Insights into vehicle trajectories at the handling limits: analysing open data from race car drivers

Race car drivers can offer insights into vehicle control during extreme manoeuvres; however, little data from race teams is publicly available for analysis. The Revs Program at Stanford has built a collection of vehicle dynamics data acquired from vintage race cars during live racing events with the intent of making this database publicly available for future analysis. This paper discusses the data acquisition, post-processing, and storage methods used to generate the database. An analysis of available data quantifies the repeatability of professional race car driver performance by examining the statistical dispersion of their driven paths. Certain map features, such as sections with high path curvature, consistently corresponded to local minima in path dispersion, quantifying the qualitative concept that drivers anchor their racing lines at specific locations around the track. A case study explores how two professional drivers employ distinct driving styles to achieve similar lap times, supporting the idea that driving at the limits allows a family of solutions in terms of paths and speed that can be adapted based on specific spatial, temporal, or other constraints and objectives.     

Modeling research on manufacturing execution system based on large-scale system cybernetics

A cybernetics model of manufacturing execution system(MES CM) was proposed and studied from the viewpoint of cybernetics.Combining with the features of manufacturing system, the MES CM was modeled by"generalized modeling"method that is discussed in large-scale system theory.The mathematical model of MES CM was constructed by the generalized operator model, and the main characteristics of MES CM were analyzed.     

A data model applied to the knowledge-based ICAS

Image-guided computer aided surgery system (ICAS) contributes to safeness and success of surgery operations by means of displaying anatomical structures and showing correlative information to surgeons in the process of operation. Based on analysis of requirements for ICAS, a new concept of clinical knowledge-based ICAS was proposed. Designing a reasonable data structure model is essential for realizing this new concept. The traditional data structure is limited in expressing and reusing the clinical knowledge such as locating an anatomical object, topological relations of anatomical objects and correlative clinical attributes. A data structure model called mixed adjacency lists by octree-path-chain (MALOC) was outlined, which can combine patient's images with clinical knowledge, as well as efficiently locate the instrument and search the objects' information. The efficiency of data structures was analyzed and experimental results were given in comparison to other traditional data structures. The result of the nasal surgery experiment proves that MALOC is a proper model for clinical knowledge-based ICAS that has advantages in not only locating the operative instrument precisely but also proving surgeons with real-time operation-correlative information. It is shown that the clinical knowledge-based ICAS with MALOC model has advantages in terms of safety and success of surgical operations, and help in accurately locating the operative instrument and providing operation-correlative knowledge and information to surgeons in the process of operations.     

Analysis and improvement of authenticatable ring signcryption scheme

We show that the Zhang-Yang-Zhu-Zhang identity-based authenticatable ring signcryption scheme is not secure against chosen plaintext attacks.Furthermore, we propose an improved scheme that remedies the weakness of the Zhang-Yang-Zhu-Zhang scheme.The improved scheme has shorter ciphertext size than the Zhang-Yang-Zhu-Zhang scheme.We then prove that the improved scheme satisfies confidentiality, unforgeability, anonymity and authenticatability.     

Power network parameter estimation method based on data mining technology

The parameter values which actually change with the circumstances, weather and load level etc.produce great effect to the result of state estimation. A new parameter estimation method based on data mining technology was proposed. The clustering method was used to classify the historical data in supervisory control and data acquisition (SCADA) database as several types. The data processing technology was impliedto treat the isolated point, missing data and yawp data in samples for classified groups. The measurement data which belong to each classification were introduced to the linear regression equation in order to gain the regression coefficient and actual parameters by the least square method. A practical system demonstrates the high correctness, reliability and strong practicability of the proposed method.     

Marine accident analysis for collision and grounding in oil tanker using FTA method

In this study, collision and grounding data registered in GISIS (Global Integrated Shipping Information System) were investigated for oil tankers. The database includes the information of the collision and grounding accidents during the period between 1998 and 2010 in oil tankers. The risk assessments were carried out using fault tree analysis (FTA) programme for the incidents as collision and grounding occurred in oil tankers. In this study, we were able to investigate first the potential problems which cause the collision and grounding accidents have been determined, second, the occurrence of accidents has been shown with causal factors by the FTA method, and, finally, the significance degree of the initial events causing occurrence of accidents have been put forth. Collision in oil tanker resulted in economical loss (81%), pollution (6%) and death or injury (13%). Grounding in oil tanker resulted in economical loss (91%) and pollution (9%). According to the FTA results, the main reason for the accidents originating from human error is as follows: for collision accidents, Convention on the International Regulations for Preventing Collisions at Sea (COLREG) violation and the lack of communication between vessels; and for grounding accidents, the interpretation failure of the officer on watch and lack of communication in the bridge resource management.     

Virtual prototype simulation on underwater hydraulic impingement shovel

The virtual prototype technology is applied to the design of the hydraulic impingement shovel, which is to increase the reliability of the design. The work principle of hydraulic impingement shovel is expatiated, and its dynamic equations are established. The 3D model of virtual prototype is built by PRO/E. Then the couple between the mechanical body of prototype and the hydraulic system is completed by virtue of ADAMS. Finally, the simulation is made on the virtual prototype. The simulation results show that the design of underwater hydraulic impingement shovel is rational. The virtual prototype technology could lay sound foundation of successful manufacturing of physical prototype for the first time and offer highly effective and feasible means for the design and production of underwater equipments.     

Experimental study on the width of the turbulent area around bridge pier

At present, the method of calculating the turbulent flow width around the bridge pier is not given in the "Standard for Inland River Navigation" (GB50139-2004) in China, and the bridge designer usually increases the bridge span in order to ensure the navigation safety, which increases both of the structural design difficulty and the project investments. Therefore, it is extremely essential to give a research on the turbulent flow width around the bridge pier. Through the experiments of the fixed bed and the mobile bed, the factors influencing the turbulent flow width around the bridge pier have been analyzed, such as the approaching flow speed, the water depth, the angles between the bridge pier and the flow direction, the sizes of bridge pier, the shapes of the bridge pier, and the scouring around the bridge pier, etc. Through applying the dimension analytic method to the measured data, the formula of calculating the turbulent flow width around the bridge pier is then inferred.     

Using a SVM to evaluate damage to vibration isolators in an elastic raft system

Predicting damage to vibration isolators in a raft experiencing heavy shock loadings from explosions is an important task when designing a raft system. It is also vital to be able to research the vulnerability of heavily shocked floating rafts unreliable, especially when the allowable values The conventional approach to prediction has been or ultimate values of vibration isolators of supposedly uniform standard in a raft actually have differing and uncertain values due to defective workmanship. A new model for predicting damage to vibration isolators in a shocked floating raft system is presented in this paper. It is based on a support vector machine（SVM）, which uses Artificial Intelligence to characterize complicated nonlinear mapping between the impacting environment and damage to the vibration isolators. The effectiveness of the new method for predicting damage was illustrated by numerical simulations, and shown to be effective when relevant parameters of the model were chosen reasonably. The effect determining parameters, including kernel function and penalty factors, has on prediction results is also discussed. It can be concluded that the SVM will probably become a valid tool to study damage or vulnerability in a shocked raft system.