managed the responses based on the detection of gas in the environment. Indicator and secure digital memory have been used to notify and record the activity during the operation. In the environment test, the system starts to trigger when sensing the level produced at 1.416 V, which represents 30% of the gas concentration. Nevertheless, the gas sensor required a 10-second initialized time to stabilize and produce the desired sensing level. At the end of this paper, the system has been successfully designed, developed, and tested at the prototype level.
{"title":"High-sensitivity gas detection and monitoring system for high-risk welding activity","authors":"Mohd Erwan Mohd Ussdek, S. A. Al Junid, Z. A. Majid, F. N. Osman, Z. Othman","doi":"10.1109/SPC.2013.6735143","DOIUrl":"https://doi.org/10.1109/SPC.2013.6735143","url":null,"abstract":"This study attempted to investigate and design a new mechanism in monitoring safety by detecting gas concentration levels to prevent an explosion during welding activities at a high-risk site. The objective of this project was to design and develop a new safety precaution monitoring system during welding activity using a low-cost microcontroller to improve the current practice. The study was conducted by dividing the project into three stages: sensing, controlling, and notification. At the sensing stage, different gas concentration levels have been tested and analyzed to determine the safety range or level. However, the controller part managed the responses based on the detection of gas in the environment. Indicator and secure digital memory have been used to notify and record the activity during the operation. In the environment test, the system starts to trigger when sensing the level produced at 1.416 V, which represents 30% of the gas concentration. Nevertheless, the gas sensor required a 10-second initialized time to stabilize and produce the desired sensing level. At the end of this paper, the system has been successfully designed, developed, and tested at the prototype level.","PeriodicalId":198247,"journal":{"name":"2013 IEEE Conference on Systems, Process & Control (ICSPC)","volume":"291 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116423782","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 : 1900-01-01DOI: 10.1109/SPC.2013.6735133
Ai Kijima, Y. Mitsukura, N. Hamada
This study proposes a method to estimate the direction of arrival (DOA) for multiple sources by using microphone array under aliasing condition. Many conventional DOA estimation methods based on the sparseness of time-frequency (T-F) components of speech signals have to limit the sensor distance because of avoiding spatial aliasing due to the use of phase difference. Here we propose a 3-dimentional DOA or both azimuth and elevation angles estimation method capable to treat even in the presence of spatial aliasing. To cope with this problem, a reliable T-F cell selection and new bandwidth control in the kernel density estimation. In order to verify the effectiveness of the proposed method, experimental simulations have been performed. The simulation results show that the proposed DOA estimation gives accurate estimation even for spatial aliasing cases.
{"title":"DOA estimation by 3-D microphone array in the presence of spatial aliasing","authors":"Ai Kijima, Y. Mitsukura, N. Hamada","doi":"10.1109/SPC.2013.6735133","DOIUrl":"https://doi.org/10.1109/SPC.2013.6735133","url":null,"abstract":"This study proposes a method to estimate the direction of arrival (DOA) for multiple sources by using microphone array under aliasing condition. Many conventional DOA estimation methods based on the sparseness of time-frequency (T-F) components of speech signals have to limit the sensor distance because of avoiding spatial aliasing due to the use of phase difference. Here we propose a 3-dimentional DOA or both azimuth and elevation angles estimation method capable to treat even in the presence of spatial aliasing. To cope with this problem, a reliable T-F cell selection and new bandwidth control in the kernel density estimation. In order to verify the effectiveness of the proposed method, experimental simulations have been performed. The simulation results show that the proposed DOA estimation gives accurate estimation even for spatial aliasing cases.","PeriodicalId":198247,"journal":{"name":"2013 IEEE Conference on Systems, Process & Control (ICSPC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131703867","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 : 1900-01-01DOI: 10.1109/SPC.2013.6735135
F. Ruslan, A. Samad, Zainazlan Md Zain, R. Adnan
Flood water level prediction using system identification technique is still new area for most of the researchers. This is due to the dynamics of the flood water level itself that is often characterized as highly nonlinear. Thus, it is quite a challenging task to represent the flood water level behavioural in mathematical expressions. This paper presents flood water level modelling using MISO (Multiple Input Single Output) ARX (Autoregressive Exogenous Input) structure and cascaded Neural Network model for performance improvement. In this paper, the transfer function relating the input parameters and output parameter was identified with the aid of MISO ARX model. The input and output parameters are based on real time data obtained from Department of Irrigation and Drainage Malaysia. However, the MISO ARX performance result is not quite impressive to look into. Hence, Neural Network model is cascaded to the MISO ARX model to improve the result. Simulation results show that the proposed cascaded model provides improved prediction performance.
{"title":"Flood water level modelling using Multiple Input Single Output (MISO) ARX structure and cascaded Neural Network for performance improvement","authors":"F. Ruslan, A. Samad, Zainazlan Md Zain, R. Adnan","doi":"10.1109/SPC.2013.6735135","DOIUrl":"https://doi.org/10.1109/SPC.2013.6735135","url":null,"abstract":"Flood water level prediction using system identification technique is still new area for most of the researchers. This is due to the dynamics of the flood water level itself that is often characterized as highly nonlinear. Thus, it is quite a challenging task to represent the flood water level behavioural in mathematical expressions. This paper presents flood water level modelling using MISO (Multiple Input Single Output) ARX (Autoregressive Exogenous Input) structure and cascaded Neural Network model for performance improvement. In this paper, the transfer function relating the input parameters and output parameter was identified with the aid of MISO ARX model. The input and output parameters are based on real time data obtained from Department of Irrigation and Drainage Malaysia. However, the MISO ARX performance result is not quite impressive to look into. Hence, Neural Network model is cascaded to the MISO ARX model to improve the result. Simulation results show that the proposed cascaded model provides improved prediction performance.","PeriodicalId":198247,"journal":{"name":"2013 IEEE Conference on Systems, Process & Control (ICSPC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130165243","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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