Design of motion control of dam safety inspection underwater vehicle

Plenty of dams in China are in danger while there are few effective methods for underwater dam inspections of hidden problems such as conduits,cracks and inanitions.The dam safety inspection remotely operated vehicle(DSIROV) is designed to solve these problems which can be equipped with many advanced sensors such as acoustical,optical and electrical sensors for underwater dam inspection.A least-square parameter estimation method is utilized to estimate the hydrodynamic coefficients of DSIROV,and a four degree-of-freedom(DOF) simulation system is constructed.The architecture of DSIROV's motion control system is introduced,which includes hardware and software structures.The hardware based on PC104 BUS,uses AMD ELAN520 as the controller's embedded CPU and all control modules work in VxWorks real-time operating system.Information flow of the motion system of DSIROV,automatic control of dam scanning and dead-reckoning algorithm for navigation are also discussed.The reliability of DSIROV's control system can be verified and the control system can fulfill the motion control mission because embankment checking can be demonstrated by the lake trials.

Analysis of Safety Assessment and Testing of Heavy Traffic Vehicles on Old Bridges Without Data

This article presents a real-life project that aimed to evaluate the safety of traffic vehicles on old bridges without any prior data.The project involved various safety inspections,including conventional,static,and dynamic load inspections and safety assessments.After conducting these tests,it was concluded that the structure of the old bridge is relatively safe,with only a few bumps.The bridge could function normally following appropriate treatment.The analysis provides valuable insights into the assessment of the quality and safety of such bridges to ensure the safe driving of heavy vehicles.

Scientific Exploration and Practice of Safe Production Management of Non-Ferrous Metal Mines under the New Situation—Taking Dahongshan Copper Mine as an Example

Although Dahongshan Copper Mine, a subsidiary of Chinalco Co., Ltd., has a large production scale, it is still a non-ferrous metal underground mine with low mechanization and labor efficiency due to its early production. In recent years, mines have carried out a large number of beneficial scientific explorations and practices on production safety management. According to their own actuality, they have combined production safety with scientific management. They have done meticulously and solidly, ensuring that the safety situation continues to be good. There have been no serious injury accidents, let alone work-related accidents for two consecutive years. The injury rate of thousands of people is in the good results within the control index have also made it a benchmark for safety management in the nonferrous mines of Chinalco. This paper analyzes and discusses the scientific methods and good experiences of the production safety of Dahongshan Copper Mine from six aspects: explosion management, organizational security, infrastructure, safety inspection and quantitative mechanism, safety training, safety science and technology, etc. Finally, it gives suggestions for the next production safety of Dahongshan Copper Mine.

Bridging the gap:Neuro-Symbolic Computing for advanced AI applications in construction

Deep Learning(DL)has revolutionized the field of Artificial Intelligence(Al)in various domains such as computer vision(CV)and natural language processing.However,DL models have limitations including the need for large labeled datasets,lack of interpretability and explainability,potential bias and fairness issues,and limitations in common sense reasoning and contextual understanding.On the other side,DL has shown significant potential in construction for safety and quality inspection tasks using CV models.However,current CV approaches may lack spatial context and measurement capabilities,and struggle with complex safety and quality requirements.The integration of Neuro-Symbolic Computing(NSC),an emerging field that combines DL and symbolic reasoning,has been proposed as a potential solution to address these limitations.NSC has the potential to enable more robust,interpretable,and accurate AI systems in construction by harnessing the strengths of DL and symbolic reasoning.The combination of symbolism and connectionism in NSC can lead to more efficient data usage,improved generalization ability,and enhanced interpretability.Further research and experimentation are needed to effectively integrate NSC with large models and advance CV technologies for precise reporting of safety and quality inspection results in construction.