In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to...In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.展开更多
The solar insecticidal lamp(SIL)is an innovative green control device.Nevertheless,a major challenge is often encountered when carrying out insecticidal work is low energy utilization efficiency.The substantial energy...The solar insecticidal lamp(SIL)is an innovative green control device.Nevertheless,a major challenge is often encountered when carrying out insecticidal work is low energy utilization efficiency.The substantial energy consumption required to turn on the SIL,coupled with the extension of insecticidal working time during the low pest activity periods,can result in low energy efficiency.Especially when the energy storage level is below 50%,the inefficient use of energy significantly reduces the effectiveness of pest control.Consequently,an ineffective on/off scheme for these lamps may lead to suboptimal energy utilization.In this paper,we present the solar insecticidal lamp intelligent energy management scheme(SIL-IEMS)to address the challenge of inefficient energy utilization in the solar insecticidal lamp internet of things(SIL-IoT).SIL-IEMS primarily utilizes genetic algorithm(GA)and greedy algorithms to optimize insecticidal working time by considering constraints such as residual energy and the number of trap pests.Comparing SIL-IEMS to the traditional remote switching method(TRSM)and the solar insecticidal lamp genetic algorithm(SILGA),our simulation results showcase its superior energy efficiency and pest control effectiveness.Particularly noteworthy is the SILIEMS's 17.6%increase in insecticidal efficiency compared to TRSM and 6%improvement over SILGA when the SIL begins with a remaining energy level of 15%.展开更多
基金supported by Institute of Information and Communications Technology Planning and Evaluation(IITP)grant funded by the Korean government(MSIT)(No.2019-0-01842,Artificial Intelligence Graduate School Program(GIST))supported by Korea Planning&Evaluation Institute of Industrial Technology(KEIT)grant funded by the Ministry of Trade,Industry&Energy(MOTIE,Republic of Korea)(RS-2025-25448249+1 种基金Automotive Industry Technology Development(R&D)Program)supported by the Regional Innovation System&Education(RISE)programthrough the(Gwangju RISE Center),funded by the Ministry of Education(MOE)and the Gwangju Metropolitan City,Republic of Korea(2025-RISE-05-001).
文摘In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.
基金supported in part by the National Natural Science Foundation of China(62072248).
文摘The solar insecticidal lamp(SIL)is an innovative green control device.Nevertheless,a major challenge is often encountered when carrying out insecticidal work is low energy utilization efficiency.The substantial energy consumption required to turn on the SIL,coupled with the extension of insecticidal working time during the low pest activity periods,can result in low energy efficiency.Especially when the energy storage level is below 50%,the inefficient use of energy significantly reduces the effectiveness of pest control.Consequently,an ineffective on/off scheme for these lamps may lead to suboptimal energy utilization.In this paper,we present the solar insecticidal lamp intelligent energy management scheme(SIL-IEMS)to address the challenge of inefficient energy utilization in the solar insecticidal lamp internet of things(SIL-IoT).SIL-IEMS primarily utilizes genetic algorithm(GA)and greedy algorithms to optimize insecticidal working time by considering constraints such as residual energy and the number of trap pests.Comparing SIL-IEMS to the traditional remote switching method(TRSM)and the solar insecticidal lamp genetic algorithm(SILGA),our simulation results showcase its superior energy efficiency and pest control effectiveness.Particularly noteworthy is the SILIEMS's 17.6%increase in insecticidal efficiency compared to TRSM and 6%improvement over SILGA when the SIL begins with a remaining energy level of 15%.
