Bibliographic record
Abstract
MESA06—The 2nd IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications Beijing, China on August 13–16, 2006Jorge Angeles, McGill University, CanadaDavid M. Auslander, U. of California, Berkeley, USATianyou Chai, Northeast University, ChinaKrishna C. Gupta, U. of Illinois at Chicago, USARen C. Luo, National Chung Cheng University, ChinaMichael McCarthy, U. of California, Irvine, USABahram Ravani, U. of California, Davis, USAT. J. Tarn, Washington University, USAMasayoshi Tomizuka, U. of California, Berkeley, USAYoulun Xiong, Huazhong U. of Science and Technology, ChinaFeiyue Wang, U. of Arizona and Chinese Academy of Sciences, USANanning Zheng, Xian Jiaotong University, ChinaDongming Guo, Dalian U. of Technology, ChinaHarry H. Cheng, U. of California, Davis, USAYing Chen, Hangzhou Dianzi University, ChinaHanqi Zhuang, Florida Atlantic University, USAS. Felix Wu, U. of California, Davis, USADalei Guo, Chinese Academy of Sciences, ChinaYanqing Guo, University of Arizona, USADaniel Zeng, University of Arizona, USAHuayong Yang, Zhejiang University, ChinaQ. Jeffrey Ge, State University of New York, USAFrederick M. Proctor, National Institute of Standards and Technology, USAZhilie Chen, Shenzhen Evoc Intelligent Technology Co. Ltd., ChinaLefei Li, University of Arizona, USAXudong Hu, Zhejiang Sci-Tech University, ChinaJian S. Dai, University of London, UKBo Chen, University of California, Davis, USAPeihua Gu, Shantou University, ChinaZuomin Dong, University of Victoria, CanadaDu Zhang, California State University, USAChunheng Wang, Chinese Academy of SciencesLi Zheng, Tsinghua University, ChinaHuiguang He, Chinese Academy of SciencesHao Hong, Chinese Academy of Sciences, Chinamesa2006@gmail.comIEEE Intelligent Transportation Systems SocietyASME Division of Computer and Information in EngineeringASME Division of Design EngineeringChinese Association for AutomationChinese Mechanical Engineering SocietyNational Natural Science Foundation of ChinaMechanical and electrical engineering show an increasing integration of mechanics with electronics and information processing. This integration is between the components (hardware) and the information-driven functions (software), resulting in integrated systems called mechatronic systems. The development of mechatronic systems involves finding an optimal balance between the basic mechanical structure, sensor and actuator implementation, automatic digital information processing, and overall control for which embedded systems play a key role. The field of embedded systems is getting more and more challenging, and issues in development of embedded software are attracting the attention of an increasing number of researchers both in industry and academia. The goal of MESA06 is to bring together experts from the fields of mechatronic and embedded systems to disseminate the recent advances made in the area, discuss the future research directions, and exchange application experience with respect to the conference themes.Mechatronics and RoboticsAnalysis, modeling, and simulationAutonomous mobile robotsAdvanced controlSystem designRobots and mobile devicesMan machine interfacesOpen architecture system integrationEmbedded Systems Infrastructure and TheorySystem-on-a-chip (SoC) technologyEmbedded microcontrollersMultiprocessorsHardware specificationSynthesis, modeling, simulation, and analysisPower-awareEmbedded system securityReal-time issuesSoftware architecturesVirtual machinesOS and middleware supportMemory management supportHardware/software co-designSensors and MEMsAnalysis, modeling, and simulationMEMS and NEMSSensor design, integration, and fusionSensor networksNetworked Mechatronic and Embedded SystemsCommunication toolsReconfigurable, scalable, and interoperable middleware developmentNetwork-on-chipComponent-based approachesMobile and agent-based computingDevelopment, Verification, and Debug Tools for Mechatronic and Embedded SystemsCompilersAssemblers and cross assemblersSystem design toolsTest and debug strategiesEmulators and simulatorsDebuggersSoftware simulations of hardware componentsMechatronic and Embedded System ApplicationsChallenges, requirements, model problems, and constraints associated with various application domainsUse of mechatronic and embedded technologies in meeting particular system requirements, performance, scalability, reliability, and securityAssessments of mechatronic and embedded technologies for particular application domainsTechnology transition lessons learnedApplications in intelligent transportation systemsApplications in intelligent manufacturing and automation systemsEducation in Mechatronics and Embedded ComputingInnovations in course, curriculum, laboratory developmentDevelopment of teaching tools and innovative teaching strategiesIntegration of emerging technologies into the undergraduate and graduate programsComplete manuscripts in PDF format must be electronically submitted to the conference website http://www.asmemesa.org. Submitted manuscripts should be six (6) pages or less in IEEE two-column format, including figures, tables, and references.May 10, 2006—Full paper, proposal for special session, workshop and tutorialJune 20, 2006—Notification of acceptanceJuly 1, 2006—Camera ready paper submissionFor detailed information, please visit the conference website at http://www.asmemesa.org or https://150.135.155.193/mesa06/
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.888 | 0.858 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".