Gas classification techniques are often found in several applied fields such as, detection of leak gas in gas cylinders, monitoring the threshold of harmful pollutant gases in the air, health diagnostics, early detection of fire hazards, and others. This requires measurement techniques that are adaptive and robust that can dynamically capture information on changes in vapor or gas compounds contained in free air. This research has been conducted to analyze and identify the types of gas compounds, namely CO and petrodiesel fuel vapor (C14H30). The design of this tool uses the principle of spectrophotometry and the calculation of Backprogation Neural Networks. The working principle is that light radiation in the Light Emitting Diode (LED) series, which has a wavelength range of 385nm to 1720nm, is absorbed to penetrate CO gas or petrodiesel fuel vapor (C14H30) that you want to identify. Light radiation that has passed through the gas / vapor compound was captured by the photodiode sensor. The emission of LED series light radiation produces different wavelength absorption patterns that will be processed by the Backprogation Neural network as an input signal in the identification and learning process. The results of this experiment show the success rate of the Backpropagation neural network in identifying the type of CO gas and petrodiesel fuel vapor (C14H30) is 80%.
{"title":"Aplikasi Metode Spektrofotometri pada Klasifikasi Gas Karbon Monoksida (CO) dan Uap Bahan Bakar Petrodiesel (C14H30)","authors":"Happy Nugroho, Edhi Sarwono, Aditya Rinaldi","doi":"10.30872/ppj.v1i1.559","DOIUrl":"https://doi.org/10.30872/ppj.v1i1.559","url":null,"abstract":"Gas classification techniques are often found in several applied fields such as, detection of leak gas in gas cylinders, monitoring the threshold of harmful pollutant gases in the air, health diagnostics, early detection of fire hazards, and others. This requires measurement techniques that are adaptive and robust that can dynamically capture information on changes in vapor or gas compounds contained in free air. This research has been conducted to analyze and identify the types of gas compounds, namely CO and petrodiesel fuel vapor (C14H30). The design of this tool uses the principle of spectrophotometry and the calculation of Backprogation Neural Networks. The working principle is that light radiation in the Light Emitting Diode (LED) series, which has a wavelength range of 385nm to 1720nm, is absorbed to penetrate CO gas or petrodiesel fuel vapor (C14H30) that you want to identify. Light radiation that has passed through the gas / vapor compound was captured by the photodiode sensor. The emission of LED series light radiation produces different wavelength absorption patterns that will be processed by the Backprogation Neural network as an input signal in the identification and learning process. The results of this experiment show the success rate of the Backpropagation neural network in identifying the type of CO gas and petrodiesel fuel vapor (C14H30) is 80%. \u0000 ","PeriodicalId":221598,"journal":{"name":"Progressive Physics Journal","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126574879","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}
The research on the determining machine factor (k) of Shimadzu digital radiograph X-ray machine have been done at the RSUD Dr. Kanujoso Djatiwibowo Balikpapan. Machine factor should be required in the calculation of radiation exposure which produced by the X-ray machine. Measurement data that have been used as independent variables were current-time, focus distance to the detector and tube voltage variations of 80 kV to 100 kV, while the dependent variable was the S value. Determination of the k value was conducted by weighted linear regression between V2 and using the results of research by Seibert and Morin (2011) for the condition of calibrated X-ray machine. Thus, the reseach has obtained the machine factor of the Shimadzu digital radiograph X-ray machine at the RSUD Dr. Kanujoso Djatiwibowo Balikpapan.
岛津数字x光机的决定机器因子(k)的研究已在RSUD Dr. Kanujoso Djatiwibowo Balikpapan进行。在计算x光机产生的辐射暴露量时,应考虑机器因素。测量数据以电流时间、到探测器的聚焦距离、80 ~ 100 kV的管电压变化为自变量,因变量为S值。k值的确定采用V2之间的加权线性回归,并采用Seibert和Morin(2011)对标定x射线机条件的研究结果。因此,该研究获得了RSUD Dr. Kanujoso Djatiwibowo Balikpapan的岛津数字x射线机的机器因子。
{"title":"Penentuan Nilai Faktor Mesin Pesawat Sinar-X Radiografi Digital Merek Shimadzu di RSUD Dr. Kanujoso Djatiwibowo Balikpapan","authors":"Mamba'ul Fitriyana, S. Muliyono, Kadek Subagiada","doi":"10.30872/ppj.v1i1.563","DOIUrl":"https://doi.org/10.30872/ppj.v1i1.563","url":null,"abstract":"The research on the determining machine factor (k) of Shimadzu digital radiograph X-ray machine have been done at the RSUD Dr. Kanujoso Djatiwibowo Balikpapan. Machine factor should be required in the calculation of radiation exposure which produced by the X-ray machine. Measurement data that have been used as independent variables were current-time, focus distance to the detector and tube voltage variations of 80 kV to 100 kV, while the dependent variable was the S value. Determination of the k value was conducted by weighted linear regression between V2 and using the results of research by Seibert and Morin (2011) for the condition of calibrated X-ray machine. Thus, the reseach has obtained the machine factor of the Shimadzu digital radiograph X-ray machine at the RSUD Dr. Kanujoso Djatiwibowo Balikpapan.","PeriodicalId":221598,"journal":{"name":"Progressive Physics Journal","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127080446","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}
G. Jaya, H. Sutanto, E. Hidayanto, Galih Puspa Saraswati
Radiotherapy is a method of externally cancer therapy using a Linear Accelerator (LINAC) tool. LINAC can produce photon and electron beam energy which will be used to treat cancer according to the position of the cancer in the patient's body. When using an electron beam to treat cancer on the surface of the skin, it is found that there is a lack of a radiation dose. The use of bolus with Silicone Rubber (SR) material is a solution to provide an increase in radiation doses in the surface area of the skin. In this study SR boluses were made using the sol-gel method with dimensions of 17 cm x 17 cm x 1 cm. The SR Bolus was illuminated with an applicator field area of 10 cm x 10 cm and energy variations of 5 MeV and 7 MeV. The surface dose produced at the moment without using a bolus for 5 MeV and 7 MeV energy is 1.60 Gy and 1.61 Gy. When using bolus, the surface dose of 5 MeV and 7 MeV energy is 2.12 Gy and 2.06 Gy. From the results of this study it can be concluded that the use of SR bolus can increase a higher surface dose without using bolus.
放射治疗是一种使用线性加速器(LINAC)工具进行外部癌症治疗的方法。LINAC可以根据癌症在患者体内的位置产生光子和电子束能量,用于治疗癌症。当使用电子束治疗皮肤表面的癌症时,发现缺乏辐射剂量。使用含有硅橡胶(SR)材料的丸剂是一种增加皮肤表面辐射剂量的解决方案。本研究采用溶胶-凝胶法制备SR微丸,尺寸为17 cm × 17 cm × 1 cm。SR Bolus的照射场面积为10 cm × 10 cm,能量变化为5 MeV和7 MeV。目前,在不使用丸剂的情况下,5mev和7mev能量产生的表面剂量分别为1.60 Gy和1.61 Gy。当使用bolus时,5mev和7mev能量的表面剂量分别为2.12 Gy和2.06 Gy。从本研究的结果可以得出结论,使用SR丸可以增加更高的表面剂量,而不使用丸。
{"title":"Studi Penggunaan Bolus Berbahan Silicone Rubber terhadap Dosis Permukaan pada Radioterapi Berkas Elektron","authors":"G. Jaya, H. Sutanto, E. Hidayanto, Galih Puspa Saraswati","doi":"10.30872/ppj.v1i1.561","DOIUrl":"https://doi.org/10.30872/ppj.v1i1.561","url":null,"abstract":"Radiotherapy is a method of externally cancer therapy using a Linear Accelerator (LINAC) tool. LINAC can produce photon and electron beam energy which will be used to treat cancer according to the position of the cancer in the patient's body. When using an electron beam to treat cancer on the surface of the skin, it is found that there is a lack of a radiation dose. The use of bolus with Silicone Rubber (SR) material is a solution to provide an increase in radiation doses in the surface area of the skin. In this study SR boluses were made using the sol-gel method with dimensions of 17 cm x 17 cm x 1 cm. The SR Bolus was illuminated with an applicator field area of 10 cm x 10 cm and energy variations of 5 MeV and 7 MeV. The surface dose produced at the moment without using a bolus for 5 MeV and 7 MeV energy is 1.60 Gy and 1.61 Gy. When using bolus, the surface dose of 5 MeV and 7 MeV energy is 2.12 Gy and 2.06 Gy. From the results of this study it can be concluded that the use of SR bolus can increase a higher surface dose without using bolus.","PeriodicalId":221598,"journal":{"name":"Progressive Physics Journal","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121783183","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}