Approximate optimal AUTOSAR software components deploying approach for automotive E/E system

The AUTOSAR has been developed as the worldwide standard for automotive E/E software systems, making the electronic components of different suppliers to be employed universally. However, as the number of component-based applications in modern automotive embedded systems grows rapidly and the hardware topology becomes increasingly complex, deploying such large number of components in automotive distributed system in manual way is over-dependent on experience of engineers which in turn is time consuming. Furthermore, the resource limitation and scheduling analysis make the problems more complex for developers to find out an approximate optimal deploying approach in system integration. In this paper, we propose a novel method to deploy the AUTOSAR components onto ECUs with the following features. First, a clustering algorithm is designed for deploying components automatically within relatively low time complexity. Second, a fitness function is designed to balance the ECUs load. The goal of our approach is to minimize the communication cost over all the runnable entities while meeting all corresponding timing constraints and balancing all the ECUs load. The experiment results show that our approach is efficient and has well performance by comparing with other existing methods in specific and synthetic data set.     

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.     

Fault diagnosis for singular stochastic systems

This paper studies the fault diagnosis of singular stochastic systems. The probability distribution of output is measured by probability density functions (PDFs), which are modeled by a square root B-spline expansion. An adaptive nonlinear observer is proposed to estimate the size of the fault occurring in systems. Furthermore, the linear matrix inequality (LMI) approach is applied to establish sufficient conditions for the existence of the observer. Finally, the simulation results are given to indicate the method for diagnosing the fault.     

Shared pilot passage plan and navigational safety during pilotage

Navigating in confined waters with a pilot aboard requires that the pilot’s intention and plan is understood by all present on the bridge. The present study investigates the effect of having a detailed route plan and monitoring it in the context of pilotage. The aim was to see how the presence of a shared pilot passage plan in the Electronic Chart Display and Information System (ECDIS) affects the identification and recovery from navigational errors made by a pilot. Twenty participants, 10 with a shared route plan and 10 without, participated as navigators in a simulator scenario involving pilotage in the Oslofjord. Participants were bachelor students in nautical science. The navigation scenarios involved the intentional error by the pilot just before a predefined way point. Three measurements of relevance to navigational safety were recorded: (1) the time it took the participant to express concern, (2) the time it took the participant to correct the error (‘time to recovery’), and (3) the number of groundings. The study revealed that time to express concern and time to recovery were significantly much shorter for the group with a shared pilot passage plan presented in ECDIS than for the group without such a plan. Providing a bridge team with a shared pilot passage plan in ECDIS during pilotage might improve the ability to identify and correct navigational errors.     

Vocational and academic approaches to maritime education and training (MET): Trends,challenges and opportunities

Traditional seafarer training has always focused on the acquisition and use of practical skills. The prevailing view is that, while this approach addresses a degree of cognitive skills, it focuses on and gives much more emphasis to the acquisition of hands-on practical skills for the performance of specific tasks. On the other hand, academic education has been seen to be much more focused on the development of in-depth analytical and critical thinking skills; cognitive skills that are less reliant on hands-on task-oriented training, but stress critical reading and discussion. The global trend in maritime education and training is increasingly to link an essentially vocational education that provides specific and restricted competence outcomes with more general or deeper academic components leading to an academic qualification. This trend has led to some dilemmas for curriculum development, for training legislation in a global industry, and for achieving desired learning outcomes in a professional setting (in the shipping industry). This paper discusses some of the challenges arising from this trend and the opportunities the trend offers.     

Rapid first-stage tests of on-ship infection

Protection against on-ship infectious disease—whether due to mishap or to harmful purpose—faces special situational problems. Sometimes, when infection levels on board have reached threshold levels, emergency actions are required. Often, the most thorough strategies for responding to threat are not feasible. A rapid first-stage test (RFT) is a fast, minimally invasive procedure used to rule out from possible infection a large percentage of an infection-threatened group. Prevention and control of on-ship infection need to combine various interconnected tactics. When timely criterion tests are not possible, the medical team must adopt fast alternative measures. The methods used to summarize protection against on-ship infectious agents included a scientific literature review and a web search. The fields of the review were maritime, health, and technology sources. Special attention was paid to material dealing with risks and threats of on-ship penetration by infectious agents, on-ship infection prevalence thresholds, and rapid diagnostic screens. The Bayes rule and the law of large numbers were applied to the analysis, for large on-ship populations, of RFT indications of crossing of an infection prevalence threshold. The increasing risk of serious on-ship infection—either accidental or purposeful—calls for a multi-layered protection approach. RFTs are a key part of the outer layer of such a defense. Well-designed and well-administered RFTs provide several advantages for defense against on-ship infection: low-cost, non-invasive, fast, and focuses on a drastically smaller number of infection possibilities.