Offshore oil and gas activities involve risks and have the potential for major accidents, where undesirable events can escalate into acute pollution or losses of production and even human lives. To protect against such events, safety-instrumented systems are installed for early alarming, shutdowns, and mitigation of consequences. Safety-instrumented systems are attracting increasing attention. However, the previous five years show an apparently increasing trend in notifications related to so-called 'non-verifiable incidents' in posterity for these systems, where the causal factor often appears to be insufficiently understood and improperly documented at an early stage.<br/>Today's safety-instrumented systems widely record analogue values from a large number of instruments, where automatic alarm and activation are based on pre-configured limits (acceptance criteria). These are raw amperage data that are rarely presented as is in daily operation; instead, they are converted to numerical values and visualized using image processing software, i.e., in mimics. However, this is considered insufficient to establish proper situational awareness. To differentiate between different conditions, the recorded values should be seen in context with system design and expected signature curves for actual activations. <br/>In this article, our hypothesis is that a specific safety-instrumented system, having its defined functionality, will produce a unique signature curve when the assigned function is carried out as intended, and that knowledge of this curve can be used to filter out other faulty conditions. To test this hypothesis, we study relevant data recorded from sprinkler and deluge systems at an offshore oil and gas installation on the Norwegian Continental Shelf (NCS). By embedding trends with recorded data from the instruments into existing mimics, we show, based on the results, that real incidents can be recognized. This can potentially reduce the extent of notifications related to non-verifiable incidents.

The present study explores the design of a flat wall rectilinear 3D diffuser with square inlet and a rectangular outlet for subsonic, incompressible flow. In this 3D diffuser, the diffuser walls are diverged in the horizontal plane to control the flow separation from the walls of the diffuser, which results in a decrease in the coefficient of static pressure recovery; this is a phenomenon called ‘stall’. In case of a wide angled 2D diffuser, 'stall' is a serious issue. In present work, a 3D diffuser was designed with the same area ratio (4), aspect ratio (1), and non-dimensional length (8.21) as those of a 2D diffuser. With varying Reynolds numbers, it was found that the coefficient of static pressure recovery increases continuously for the 3D diffuser. In case of the 2D diffuser, it decreases as the Reynolds number exceeds 2×10⁵. From CFD analysis, it was found that vectors lie in the mid-longitudinal plane. A three-hole probe was used to measure local velocity to achieve velocity profiles at five different -2 equally distant planes between the inlet and outlet. The maximum error in the experimental results and that of the CFD analysis for the velocity profile and coefficient of static pressure recovery were 9% and 1.02%, respectively.

The study of ABS structures in their different stages of life cycle involves taking into account several complicated phenomena. However, the effect of temperature and geometric discontinuity in the material has been identified as morphological mechanisms responsible for the deterioration of its mechanical properties. Within the framework of this problem, this study is interested in the characterization of ABS performance and the evaluation of the effect of the defect on its mechanical behavior. The influence of discontinuity in the material was assessed by a series of uniaxial tensile tests on virgins and artificially damaged specimens. Subsequently, the results of the experimental tests were used to predict the damage and the instantaneous reliability of the material as well as to find a relationship between these two parameters through the fraction of life, which makes it possible to monitor the degradation of the material and predict the moment of acceleration of the damage. This technique can provide manufacturers with a predictive maintenance strategy.

Convolutional neural network is an important method to solve most computer vision problems nowadays. Although increasing the computing cost and the scale of model will make most tasks achieve satisfactory results, the difficulty of increasing the computing cost and high-quality data also limit the increase of model scale. In this paper, when using neural network to segment the remote sensing image, aiming at the problem that the classification effect of the internal pixels of the target is not ideal, a multi-scale fusion structure about the dimension of the feature map is proposed as the classifier module of the model. In order to further improve the performance of semantic segmentation model, inspired by the Generative adversarial nets, combined with super-resolution, generative semantic segmentation architecture is proposed. In order to verify the effect of the two methods, the kappa coefficient was selected as the evaluation to conduct the semantic segmentation experiment of the remote sensing image of seaculture. With little to no increase in the scale of the model, the classification ability of the model is improved, and the effect is compared intuitively from the segmentation image.

IaaS cloud computing data centers always employ live migration of virtual machines (VM) to achieve dynamic scheduling and management of IT resources. Pre-copy and Post-copy are two prevalent live migration algorithms. Both have pros and cons, and they work well only in suitable scenarios. Contrary to Pre-copy, Post-copy performs better with memory writing workloads running in VM to be migrated. Post-copy switches the VM to the target host immediately once the live migration is launched, and then it requests the source host to transfer memory pages whenever the VM accesses a nonexistent page. Although this Post-copy scheme can guarantee that every memory page needs to copy only once and the migration time is predictable, it can cause a mass of network requests for fault pages, which can lead to a prolonged downtime for VM and a degraded live migration performance. This paper proposes an improved Post-copy scheme with memory page prefetching, which leverages PPM (Prediction by Partial Match) to build a prediction model for memory page accessing and predict the pages to be read soon. Once a network page fault happens, this model determines the following pages to be accessed. Then, this faulted page together with these predicted pages are sent to the target host to avoid subsequent page faults. Experiment results show that this proposed scheme can allow for accurate page prediction and prominently decrease the network page faults and downtime for VM.

To improve the operational efficiency of airport surfaces, this paper studies the air traffic congestion prediction of airport surfaces, demonstrates the limitations of traffic congestion prediction, and proposes a prediction method for airport surface traffic congestion based on decision tree. Firstly, the definition and measurement methods of traffic congestion in airport surfaces are promoted. Then, the key factors affecting traffic congestion are extracted, and a prediction model of traffic congestion is established. Finally, we verify the validity of the model based on actual operation data from Atlanta. The results show that the accuracy of the prediction is 70%.

In order to represent the building energy consumption data in the existing relational database system, the traditional method needs to update and store the building location information frequently. This takes up a large amount of resources and drops the performance sharply, resulting in low efficiency of query. In order to overcome these problems, an index method based on hbstr tree in the cloud computing environment is proposed to model the spatial location of buildings and the time attribute of building energy consumption data. Through abstract methods, the frequently updated location and time information can be represented in a static way. On this basis, the building energy consumption data is updated and divided, and the existing relational database is used for storage and processing. The spatial-temporal characteristics of building energy consumption data are fully considered for data compression to obtain feature points, and the maximum and minimum distance method is used to select the initial clustering center. At the same time, combining the advantages of spatiotemporal R-tree, b * tree, and hash table, the index is constructed to realize the effective index of building energy consumption data. The experimental results show that the proposed method can ensure the efficient query of building energy consumption data in large-scale and multi concurrent numbers, and the query accuracy can meet the actual needs.

The condition monitoring of food cold chain quality management systems is the key to ensuring the stable operation of food cold chain quality management systems. In the process of food cold chain quality management systems, it is necessary to accurately detect and identify the quality of food cold chain systems, and the quality management technology of food cold chain systems based on big data analysis is put forward. The statistical analysis model of food cold chain system quality management evaluation is constructed. Through the same kind of mapping and closed frequency complex item collection evaluation method, the hierarchical cluster analysis of data is carried out. The criterion of food cold chain quality management systems can be obtained by the Sigma test. In the statistical regression analysis model, the attribute set of food cold chain quality distribution big data in food cold chain quality management systems is constructed. There are six secondary indexes under the management scale of the enterprise, five secondary indexes under the warehousing management level, four secondary indicators under the logistics level, three information indexes under the information level, and three evaluation indexes under the customer management level. By using this evaluation system, the development status and operation status of fresh cold chain logistics can be reflected comprehensively, and the quality management of food cold chain systems can be determined. It is found that the reliability of this method for quality management of food cold chain systems is good.

To improve the operation control ability of distribution networks, an operation control method based on a distributed data quality management system is proposed. A hybrid doubly-fed DC transmission configuration method is used to configure the quality distribution fusion feature of distribution network safety management combined with the dynamic parameter adjustment model of distribution network safety management quality distribution fusion feature is established. The interference suppression in the process of distribution network security management quality distribution fusion feature security control is carried out by combining the small disturbance steady-state suppression method. The distributed data quality management and information fusion method is used to adjust the error feedback and optimize the parameters in the process of distribution network security management quality distribution fusion feature security control, and the optimal design of distribution network operation control algorithm is realized. In additional, the proposed method has good output stability, large output gain and good anti-interference, and has a good guiding significance in the security networking control and distributed data quality management of distribution network. The simulation results show that the proposed method has good output stability, large output gain and good anti-interference ability in the security network control and distributed data quality management of distribution network.

There is a quality benefit error in the block collaborative supply chain that is affected by the environmental factors of the supply market. In order to improve the ability of collaborative quality evaluation, a supply chain collaborative quality evaluation method based using structural characteristics is proposed. The sampling model of collaborative quality characteristic information of block collaborative supply chain is constructed. According to the quantitative recurrent analysis results of the collaborative quality sample data, block chain information fusion is carried out, and the association rules fusion feature distribution parameter set of block collaborative supply chain collaborative quality panel data is extracted. The collaborative quality data fusion of block collaborative supply chain is carried out by using the method of random probability density feature detection. Combined with the method of piecewise linear estimation, the statistical feature quantity of supply chain collaborative quality evaluation is constructed. According to the prior sample quantitative recurrent analysis results of supply chain collaborative quality evaluation, the collaborative quality feature quantity is analyzed, and the cooperative quality feature quantity of block collaborative supply chain is extracted. The structure feature extraction and fusion clustering method are used for information clustering. According to the distributed fusion results of characteristics, the collaborative quality of supply chain is evaluated. The simulation results show that the proposed method has high accuracy and good confidence in the evaluation of supply chain collaborative quality, and it improves the ability of collaborative quality control of block collaborative supply chain.

By analyzing and comparing the G.729a speech coding specifications, this paper proposes an effective information hiding method. The proposed method hides the transmitted confidential information bits according to the need of the hiding information to adjust the last bit value of the excitation pulse position coding in G.729a speech coding, thereby realizing the embedding of secret information. We discussed and introduced in detail the information embedding and extraction algorithm of the proposed information hiding method. In order to test the feasibility and efficiency of the method studied, we conducted simulation experiments. The experiment used six kinds of audio material clips that we recorded, cut and edited by ourselves, namely children's male voice, children's female voice, male singing voice, female singing voice, Chinese classical music, and western music. Experiments show that when the secret information transmission rate is 200 bit/s and the hiding rate is 2.5%, the percentage of PESQ value reduction of the voice carrier proposed by the information hiding method proposed in this paper is 5.31%. While the secret information transmission rate is 300 bit/s and the hiding rate is 3.75%, the percentage of PESQ value reduction is 7.95%. The experimental results showed that the algorithm discussed in this paper has a sound covert communication capacity and strong concealment, indicating that the proposed algorithm demonstrates good information hiding performance.

Teaching quality has always been an important part of measuring the level of school education, and using mining algorithms to study the factors of teaching quality can effectively improve the quality of teaching. An improved Apriori algorithm was proposed aiming at the shortcomings of the Apriori mining algorithm, such as long time consumption and low algorithm efficiency. The algorithm improves the mining performance of the algorithm by adding databases, improving frequent 1-itemsets, improving frequent 2-itemsets, and introducing dynamic storage space. In simulation experiments, compared with other mining algorithms, the algorithm in this paper has a better analysis effect in teaching quality content, and can provide a useful reference for teaching quality.

Creativity can be challenging in the idea generation process as it is hard to formalise and control. However, there are requirements for creative ideas in many fields. Generating new and inspirational ideas is mainly manual work, and it usually happens in the individual human mind or a group of persons. There is a lack of software systems to generate creative ideas automatically. In this paper, a prototype software system based on a semantic reasoning method is proposed for assisting creativity in the general idea generation process. The kernel algorithm of the system is a set of inference rules designed on the basis of semantic computing technologies and creativity techniques, which is the core of the semantic reasoning. The fundamental information supporting the inference are domains knowledge managed as ontology bases. Furthermore, a major recommender is designed and implemented by employing the proposed idea creation method to enhance the inspiration level of university choice for teenagers. As a prototype software system, the developed major recommender application proves the feasibility and innovation of the proposed method.

With the rapid development of prefabricated buildings, using the existing system to manage prefabricated buildings faces the challenge of changing climates in cold areas. This paper analyzes the construction characteristics and the unique challenge in cold area for the purpose of improving existing tools, techniques and quality management system. Through the comparison between traditional contracting mode and proposed Quality Management and Safety Evaluation System (EPC), the advantages of EPC general contracting mode of prefabricated building are validated based on the aspects of quality, schedule, cost and safety. This paper demonstrates the necessity and reliability of popularizing and applying Quality Management and Safety Evaluation System for prefabricated building projects, and in particular, an improved theoretical basis for promoting the application of EPC general contracting mode of prefabricated buildings in cold regions.