Pub Date : 2021-09-24DOI: 10.1109/ICASI52993.2021.9568426
Salman Hassan, Safioul Bari, A. S. M. M. B. Shuvo, Shahriar Khan
Security is a requirement in society, yet its wide implementation is held back because of high expenses, and barriers to the use of technology. Experimental implementation of security at low cost will only help in promoting the technology at more affordable prices. This paper describes the design of a security system of surveillance using Raspberry Pi and Arduino UNO. The design senses the presence of $a$ human in a surveillance area and immediately sets off the buzzer and simultaneously starts capturing video of the motion it had detected and stores it in a folder. When the design senses a motion, it immediately sends an SMS to the user. The user of this design can see the live video of the motion it detects using the internet connection from a remote area. Our objective of making a low-cost surveillance area security system has been mostly fulfilled. Although this is a low-cost project, features can be compared with existing commercially available systems.
{"title":"Implementation of a Low-Cost IoT Enabled Surveillance Security System","authors":"Salman Hassan, Safioul Bari, A. S. M. M. B. Shuvo, Shahriar Khan","doi":"10.1109/ICASI52993.2021.9568426","DOIUrl":"https://doi.org/10.1109/ICASI52993.2021.9568426","url":null,"abstract":"Security is a requirement in society, yet its wide implementation is held back because of high expenses, and barriers to the use of technology. Experimental implementation of security at low cost will only help in promoting the technology at more affordable prices. This paper describes the design of a security system of surveillance using Raspberry Pi and Arduino UNO. The design senses the presence of $a$ human in a surveillance area and immediately sets off the buzzer and simultaneously starts capturing video of the motion it had detected and stores it in a folder. When the design senses a motion, it immediately sends an SMS to the user. The user of this design can see the live video of the motion it detects using the internet connection from a remote area. Our objective of making a low-cost surveillance area security system has been mostly fulfilled. Although this is a low-cost project, features can be compared with existing commercially available systems.","PeriodicalId":103254,"journal":{"name":"2021 7th International Conference on Applied System Innovation (ICASI)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134311697","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}
NMOSFET devices fabricated by 0.18 micron process are studied through fitting current-voltage (I-V) characteristic curves. The conventional formulas well addressing I-V curves of MOSFET transistors have been technically modified and subtracted a Gaussian term to fit the non-linear kink-like effects, which is as seen in Sine-Gordon equation. In each triode region, the increase rates of current against VDS (the voltage bias between Drain and Source) seem like a half-side peak producing generated peak-like heat, which is quantized as phonons and apparently downgrades the electrical performances. The heat of peaks is shown to be like a Gaussian curve, which effectively reduces the mobility of carriers and thus suppresses the specific IDS. The extra parameters are introduced to achieve well-fitting curves.
{"title":"Current-Voltage Characteristic Curves Addressing Non-Linear Kink-like Effects","authors":"Hsin-Chia Yang, Peifeng Yang, Chia-Chun Lin, Kuan-Hung Chen, You-Sheng Lin, Sung-Ching Chi","doi":"10.1109/ICASI52993.2021.9568436","DOIUrl":"https://doi.org/10.1109/ICASI52993.2021.9568436","url":null,"abstract":"NMOSFET devices fabricated by 0.18 micron process are studied through fitting current-voltage (I-V) characteristic curves. The conventional formulas well addressing I-V curves of MOSFET transistors have been technically modified and subtracted a Gaussian term to fit the non-linear kink-like effects, which is as seen in Sine-Gordon equation. In each triode region, the increase rates of current against VDS (the voltage bias between Drain and Source) seem like a half-side peak producing generated peak-like heat, which is quantized as phonons and apparently downgrades the electrical performances. The heat of peaks is shown to be like a Gaussian curve, which effectively reduces the mobility of carriers and thus suppresses the specific IDS. The extra parameters are introduced to achieve well-fitting curves.","PeriodicalId":103254,"journal":{"name":"2021 7th International Conference on Applied System Innovation (ICASI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128647878","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 : 2021-09-24DOI: 10.1109/ICASI52993.2021.9568480
Ehab Awad
Mid-wave and long-wave infrared bands become crucial for imaging and detection devices because they give unique characteristics of different objects, materials, and surfaces. Enhancing optical absorption through plasmonics is the key issue to increase the responsivity of such detection devices. Also, adding polarimetric detection to such devices can discriminate different polarizations and thus highlights and distinguishes between different features in a scene using unique signatures of different objects. In this work, a plasmonic-enhanced detection together with polarimetric discrimination is demonstrated. It covers an ultra-broadband wavelength range (3-14µm) of both mid-wave and long-wave infrared spectral bands. It is built around a focal-plane array of periodic and interleaved graded width gold-stripes and air-gaps that are placed on top of an infrared detector. A micro-bolometer is chosen as one example of infrared detection devices to evaluate the gold structure performance. The interleaved graded width structure allows covering the ultra-broadband infrared range. While the plasmonics allows having a polarization extinction-ratio discrimination up to 35dB, in addition to enhanced absorption up to 200%. Moreover, the structure allows for a wide field-of-view angle equals to 120°.
{"title":"Ultra-Broadband and Wide Field-Of-View Enhanced Polarimetric Infrared Detection Using a Plasmonic Gold Structure","authors":"Ehab Awad","doi":"10.1109/ICASI52993.2021.9568480","DOIUrl":"https://doi.org/10.1109/ICASI52993.2021.9568480","url":null,"abstract":"Mid-wave and long-wave infrared bands become crucial for imaging and detection devices because they give unique characteristics of different objects, materials, and surfaces. Enhancing optical absorption through plasmonics is the key issue to increase the responsivity of such detection devices. Also, adding polarimetric detection to such devices can discriminate different polarizations and thus highlights and distinguishes between different features in a scene using unique signatures of different objects. In this work, a plasmonic-enhanced detection together with polarimetric discrimination is demonstrated. It covers an ultra-broadband wavelength range (3-14µm) of both mid-wave and long-wave infrared spectral bands. It is built around a focal-plane array of periodic and interleaved graded width gold-stripes and air-gaps that are placed on top of an infrared detector. A micro-bolometer is chosen as one example of infrared detection devices to evaluate the gold structure performance. The interleaved graded width structure allows covering the ultra-broadband infrared range. While the plasmonics allows having a polarization extinction-ratio discrimination up to 35dB, in addition to enhanced absorption up to 200%. Moreover, the structure allows for a wide field-of-view angle equals to 120°.","PeriodicalId":103254,"journal":{"name":"2021 7th International Conference on Applied System Innovation (ICASI)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130567656","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 : 2021-09-24DOI: 10.1109/ICASI52993.2021.9568412
Jain-Shing Wu, Chien-Chang Wu, Chien-Sen Liao
In recent years, due to the vigorous development of artificial intelligence in various fields, many various applications have appeared. However, due to the biological uncertainty, only a few research apply artificial intelligence to manage the biological production process. The fermentation process of lactic acid bacteria has biologically uncertain, and the parameters in the fermentation process are difficult to set with fixed values to be automatically executed. Therefore, the current fermentation process is carried out manually. Due to the uncertainty in the production process, once human error occurs, it often causes hundreds of thousands or even millions dollars of losses. Therefore, if the fermentation effect can be improved, the subsequent production efficiency can be directly improved. In order to automate the fermentation process, in this project, we hope that by combining artificial intelligence (AI) with the background of lactic acid bacteria cultivation, the current complicated manual fermentation process can be transformed into automation as the goal of Industry 4.0. Based on the logs of the experiments of Lactobacillus fermentation, we use Long Shorten-Memory (LSTM) to predict the output amount of fermentation results. In the experimental results, we collects 9 trials of experimental results (4 case for over 3*109, 5 cases for approaching 3*109 and 7 cases for 0 output). And then, all the results are randomly separated into training and testing datasets for 20 different runs. The training dataset average accuracy of 20 runs is 100%. And the testing dataset average accuracy of 20 runs is 95%. Hence, according to the experimental results, we can know the proposed methods really can predicted the amount of the fermentation products.
{"title":"Novel Lactobacillus Fermentation Prediction Using Deep Learning","authors":"Jain-Shing Wu, Chien-Chang Wu, Chien-Sen Liao","doi":"10.1109/ICASI52993.2021.9568412","DOIUrl":"https://doi.org/10.1109/ICASI52993.2021.9568412","url":null,"abstract":"In recent years, due to the vigorous development of artificial intelligence in various fields, many various applications have appeared. However, due to the biological uncertainty, only a few research apply artificial intelligence to manage the biological production process. The fermentation process of lactic acid bacteria has biologically uncertain, and the parameters in the fermentation process are difficult to set with fixed values to be automatically executed. Therefore, the current fermentation process is carried out manually. Due to the uncertainty in the production process, once human error occurs, it often causes hundreds of thousands or even millions dollars of losses. Therefore, if the fermentation effect can be improved, the subsequent production efficiency can be directly improved. In order to automate the fermentation process, in this project, we hope that by combining artificial intelligence (AI) with the background of lactic acid bacteria cultivation, the current complicated manual fermentation process can be transformed into automation as the goal of Industry 4.0. Based on the logs of the experiments of Lactobacillus fermentation, we use Long Shorten-Memory (LSTM) to predict the output amount of fermentation results. In the experimental results, we collects 9 trials of experimental results (4 case for over 3*109, 5 cases for approaching 3*109 and 7 cases for 0 output). And then, all the results are randomly separated into training and testing datasets for 20 different runs. The training dataset average accuracy of 20 runs is 100%. And the testing dataset average accuracy of 20 runs is 95%. Hence, according to the experimental results, we can know the proposed methods really can predicted the amount of the fermentation products.","PeriodicalId":103254,"journal":{"name":"2021 7th International Conference on Applied System Innovation (ICASI)","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114665657","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 : 2021-09-24DOI: 10.1109/ICASI52993.2021.9568453
Quoc-Thang Phan, Yuan-Kang Wu, Q. Phan
Wind power generation takes on an increasingly vital role in the power grid due to its environmental and economic benefits. However, the primary challenges that are related to the integration of wind power into power systems include variability, uncertainty. An accurate forecasting reduces operating costs and enhances power system stability. Wind power forecasting include many steps, including data collection, data preprocessing, the construction and training for models, and error calculation. Among them, data preprocessing plays an important role on the process of wind power forecasting since the inputs of the forecasting model would be sensitive to the quality of data. As a result, this paper presents a survey on the methods for wind-data processing. These methods aim to preprocess and extract suitable features from numerical weather prediction (NWP) wind speeds and measured wind power data. Finally, this paper used a case study to demonstrate the important of the preprocessing step on wind power forecasting.
{"title":"An Overview of Data Preprocessing for Short-Term Wind Power Forecasting","authors":"Quoc-Thang Phan, Yuan-Kang Wu, Q. Phan","doi":"10.1109/ICASI52993.2021.9568453","DOIUrl":"https://doi.org/10.1109/ICASI52993.2021.9568453","url":null,"abstract":"Wind power generation takes on an increasingly vital role in the power grid due to its environmental and economic benefits. However, the primary challenges that are related to the integration of wind power into power systems include variability, uncertainty. An accurate forecasting reduces operating costs and enhances power system stability. Wind power forecasting include many steps, including data collection, data preprocessing, the construction and training for models, and error calculation. Among them, data preprocessing plays an important role on the process of wind power forecasting since the inputs of the forecasting model would be sensitive to the quality of data. As a result, this paper presents a survey on the methods for wind-data processing. These methods aim to preprocess and extract suitable features from numerical weather prediction (NWP) wind speeds and measured wind power data. Finally, this paper used a case study to demonstrate the important of the preprocessing step on wind power forecasting.","PeriodicalId":103254,"journal":{"name":"2021 7th International Conference on Applied System Innovation (ICASI)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124003243","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 recent decades, residential and commercial solar photovoltaic (PV) systems have been increased. However, most of these PV systems are not monitored by power system operators. Thus, they are called invisible solar power or behind-the-meter (BTM) solar sites. The presence of these types of PV sites could pose many new challenges to the analysis of hosting capacity, net load forecasting for feeders, or volt-ampere-reactive (VAR) control. To handle these system operations, numerous approaches have been developed to estimate invisible or behind-the-meter solar generation. This paper presents a complete literature review about the estimation techniques for invisible solar generation, providing an important reference to power system operators. These up-to-date estimation techniques are classified into two categories, i.e., data-driven methods and model-based methods. This paper will summarize the required data for each method and study their strengths and drawbacks for PV estimation.
{"title":"An Overview of Invisible Solar Generation Estimating Approaches","authors":"Thi Bich Phuong Nguyen, Yuan-Kang Wu, Manh-Hai Pham","doi":"10.1109/ICASI52993.2021.9568402","DOIUrl":"https://doi.org/10.1109/ICASI52993.2021.9568402","url":null,"abstract":"In recent decades, residential and commercial solar photovoltaic (PV) systems have been increased. However, most of these PV systems are not monitored by power system operators. Thus, they are called invisible solar power or behind-the-meter (BTM) solar sites. The presence of these types of PV sites could pose many new challenges to the analysis of hosting capacity, net load forecasting for feeders, or volt-ampere-reactive (VAR) control. To handle these system operations, numerous approaches have been developed to estimate invisible or behind-the-meter solar generation. This paper presents a complete literature review about the estimation techniques for invisible solar generation, providing an important reference to power system operators. These up-to-date estimation techniques are classified into two categories, i.e., data-driven methods and model-based methods. This paper will summarize the required data for each method and study their strengths and drawbacks for PV estimation.","PeriodicalId":103254,"journal":{"name":"2021 7th International Conference on Applied System Innovation (ICASI)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124182126","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}
Power devices have been overwhelmingly popular for the last decades. Insulated Gate Bipolar Transistor (IGBT) is quite dominant for the capability of providing relatively high current at pretty high sustaining breakdown voltages. The underlying bipolar junction transistors (including NPN and PNP) get activated as the bias is applied to the isolated gate which is equivalent to a MOSFET transistor with high input impedance. Flowing current ignites wherever potential bipolar junction transistors locate. The current of the whole IGBT is lead by the VEC voltage while, for VMOS (vertical MOS) type, the current flows from the bottom (Emitter) straight up to the top (Collector) through N-type drift region. With the careful design of the concentration in N-type drift region, a multi-merit transistor of high power is thus available. The measured characteristic curves of current (IEC) versus voltage (VEC) are thus fitted and modeled.
{"title":"Promising Algorithm Addressing Characteristic Curves of Insulated Gated Bipolar Transistor (IGBT) Fitted by Applying Bipolar Transistor Driven by Insulated Gate Bias","authors":"Hsin-Chia Yang, Sung-Ching Chi, Peifeng Yang, Chia-Chun Lin, Kuan-Hung Chen, You-Sheng Lin","doi":"10.1109/ICASI52993.2021.9568443","DOIUrl":"https://doi.org/10.1109/ICASI52993.2021.9568443","url":null,"abstract":"Power devices have been overwhelmingly popular for the last decades. Insulated Gate Bipolar Transistor (IGBT) is quite dominant for the capability of providing relatively high current at pretty high sustaining breakdown voltages. The underlying bipolar junction transistors (including NPN and PNP) get activated as the bias is applied to the isolated gate which is equivalent to a MOSFET transistor with high input impedance. Flowing current ignites wherever potential bipolar junction transistors locate. The current of the whole IGBT is lead by the VEC voltage while, for VMOS (vertical MOS) type, the current flows from the bottom (Emitter) straight up to the top (Collector) through N-type drift region. With the careful design of the concentration in N-type drift region, a multi-merit transistor of high power is thus available. The measured characteristic curves of current (IEC) versus voltage (VEC) are thus fitted and modeled.","PeriodicalId":103254,"journal":{"name":"2021 7th International Conference on Applied System Innovation (ICASI)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127169258","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 machining accuracy of a machine tool is affected by several factors, including temperature variations of the environmental space, thermal deformation in dynamic operations. However, with the development of In-dustry 4.0, the manufacturing industry has moved towards digital, intelligent and predictive technologies. In terms of eliminating thermal errors, the neural network method is utilized to obtain the thermal error compensation model for machine tools to improve the machining accuracy. However, when thermal and dynamic er-rors caused by the movement of a machine tool are abnormally large, it will lead to shutting down of the ma-chine tool for the elimination or handling of this problem. Shutdown detection has an impact on the production cycle and capacity of the factory. In this study, the inertial measurement unit (IMU) with accelerometers and gyroscopes was employed to measure the accuracy of a machine tool. When measuring the acceleration signals of a machine tool in the dynamic process, the acceleration signals were filtered and integrated by mathematical operations to obtain the velocity and displacement from IMU signals. The velocity and dis-placement data were combined through data fusion to eliminate information errors caused by multiple integration in mixed data. In the verification experiment, the machine tool was set with the error values of 15µm and 50µm to verify the signal measurement and processing accuracy of the IMU module. Under 10mm moving distance, the displacement of a machine tool could be detected by the errors of 20.58µm and 47.66µm, re-spectively. The errors in IMU measurement accuracy were 37.2% and 4.7%, respectively. The results from this study disclosed that this method produced highly reliable thermal displacement values in real-time and could be applied to development of functions such as instant fault identification and self-compensation control.
{"title":"Inertial Measurement Unit for Measuring and Processing of Axial Thermal Displacement Signal of a Machine Tool","authors":"Kun-Ying Li, Chin-Ming Chen, Meng-Chiou Liao, Kai-Jung Chen","doi":"10.1109/ICASI52993.2021.9568489","DOIUrl":"https://doi.org/10.1109/ICASI52993.2021.9568489","url":null,"abstract":"The machining accuracy of a machine tool is affected by several factors, including temperature variations of the environmental space, thermal deformation in dynamic operations. However, with the development of In-dustry 4.0, the manufacturing industry has moved towards digital, intelligent and predictive technologies. In terms of eliminating thermal errors, the neural network method is utilized to obtain the thermal error compensation model for machine tools to improve the machining accuracy. However, when thermal and dynamic er-rors caused by the movement of a machine tool are abnormally large, it will lead to shutting down of the ma-chine tool for the elimination or handling of this problem. Shutdown detection has an impact on the production cycle and capacity of the factory. In this study, the inertial measurement unit (IMU) with accelerometers and gyroscopes was employed to measure the accuracy of a machine tool. When measuring the acceleration signals of a machine tool in the dynamic process, the acceleration signals were filtered and integrated by mathematical operations to obtain the velocity and displacement from IMU signals. The velocity and dis-placement data were combined through data fusion to eliminate information errors caused by multiple integration in mixed data. In the verification experiment, the machine tool was set with the error values of 15µm and 50µm to verify the signal measurement and processing accuracy of the IMU module. Under 10mm moving distance, the displacement of a machine tool could be detected by the errors of 20.58µm and 47.66µm, re-spectively. The errors in IMU measurement accuracy were 37.2% and 4.7%, respectively. The results from this study disclosed that this method produced highly reliable thermal displacement values in real-time and could be applied to development of functions such as instant fault identification and self-compensation control.","PeriodicalId":103254,"journal":{"name":"2021 7th International Conference on Applied System Innovation (ICASI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126661997","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 : 2021-09-24DOI: 10.1109/ICASI52993.2021.9568447
D. Lu, I. Chen
A novel three-dimensional structure for 6-transistor static random access memory (SRAM) cell composed of vertical gate-all-around transistors is proposed. A three-layer design for the cell is laid out with an area of 24F2, which is multiple times denser than conventional cell design. Significant cost-per-function benefits are thus expected. Buried power rail design facilitates routing in the ultra-compact cell. A novel monolithic process sequence to realize the cell utilizes 9 masks, somewhat increasing processing cost as compared to two-dimensional SRAM. The vertical gate-all-around transistor may either have conventional junction or junctionless design, the latter implying simpler fabrication process. Reasonable cell characteristics is demonstrated with TCAD simulation down to a supply voltage of 0.5V.
{"title":"A Novel Three-Dimensional 6T-SRAM Cell Featuring Vertical Transistors and 24F2 Layout Area","authors":"D. Lu, I. Chen","doi":"10.1109/ICASI52993.2021.9568447","DOIUrl":"https://doi.org/10.1109/ICASI52993.2021.9568447","url":null,"abstract":"A novel three-dimensional structure for 6-transistor static random access memory (SRAM) cell composed of vertical gate-all-around transistors is proposed. A three-layer design for the cell is laid out with an area of 24F2, which is multiple times denser than conventional cell design. Significant cost-per-function benefits are thus expected. Buried power rail design facilitates routing in the ultra-compact cell. A novel monolithic process sequence to realize the cell utilizes 9 masks, somewhat increasing processing cost as compared to two-dimensional SRAM. The vertical gate-all-around transistor may either have conventional junction or junctionless design, the latter implying simpler fabrication process. Reasonable cell characteristics is demonstrated with TCAD simulation down to a supply voltage of 0.5V.","PeriodicalId":103254,"journal":{"name":"2021 7th International Conference on Applied System Innovation (ICASI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127485012","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 order to improve the logical ability of all youths, the governments of different countries have set the code programming curriculum in high school as one of their education policies. Hence, some programming aid software are developed for student trying to learn how to program, taking exams on the system, and evaluating the students according to the codes of students. However, it is hard to know the enthusiasm from learning of the students which directly impact the following learning status of students. Hence, observing the enthusiasm from learning of the students is important for teachers. In our previous work, we used the Fuzzy Logic to evaluate the enthusiasm of the students from the log files in the DICE system. However, it only observes the login frequency and during time. In this paper, we provide a novel way to improve the evaluating the enthusiasm of the students. First, we analyze the all the information from the log files. And then, we store all behaviors with time stamp of one student into one special tensor. After all tensors of all students ready, we send input them to the Convolution Neural Network (CNN) to classify the students to three categories “passion”, “normal” and “apathetic.” We used log files with the number of over 206. We separate the dataset into training and testing datasets 20 times randomly. The accuracy of training dataset is 100%, and the accuracy of average testing dataset is 93.01%. The experimental results show that we can separate the students into the three categories. Hence, for our new method can significantly measure the learning enthusiasm of the students.
{"title":"Novel Enthusiasm Evaluating Method for Code Programming Curriculum","authors":"Jain-Shing Wu, Ting Chien, Chin-Yi Yang, Li-Ren Chien","doi":"10.1109/ICASI52993.2021.9568484","DOIUrl":"https://doi.org/10.1109/ICASI52993.2021.9568484","url":null,"abstract":"In order to improve the logical ability of all youths, the governments of different countries have set the code programming curriculum in high school as one of their education policies. Hence, some programming aid software are developed for student trying to learn how to program, taking exams on the system, and evaluating the students according to the codes of students. However, it is hard to know the enthusiasm from learning of the students which directly impact the following learning status of students. Hence, observing the enthusiasm from learning of the students is important for teachers. In our previous work, we used the Fuzzy Logic to evaluate the enthusiasm of the students from the log files in the DICE system. However, it only observes the login frequency and during time. In this paper, we provide a novel way to improve the evaluating the enthusiasm of the students. First, we analyze the all the information from the log files. And then, we store all behaviors with time stamp of one student into one special tensor. After all tensors of all students ready, we send input them to the Convolution Neural Network (CNN) to classify the students to three categories “passion”, “normal” and “apathetic.” We used log files with the number of over 206. We separate the dataset into training and testing datasets 20 times randomly. The accuracy of training dataset is 100%, and the accuracy of average testing dataset is 93.01%. The experimental results show that we can separate the students into the three categories. Hence, for our new method can significantly measure the learning enthusiasm of the students.","PeriodicalId":103254,"journal":{"name":"2021 7th International Conference on Applied System Innovation (ICASI)","volume":"100 1‐2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132905841","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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