The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it i...The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it is necessary to conduct in-depth research and propose corresponding error detection and error elimination methods.This paper first proposes the root causes and threats of bias in AI algorithms,then summarizes the existing bias detection and error elimination methods,and proposes a bias processing framework in three-level dimensions of data,models,and conclusions,aiming to provide a framework for a comprehensive solution to errors in algorithms.At the same time,it also summarizes the problems and challenges in existing research and makes a prospect for future research trends.It is hoped that it will be helpful for us to build fairer AI.展开更多
The application of Genetic Algorithm (GA) to the optimization of important parameters (Directivity, Radiated Power, Impedance etc.) of magnetically biased microstrip antenna, fabricated on ferrite substrate, is report...The application of Genetic Algorithm (GA) to the optimization of important parameters (Directivity, Radiated Power, Impedance etc.) of magnetically biased microstrip antenna, fabricated on ferrite substrate, is reported. The fitness functions for the GA program have been developed using cavity method for the analysis of microstrip antenna. The effect of external magnetic biasing has also been incorporated in the fitness function formulation as effective propagation constant. Using stochastic based search method of GA the common characteristics of electro-magnetic were entertained which cannot be handled by other optimization techniques. The genetic algorithm was run for 500 generations. The computed results are in good agreement with the results obtained experimentally.展开更多
The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of ...The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of civil aviation, satellite based augmentation system (SBAS) has been planned by various countries including USA, Europe, Japan and India. The Indian SBAS is named as GPS Aided Geo Augmented Navigation (GAGAN). The GAGAN network consists of several dual frequency GPS receivers located at various airports around the Indian subcontinent. The ionospheric delay, which is a function of the total electron content (TEC), is one of the main sources of error affecting GPS/SBAS accuracy. A dual frequency GPS receiver can be used to estimate the TEC. However, line-of-sight TEC derived from dual frequency GPS data is corrupted by the instrumental biases of the GPS receiver and satellites. The estimation of receiver instrumental bias is particularly important for obtaining accurate estimates of ionospheric delay. In this paper, two prominent techniques based on Kalman filter and Self-Calibration Of pseudo Range Error (SCORE) algorithm are used for estimation of instrumental biases. The estimated instrumental bias and TEC results for the GPS Aided Geo Augmented Navigation (GAGAN) station at Hyderabad (78.47°E, 17.45°N), India are presented.展开更多
This paper delves into the hidden impact of algorithmic bias on the allocation of online education resources.With the rapid development of online education,algorithms play a crucial role in resource allocation,but alg...This paper delves into the hidden impact of algorithmic bias on the allocation of online education resources.With the rapid development of online education,algorithms play a crucial role in resource allocation,but algorithmic bias has emerged as a significant issue.The study analyzes the impact of bias at three levels:data level,where data collection and annotation biases lead to uneven resource allocation and misdirected recommendations;algorithmic model level,with design flaws and bias accumulation during optimization causing unfair resource allocation decisions;and result level,imposing implicit restrictions on students’learning opportunities and posing potential threats to educational and social equity.Through case studies of Online Education Platform A and Online Education Project B,the actual manifestations and impacts of algorithmic bias are demonstrated.To address these problems,corresponding countermeasures are proposed,including data governance strategies to improve data quality,algorithmic optimization strategies to enhance fairness and transparency,and educational management and policy recommendations to strengthen regulation and promote algorithmic literacy.This research not only reveals the harm of algorithmic bias but also provides a comprehensive and systematic solution framework,which has important theoretical and practical significance for promoting fair resource allocation in online education and realizing educational equity.展开更多
文摘The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it is necessary to conduct in-depth research and propose corresponding error detection and error elimination methods.This paper first proposes the root causes and threats of bias in AI algorithms,then summarizes the existing bias detection and error elimination methods,and proposes a bias processing framework in three-level dimensions of data,models,and conclusions,aiming to provide a framework for a comprehensive solution to errors in algorithms.At the same time,it also summarizes the problems and challenges in existing research and makes a prospect for future research trends.It is hoped that it will be helpful for us to build fairer AI.
文摘The application of Genetic Algorithm (GA) to the optimization of important parameters (Directivity, Radiated Power, Impedance etc.) of magnetically biased microstrip antenna, fabricated on ferrite substrate, is reported. The fitness functions for the GA program have been developed using cavity method for the analysis of microstrip antenna. The effect of external magnetic biasing has also been incorporated in the fitness function formulation as effective propagation constant. Using stochastic based search method of GA the common characteristics of electro-magnetic were entertained which cannot be handled by other optimization techniques. The genetic algorithm was run for 500 generations. The computed results are in good agreement with the results obtained experimentally.
文摘The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of civil aviation, satellite based augmentation system (SBAS) has been planned by various countries including USA, Europe, Japan and India. The Indian SBAS is named as GPS Aided Geo Augmented Navigation (GAGAN). The GAGAN network consists of several dual frequency GPS receivers located at various airports around the Indian subcontinent. The ionospheric delay, which is a function of the total electron content (TEC), is one of the main sources of error affecting GPS/SBAS accuracy. A dual frequency GPS receiver can be used to estimate the TEC. However, line-of-sight TEC derived from dual frequency GPS data is corrupted by the instrumental biases of the GPS receiver and satellites. The estimation of receiver instrumental bias is particularly important for obtaining accurate estimates of ionospheric delay. In this paper, two prominent techniques based on Kalman filter and Self-Calibration Of pseudo Range Error (SCORE) algorithm are used for estimation of instrumental biases. The estimated instrumental bias and TEC results for the GPS Aided Geo Augmented Navigation (GAGAN) station at Hyderabad (78.47°E, 17.45°N), India are presented.
文摘This paper delves into the hidden impact of algorithmic bias on the allocation of online education resources.With the rapid development of online education,algorithms play a crucial role in resource allocation,but algorithmic bias has emerged as a significant issue.The study analyzes the impact of bias at three levels:data level,where data collection and annotation biases lead to uneven resource allocation and misdirected recommendations;algorithmic model level,with design flaws and bias accumulation during optimization causing unfair resource allocation decisions;and result level,imposing implicit restrictions on students’learning opportunities and posing potential threats to educational and social equity.Through case studies of Online Education Platform A and Online Education Project B,the actual manifestations and impacts of algorithmic bias are demonstrated.To address these problems,corresponding countermeasures are proposed,including data governance strategies to improve data quality,algorithmic optimization strategies to enhance fairness and transparency,and educational management and policy recommendations to strengthen regulation and promote algorithmic literacy.This research not only reveals the harm of algorithmic bias but also provides a comprehensive and systematic solution framework,which has important theoretical and practical significance for promoting fair resource allocation in online education and realizing educational equity.
