Pub Date : 2024-04-17DOI: 10.37934/araset.43.2.4251
Sarimah Surianshah, Suriani Hassan, Audrey Liwan
The role to mitigate climate change issue should be embark among youth. This study examines the effects of green technology policy on climate change awareness of youth in Sabah, Malaysia. Using a random sampling method, this study involved 254 respondents from four areas of Sabah namely Tawau, Lahad Datu, Sandakan, and Kota Kinabalu. This study used two approaches which are i) probit model and ii) two-stage least squares method. We find a positive effect between youth’ positive perceptions on green technology policy on their climate change awareness. In addition, the effect becomes five times higher when we address the endogeneity problem in model estimation using the two-stage least squares method and identification strategy based on the prospect theory. This study has highlighted the great impact of youth in implementing green technology to reduce climate change issues in Malaysia in general.
{"title":"The Effect of Green Technology Policy on Climate Change Awareness of Youth in Sabah, Malaysia","authors":"Sarimah Surianshah, Suriani Hassan, Audrey Liwan","doi":"10.37934/araset.43.2.4251","DOIUrl":"https://doi.org/10.37934/araset.43.2.4251","url":null,"abstract":"The role to mitigate climate change issue should be embark among youth. This study examines the effects of green technology policy on climate change awareness of youth in Sabah, Malaysia. Using a random sampling method, this study involved 254 respondents from four areas of Sabah namely Tawau, Lahad Datu, Sandakan, and Kota Kinabalu. This study used two approaches which are i) probit model and ii) two-stage least squares method. We find a positive effect between youth’ positive perceptions on green technology policy on their climate change awareness. In addition, the effect becomes five times higher when we address the endogeneity problem in model estimation using the two-stage least squares method and identification strategy based on the prospect theory. This study has highlighted the great impact of youth in implementing green technology to reduce climate change issues in Malaysia in general.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140690966","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 : 2024-04-17DOI: 10.37934/araset.43.2.189202
Sufinah Dahari
In the era of Industrial Revolution 4.0 and smart manufacturing, the development and deployment of control charts used in the semiconductor industry need to be automated. Consequently, artificial intelligence-based automation methods typically encompass the deployment of statistical software such as JMP. Automation involves frequent dataset updates; the control limits are recalculated as the parameters change (non-stationary behaviour). This requires the user to define the control chart type before its deployment on the production floor. An initially normally distributed dataset may be skewed during the process owing to the influence of outliers. If a user selects a chart based on normality assumptions, detecting a process-mean shift may be impossible if the recalculated limit fluctuates. In the semiconductor industry, a process-mean shift occurs owing to special cause variations in the process. This signals process deterioration, which may affect the quality of the product. It is unknown when outliers will affect the equilibrium of normality assumptions; therefore, it is important to develop an automated, robust control chart that can detect special cause variations under non-stationary conditions. This study proposes the use of Huber’s M-estimators in the Levey-Jennings chart to detect special cause variations in a semiconductor manufacturing process. This study computes the robust M-estimates of all available samples to calculate the new limits in the Levey-Jennings chart. This new chart is referred to as the modified Levey-Jennings with a robust Huber M-estimator (MLVHM). Using production data from Dominant Opto Technologies Sdn. Bhd., Malaysia, a statistical comparison of the MLVHM and Levey-Jennings charts was performed. While the MLVHM is stable, the absolute difference in dispersion between the two charts ranges between 25.26% and 47.91% owing to standard deviation variation in the Levey-Jennings chart in non-stationary situations with outliers. The study concludes that the MLVHM chart is robust and suitable for industrial automatic flow applications.
{"title":"Modified Levey-Jennings Chart with Robust Estimator: A Case of Semiconductor Manufacturing Process","authors":"Sufinah Dahari","doi":"10.37934/araset.43.2.189202","DOIUrl":"https://doi.org/10.37934/araset.43.2.189202","url":null,"abstract":"In the era of Industrial Revolution 4.0 and smart manufacturing, the development and deployment of control charts used in the semiconductor industry need to be automated. Consequently, artificial intelligence-based automation methods typically encompass the deployment of statistical software such as JMP. Automation involves frequent dataset updates; the control limits are recalculated as the parameters change (non-stationary behaviour). This requires the user to define the control chart type before its deployment on the production floor. An initially normally distributed dataset may be skewed during the process owing to the influence of outliers. If a user selects a chart based on normality assumptions, detecting a process-mean shift may be impossible if the recalculated limit fluctuates. In the semiconductor industry, a process-mean shift occurs owing to special cause variations in the process. This signals process deterioration, which may affect the quality of the product. It is unknown when outliers will affect the equilibrium of normality assumptions; therefore, it is important to develop an automated, robust control chart that can detect special cause variations under non-stationary conditions. This study proposes the use of Huber’s M-estimators in the Levey-Jennings chart to detect special cause variations in a semiconductor manufacturing process. This study computes the robust M-estimates of all available samples to calculate the new limits in the Levey-Jennings chart. This new chart is referred to as the modified Levey-Jennings with a robust Huber M-estimator (MLVHM). Using production data from Dominant Opto Technologies Sdn. Bhd., Malaysia, a statistical comparison of the MLVHM and Levey-Jennings charts was performed. While the MLVHM is stable, the absolute difference in dispersion between the two charts ranges between 25.26% and 47.91% owing to standard deviation variation in the Levey-Jennings chart in non-stationary situations with outliers. The study concludes that the MLVHM chart is robust and suitable for industrial automatic flow applications.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":" 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140691783","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 : 2024-04-17DOI: 10.37934/araset.43.2.167177
Mohamed Mydin M. Abdul Kader, Muhammad Naufal Mansor, Zol Bahri Razali, Wan Azani Mustafa, Ahmad Anas Nagoor Gunny, Samsul Setumin, Muhammad Khusairi Osman, Mohaiyedin Idris, Muhammad Firdaus Akbar, Wan Muhamad Faris Naim Muhami Farid, Muhammad Zubir Zainol, Nor Syamina Sharifful Mizam
Malaysia is currently driven to become another most developed country in the world. Among other priority sector is Food Sustainability. Along the process, our vegetable supply-demand keeps increasing by year. Compared to traditional systems, closed systems or its other name called hydroponic is getting more important for plant production, with artificial light which has many potential advantages, including better quality transplants, shorter production time and less resource use. To gain full profit from it, the quality of vegetables needs to be controlled efficiently. Climate conditions, especially temperature and light intensity, have a significant impact on vegetable growth and yield, as well as nutritional quality. Plant growth and development are influenced by a variety of environmental factors, the most important one is light intensity. Among the problems to be tackled in this research are plant growth manual observation, light intensity variation and abundance of growth-related data to be evaluated manually. Therefore, to solve these problems, the specific type of vegetable used here is lettuce. The proposed methods are, observation of plant growth conducted automatically round the clock in intervals of 15 minutes for the whole month (estimated mature period of lettuce), using images captured. At the same time, the proposed light intensity which is red & white to the ratio of 2:1 (optimum ratio recommended by previous researchers) will be used. The issue of data to be evaluated manually will be solved using Artificial Neural Network (ANN) architecture, in specific Deep Learning. Concisely, the results & analysis shows the research is successfully developed for plant growth monitoring by using artificial neural network which, reached 80% to 90% accuracy in the training and validation session that made the architecture sufficient for determining the growth of the said vegetable. This is indeed foreseen, will highly assist the farmer in better monitoring the growth rate of the plant.
{"title":"Environmental Lighting towards Growth Effect Monitoring System of Plant Factory using ANN","authors":"Mohamed Mydin M. Abdul Kader, Muhammad Naufal Mansor, Zol Bahri Razali, Wan Azani Mustafa, Ahmad Anas Nagoor Gunny, Samsul Setumin, Muhammad Khusairi Osman, Mohaiyedin Idris, Muhammad Firdaus Akbar, Wan Muhamad Faris Naim Muhami Farid, Muhammad Zubir Zainol, Nor Syamina Sharifful Mizam","doi":"10.37934/araset.43.2.167177","DOIUrl":"https://doi.org/10.37934/araset.43.2.167177","url":null,"abstract":"Malaysia is currently driven to become another most developed country in the world. Among other priority sector is Food Sustainability. Along the process, our vegetable supply-demand keeps increasing by year. Compared to traditional systems, closed systems or its other name called hydroponic is getting more important for plant production, with artificial light which has many potential advantages, including better quality transplants, shorter production time and less resource use. To gain full profit from it, the quality of vegetables needs to be controlled efficiently. Climate conditions, especially temperature and light intensity, have a significant impact on vegetable growth and yield, as well as nutritional quality. Plant growth and development are influenced by a variety of environmental factors, the most important one is light intensity. Among the problems to be tackled in this research are plant growth manual observation, light intensity variation and abundance of growth-related data to be evaluated manually. Therefore, to solve these problems, the specific type of vegetable used here is lettuce. The proposed methods are, observation of plant growth conducted automatically round the clock in intervals of 15 minutes for the whole month (estimated mature period of lettuce), using images captured. At the same time, the proposed light intensity which is red & white to the ratio of 2:1 (optimum ratio recommended by previous researchers) will be used. The issue of data to be evaluated manually will be solved using Artificial Neural Network (ANN) architecture, in specific Deep Learning. Concisely, the results & analysis shows the research is successfully developed for plant growth monitoring by using artificial neural network which, reached 80% to 90% accuracy in the training and validation session that made the architecture sufficient for determining the growth of the said vegetable. This is indeed foreseen, will highly assist the farmer in better monitoring the growth rate of the plant.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":" 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140691149","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}
In terms of power quality, the rising number of nonlinear loads in modern use has caused warning signs for power system and power engineering professionals. Every day, utilities have to deal with harmonic distortion caused by a growing number of non-linear power electronic equipment. To keep the system's power supply in good condition, a shunt active filter is used to filter out unwanted harmonics in the signal. This study presents a practical and low-cost method for reducing harmonics and enhancing distribution network power quality by means of the use of PV-integrated Shunt Active Power Filters (SAPF). With a teaching-learning-based optimized artificial neural network controller (TLBO-ANN) and the required DC power is extracted from the PV module. SAPF's TLBO-ANN algorithms are intended to increase system performance by reducing total harmonic distortion (THD). Here, the research work was performed in three stages to mitigate grid current harmonics. The first-stage SAPF system comprises a three-prong voltage source converter and uses DC power derived from photovoltaic panels. The P&O algorithm is used to get the maximum power out of a photovoltaic array. In the second stage, the BBO algorithm is used to fine-tune a conventional PI controller, resulting in values for and that increase the controller's performance. Furthermore, it is intended to use the BBO-PI controller's input and output values as training data for the ANN controller. This ANN controller is currently being tuned with the TLBO algorithm to find optimal values for the weight and bias parameters. In the third stage, the converter in PV-SAPF will inject the active power required by the load by using active current control theory, which means the inverter of SAPF is working like DG as well as the active power filter. Employing MATLAB simulations, we concluded that the proposed method is extremely adaptable and highly efficient in lowering harmonic currents that are brought on by non-linear loads.
在电能质量方面，现代使用的非线性负载数量不断增加，给电力系统和电力工程专业人员带来了警示信号。每天，电力公司都要处理越来越多的非线性电力电子设备造成的谐波失真。为了保持系统电源的良好状态，需要使用并联有源滤波器来滤除信号中不需要的谐波。本研究提出了一种实用、低成本的方法，通过使用光伏并联有源电力滤波器（SAPF）来减少谐波并提高配电网电能质量。通过基于教学的优化人工神经网络控制器（TLBO-ANN），从光伏组件中提取所需的直流电。SAPF 的 TLBO-ANN 算法旨在通过降低总谐波失真（THD）来提高系统性能。在此，研究工作分三个阶段进行，以减少电网电流谐波。第一阶段 SAPF 系统包括一个三相电压源转换器，使用光伏电池板产生的直流电。P&O 算法用于获取光伏阵列的最大功率。在第二阶段，使用 BBO 算法对传统的 PI 控制器进行微调，从而得出能提高控制器性能的和值。此外，还打算使用 BBO-PI 控制器的输入和输出值作为 ANN 控制器的训练数据。目前正在使用 TLBO 算法对 ANN 控制器进行调整，以找到权重和偏置参数的最佳值。在第三阶段，PV-SAPF 中的变流器将利用有功电流控制理论注入负载所需的有功功率，这意味着 SAPF 的变流器既像 DG 一样工作，也像有功功率滤波器一样工作。通过 MATLAB 仿真，我们得出结论，所提出的方法适应性极强，能高效降低非线性负载带来的谐波电流。
{"title":"TLBO trained an ANN-based DG integrated Shunt Active Power Filter to Improve Power Quality","authors":"Venkata Anjani Kumar Gaddam, Manubolu Damodar Reddy","doi":"10.37934/araset.43.2.93110","DOIUrl":"https://doi.org/10.37934/araset.43.2.93110","url":null,"abstract":"In terms of power quality, the rising number of nonlinear loads in modern use has caused warning signs for power system and power engineering professionals. Every day, utilities have to deal with harmonic distortion caused by a growing number of non-linear power electronic equipment. To keep the system's power supply in good condition, a shunt active filter is used to filter out unwanted harmonics in the signal. This study presents a practical and low-cost method for reducing harmonics and enhancing distribution network power quality by means of the use of PV-integrated Shunt Active Power Filters (SAPF). With a teaching-learning-based optimized artificial neural network controller (TLBO-ANN) and the required DC power is extracted from the PV module. SAPF's TLBO-ANN algorithms are intended to increase system performance by reducing total harmonic distortion (THD). Here, the research work was performed in three stages to mitigate grid current harmonics. The first-stage SAPF system comprises a three-prong voltage source converter and uses DC power derived from photovoltaic panels. The P&O algorithm is used to get the maximum power out of a photovoltaic array. In the second stage, the BBO algorithm is used to fine-tune a conventional PI controller, resulting in values for and that increase the controller's performance. Furthermore, it is intended to use the BBO-PI controller's input and output values as training data for the ANN controller. This ANN controller is currently being tuned with the TLBO algorithm to find optimal values for the weight and bias parameters. In the third stage, the converter in PV-SAPF will inject the active power required by the load by using active current control theory, which means the inverter of SAPF is working like DG as well as the active power filter. Employing MATLAB simulations, we concluded that the proposed method is extremely adaptable and highly efficient in lowering harmonic currents that are brought on by non-linear loads.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"102 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140693610","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 : 2024-04-17DOI: 10.37934/araset.43.2.237257
Camille Merlin S. Tan, Lawrence Y. Materum
With the vast amount of data being processed and reviewed in production and manufacturing industries nowadays, it takes a lot of time and effort to keep track of their performance metrics. Developing an automation system to improve the overall system performance and reduce man-machine interactions is necessary. Prior to the work, a computer disk drive manufacturing facility checked its fixed-tester availability manually, which made the company unable to optimize its man and machine hours as much as possible. This work proposes a system that displays all the critical information in a dashboard system to address the problem while enabling access to data history logs and further analytic insights. Moreover, the system enables readily accessible fixed-disk tester availability. The outcomes indicate significant improvements, especially in delay reductions, necessary for optimizing man and machine interactions in a fixed-disk testing facility at a leading computer disk drive manufacturer and data storage company at Laguna Technopark.
{"title":"Cleanroom Dashboard System for Time-Reduction Checking of Disk Tester Availability","authors":"Camille Merlin S. Tan, Lawrence Y. Materum","doi":"10.37934/araset.43.2.237257","DOIUrl":"https://doi.org/10.37934/araset.43.2.237257","url":null,"abstract":"With the vast amount of data being processed and reviewed in production and manufacturing industries nowadays, it takes a lot of time and effort to keep track of their performance metrics. Developing an automation system to improve the overall system performance and reduce man-machine interactions is necessary. Prior to the work, a computer disk drive manufacturing facility checked its fixed-tester availability manually, which made the company unable to optimize its man and machine hours as much as possible. This work proposes a system that displays all the critical information in a dashboard system to address the problem while enabling access to data history logs and further analytic insights. Moreover, the system enables readily accessible fixed-disk tester availability. The outcomes indicate significant improvements, especially in delay reductions, necessary for optimizing man and machine interactions in a fixed-disk testing facility at a leading computer disk drive manufacturer and data storage company at Laguna Technopark.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":" 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140692673","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 : 2024-04-17DOI: 10.37934/araset.43.2.134147
Hasbullah Ashaari, Yuhainis Mohd Yusoff, Suranto
Digital marketing has seen rapid growth. Millions have been spent on the digital marketing tools. The increase in the investment in digital marketing is due its effect in increasing sales, improving brand image, increasing customer image, and reducing the overall marketing cost. to companies. Despite these advancements in marketing and its effects, these phenomena are not really observed in small and medium enterprises in developing and less developed country. This has motivated this study to analyse factors that influence the adoption of digital marketing among a group of entrepreneurs from low-income category that is under the assistantship of a state government agency in Malaysia. The sample comprises of 60 entrepreneurs from the group that have just attended digital marketing training have responded to the survey. The study employs a quantitative technique that utilizes the Decomposed Theory of Planned Behaviour (TPB) model that combines the TPB and the Theory of Acceptance Model (TAM). The result indicates that the dependent variable (Digital Marketing Adoption) can be significantly explained by the independent variables (Except Subjective Norm). The result also provides an important finding on the importance of the participants internal factors- Perceived Ease of Use (PEU) , Perceived Usefulness (PU) and Perceived Behavior Control (PBC) in improving the rate of digital marketing adoption. This provides an important insight into the future development of e- marketing among the entrepreneurs from the lower income group in Malaysia. This indicates the importance of the role of effective training in e- marketing.
{"title":"Understanding Factors Influencing the Adoption of Digital Marketing Among Small Businesses: The Application of Decomposed Model of the Theory of Planned Behaviour (TPB)","authors":"Hasbullah Ashaari, Yuhainis Mohd Yusoff, Suranto","doi":"10.37934/araset.43.2.134147","DOIUrl":"https://doi.org/10.37934/araset.43.2.134147","url":null,"abstract":"Digital marketing has seen rapid growth. Millions have been spent on the digital marketing tools. The increase in the investment in digital marketing is due its effect in increasing sales, improving brand image, increasing customer image, and reducing the overall marketing cost. to companies. Despite these advancements in marketing and its effects, these phenomena are not really observed in small and medium enterprises in developing and less developed country. This has motivated this study to analyse factors that influence the adoption of digital marketing among a group of entrepreneurs from low-income category that is under the assistantship of a state government agency in Malaysia. The sample comprises of 60 entrepreneurs from the group that have just attended digital marketing training have responded to the survey. The study employs a quantitative technique that utilizes the Decomposed Theory of Planned Behaviour (TPB) model that combines the TPB and the Theory of Acceptance Model (TAM). The result indicates that the dependent variable (Digital Marketing Adoption) can be significantly explained by the independent variables (Except Subjective Norm). The result also provides an important finding on the importance of the participants internal factors- Perceived Ease of Use (PEU) , Perceived Usefulness (PU) and Perceived Behavior Control (PBC) in improving the rate of digital marketing adoption. This provides an important insight into the future development of e- marketing among the entrepreneurs from the lower income group in Malaysia. This indicates the importance of the role of effective training in e- marketing.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"218 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140693255","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 : 2024-04-17DOI: 10.37934/araset.43.2.3241
Muhammad Akram Ramadhan Ibrahim, Nor Izzati Jaini, Sergey Utyuzhnikov
In the trade-off ranking (TOR) method, the determination of the extreme solution (ES) is the main step before ranking the alternatives. ES reflects the best value of one criterion in a conflicting multi-criteria problem. However, the redundancy in selecting the ESs occurs because of the redundancy in some of the criteria values. Based on the stated problem, an improved TOR method is introduced in this paper to cater the redundant ES problem. An example is used from the literature to test the improvisation. The result indicates the implementation of the TOR method provides a better solution since it satisfies two conditions which are the highest value for different weights and the least compromise values in average weights. This verifies the reasonableness and effectiveness of the TOR method.
在权衡排序法（TOR）中，确定极端解（ES）是对备选方案进行排序前的主要步骤。ES 反映了多标准冲突问题中某一标准的最佳值。然而，由于某些标准值存在冗余，因此在选择 ES 时会出现冗余。基于上述问题，本文介绍了一种改进的 TOR 方法，以解决冗余 ES 问题。本文使用文献中的一个例子来检验改进方法。结果表明，TOR 方法的实施提供了一个更好的解决方案，因为它满足了两个条件，即不同权重的最高值和平均权重的最小折衷值。这验证了 TOR 方法的合理性和有效性。
{"title":"A Trade-off Ranking Method: Case of Extreme Solutions Redundancy","authors":"Muhammad Akram Ramadhan Ibrahim, Nor Izzati Jaini, Sergey Utyuzhnikov","doi":"10.37934/araset.43.2.3241","DOIUrl":"https://doi.org/10.37934/araset.43.2.3241","url":null,"abstract":"In the trade-off ranking (TOR) method, the determination of the extreme solution (ES) is the main step before ranking the alternatives. ES reflects the best value of one criterion in a conflicting multi-criteria problem. However, the redundancy in selecting the ESs occurs because of the redundancy in some of the criteria values. Based on the stated problem, an improved TOR method is introduced in this paper to cater the redundant ES problem. An example is used from the literature to test the improvisation. The result indicates the implementation of the TOR method provides a better solution since it satisfies two conditions which are the highest value for different weights and the least compromise values in average weights. This verifies the reasonableness and effectiveness of the TOR method.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":" 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140692635","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 : 2024-04-17DOI: 10.37934/araset.43.2.203219
Sufinah Dahari, Muzalwana Abdul Talib, Adilah Abdul Ghapor
Statistical Process Control (SPC) charts are frequently used in the semiconductor manufacturing environment to monitor process quality and detect special-cause variations, hence, to take corrective actions when necessary. The important aspects of control charts to consider on production floors are identifying the primary objective of implementing control charts, the type of data to monitor and the most appropriate control limits to establish. When the quality data is a type of attribute data like the proportion of defectives from a production lot, a p-chart approach is most suitable. In p-chart applications, although the assumption of normally distributed process data is not mandatory, the widespread practice is to assume the normal distribution of process data when establishing the control limits. Yet again, the reality of industrial settings is that process data are most likely influenced by outliers, resulting in highly skewed distributions. This paper addresses these issues by proposing robust SPC charting techniques to detect special-cause variations in the semiconductor manufacturing processes. Here, we present a case study of a semiconductor company in Malaysia, Dominant Opto Technologies Sdn. Bhd. to propose three robust statistical approaches for monitoring the proportion of defectives in production lots. We apply M-estimates, median, and interquartile range to calculate the upper control limits (UCL) and found that robust estimators are more effective in detecting early process deterioration and capturing the out-of-control (OOC) conditions better than traditional control charts. By proposing robust methods, this study enlightens the practical aspects of process quality improvement for real-life manufacturing setups. Because a high OOC rate may impact manufacturing productivity, we recommend the decision-makers choose the types of control charts based on the implications of each robust approach toward quality and productivity. The significance of this study includes providing insights into setting up the appropriate attribute control charts for detecting defective proportions for professionals and SPC researchers working in these areas.
统计过程控制 (SPC) 控制图常用于半导体制造环境，以监控过程质量和检测特殊原因引起的变化，从而在必要时采取纠正措施。生产车间需要考虑的控制图的重要方面是确定实施控制图的主要目标、要监控的数据类型以及要建立的最合适的控制限值。如果质量数据是一种属性数据，如生产批次中的缺陷比例，则最适合采用 p 型图方法。在 p 型图应用中，虽然并不强制要求假定过程数据呈正态分布，但普遍的做法是在确定控制限值时假定过程数据呈正态分布。然而，工业环境的现实情况是，过程数据很可能受到异常值的影响，导致分布高度偏斜。本文针对这些问题，提出了稳健的 SPC 制图技术，以检测半导体制造过程中的特殊原因变异。在此，我们以马来西亚的一家半导体公司 Dominant Opto Technologies Sdn. Bhd 为案例，提出了三种用于监控生产批次中缺陷比例的稳健统计方法。我们应用 M 估计值、中位数和四分位数间范围来计算控制上限 (UCL)，发现稳健估计值比传统控制图更能有效地检测早期流程恶化，并更好地捕捉失控 (OOC) 状况。通过提出稳健方法，本研究为现实生活中的生产设置提供了工艺质量改进的实践启示。由于高 OOC 率可能会影响生产率，我们建议决策者根据每种稳健方法对质量和生产率的影响来选择控制图的类型。本研究的意义在于为从事这些领域工作的专业人员和 SPC 研究人员提供了建立适当属性控制图以检测缺陷比例的见解。
{"title":"Robust Control Chart Application in Semiconductor Manufacturing Process","authors":"Sufinah Dahari, Muzalwana Abdul Talib, Adilah Abdul Ghapor","doi":"10.37934/araset.43.2.203219","DOIUrl":"https://doi.org/10.37934/araset.43.2.203219","url":null,"abstract":"Statistical Process Control (SPC) charts are frequently used in the semiconductor manufacturing environment to monitor process quality and detect special-cause variations, hence, to take corrective actions when necessary. The important aspects of control charts to consider on production floors are identifying the primary objective of implementing control charts, the type of data to monitor and the most appropriate control limits to establish. When the quality data is a type of attribute data like the proportion of defectives from a production lot, a p-chart approach is most suitable. In p-chart applications, although the assumption of normally distributed process data is not mandatory, the widespread practice is to assume the normal distribution of process data when establishing the control limits. Yet again, the reality of industrial settings is that process data are most likely influenced by outliers, resulting in highly skewed distributions. This paper addresses these issues by proposing robust SPC charting techniques to detect special-cause variations in the semiconductor manufacturing processes. Here, we present a case study of a semiconductor company in Malaysia, Dominant Opto Technologies Sdn. Bhd. to propose three robust statistical approaches for monitoring the proportion of defectives in production lots. We apply M-estimates, median, and interquartile range to calculate the upper control limits (UCL) and found that robust estimators are more effective in detecting early process deterioration and capturing the out-of-control (OOC) conditions better than traditional control charts. By proposing robust methods, this study enlightens the practical aspects of process quality improvement for real-life manufacturing setups. Because a high OOC rate may impact manufacturing productivity, we recommend the decision-makers choose the types of control charts based on the implications of each robust approach toward quality and productivity. The significance of this study includes providing insights into setting up the appropriate attribute control charts for detecting defective proportions for professionals and SPC researchers working in these areas.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140692042","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 : 2024-04-17DOI: 10.37934/araset.43.2.5264
Nur Qafareeny Abdul Halim, Noor Azzah Awang, Siti Nurhidayah Yaacob, Hazwani Hashim, Lazim Abdullah
The study of aggregation operators has played a crucial role in various decision-making methods. The primary function of the aggregation operator is to combine multiple numbers into a single value. While Einstein operators offer a compact notation and can handle complex and large datasets, they do not consider the interaction involved in determining the criteria weights or account for imprecise and indeterminate data. To overcome this limitation, this paper introduces an improved aggregation operator, the rough neutrosophic Shapley weighted Einstein averaging aggregation operator. The rough neutrosophic sets offer a method for effectively managing the fuzziness and uncertainty that commonly occur in real-world scenarios and the Shapley fuzzy measure helps us understand the importance or value of different elements in each scenario. This operator combines the Shapley fuzzy measure with Einstein operators under rough neutrosophic sets, which are an effective tool for handling incomplete, indeterminate, and inconsistent information. The proposed operator satisfies essential algebraic properties such as idempotency, boundedness, and monotonicity. This paper also presents a decision-making methodology based on the proposed operator, with attribute values derived from the rough neutrosophic set. Finally, the applicability of the suggested aggregation operator is illustrated with a numerical example.
{"title":"Rough Neutrosophic Shapley Weighted Einstein Averaging Aggregation Operator and its Application in Multi-Criteria Decision-Making Problem","authors":"Nur Qafareeny Abdul Halim, Noor Azzah Awang, Siti Nurhidayah Yaacob, Hazwani Hashim, Lazim Abdullah","doi":"10.37934/araset.43.2.5264","DOIUrl":"https://doi.org/10.37934/araset.43.2.5264","url":null,"abstract":"The study of aggregation operators has played a crucial role in various decision-making methods. The primary function of the aggregation operator is to combine multiple numbers into a single value. While Einstein operators offer a compact notation and can handle complex and large datasets, they do not consider the interaction involved in determining the criteria weights or account for imprecise and indeterminate data. To overcome this limitation, this paper introduces an improved aggregation operator, the rough neutrosophic Shapley weighted Einstein averaging aggregation operator. The rough neutrosophic sets offer a method for effectively managing the fuzziness and uncertainty that commonly occur in real-world scenarios and the Shapley fuzzy measure helps us understand the importance or value of different elements in each scenario. This operator combines the Shapley fuzzy measure with Einstein operators under rough neutrosophic sets, which are an effective tool for handling incomplete, indeterminate, and inconsistent information. The proposed operator satisfies essential algebraic properties such as idempotency, boundedness, and monotonicity. This paper also presents a decision-making methodology based on the proposed operator, with attribute values derived from the rough neutrosophic set. Finally, the applicability of the suggested aggregation operator is illustrated with a numerical example.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"69 s283","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140693774","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 : 2024-04-17DOI: 10.37934/araset.43.2.120
Faiz Zulkifli, Rozaimah Zainal Abidin
Malaysia is in the process of developing a new identity system to provide citizens and residents with secure and efficient access to digital services. This study aimed to determine the responsiveness of the Malaysian population to new technologies and evaluate their opinions on managing their identity online. The study employed a quantitative research design and collected data through an online questionnaire. A total of 1,014 participants from Malaysia, aged 18 years and above, were included in the study, and the data were analysed using cross-tabulation analysis. The theoretical framework of the study was based on the technology acceptance model, the risk and negative consequences theory of individuals' perceptions, and the organizational trustworthiness theory. The results showed that various demographic characteristics influence individuals' attitudes towards technology adoption and online identity management. Gender, age, ethnicity/race, work status, employment sector, level of education, place of living, household income level, and residence state were all significantly associated with early adoption of emerging technologies, trying out new technologies, and enjoyment of trying out new technologies. Certain demographic factors may also have an impact on people's preferences for specific identity management tools. The study also found significant relationships between various demographic factors and trust levels when it comes to personal data. The study findings have implications for policymakers and practitioners involved in promoting the adoption of new technologies and the management of online identities. The study's findings may assist policymakers in implementing better measures for protecting Malaysians' online identity and privacy. Additionally, the results could also inform future research investigating technology adoption and acceptance among Malaysians.
{"title":"Identity in the Digital Age: An Investigation of Malaysian Perspectives on Technology and Privacy","authors":"Faiz Zulkifli, Rozaimah Zainal Abidin","doi":"10.37934/araset.43.2.120","DOIUrl":"https://doi.org/10.37934/araset.43.2.120","url":null,"abstract":"Malaysia is in the process of developing a new identity system to provide citizens and residents with secure and efficient access to digital services. This study aimed to determine the responsiveness of the Malaysian population to new technologies and evaluate their opinions on managing their identity online. The study employed a quantitative research design and collected data through an online questionnaire. A total of 1,014 participants from Malaysia, aged 18 years and above, were included in the study, and the data were analysed using cross-tabulation analysis. The theoretical framework of the study was based on the technology acceptance model, the risk and negative consequences theory of individuals' perceptions, and the organizational trustworthiness theory. The results showed that various demographic characteristics influence individuals' attitudes towards technology adoption and online identity management. Gender, age, ethnicity/race, work status, employment sector, level of education, place of living, household income level, and residence state were all significantly associated with early adoption of emerging technologies, trying out new technologies, and enjoyment of trying out new technologies. Certain demographic factors may also have an impact on people's preferences for specific identity management tools. The study also found significant relationships between various demographic factors and trust levels when it comes to personal data. The study findings have implications for policymakers and practitioners involved in promoting the adoption of new technologies and the management of online identities. The study's findings may assist policymakers in implementing better measures for protecting Malaysians' online identity and privacy. Additionally, the results could also inform future research investigating technology adoption and acceptance among Malaysians.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140690750","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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