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In mechanical engineering, most mechanical structural systems are composed of many parts, so their structures are complex. When a mechanical structure fails, it is generally caused by various failure modes. When designers want to improve the quality of mechanical products in the design of mechanical products, reliability analysis can play an important role. It is indispensable in the design and manufacture of mechanical products, engineering use, repair and maintenance. Research on time-varying reliability of mechanical structures is of great significance in improving the quality of mechanical products and bringing more economic benefits to engineering. It has received extensive attention in both academic fields and practical engineering applications. This paper takes the time-varying reliability analysis of mechanical structures based on performance degradation as the research topic, focusing on the reliability analysis methods and techniques of mechanical structures such as Gamma degradation process, material P-S-N curve, mixed Copula function, and nested Copula function. The main research content is divided into three parts. (1) The Gamma degradation process and the P-S-N material probabilistic fatigue life curve are used when establishing the strength degradation random model of the mechanical system. It provides the theoretical basis of strength degradation level for time-varying reliability analysis of mechanical structure based on performance degradation. (2) Combine the mixed Copula function and the nested Copula function to establish a comprehensive failure correlation analysis model of the mechanical system. It provides a theoretical basis for failure correlation for time-varying reliability analysis of mechanical structures based on performance degradation. (3) The time-varying reliability analysis of the one-stage gear transmission system in the engineering example is carried out. Researched and analyzed the time-varying reliability of the large and small gears in the one-stage gear transmission system and the mechanical structure of the system itself under the correlation of strength degradation and failure. more...

There are uncertain factors affecting structural performance in actual engineering. The uncertainty-based design and optimization (UBDO) is an effective strategy to deal with these uncertain factors and improve the reliability of structural systems. In UBDO, some non-probabilistic uncertainties are difficult to be described with a certain distribution information. In this situation, the interval uncertainty analysis (IUA) can be utilized to perform the reliability evaluation in UBDO. Generally, if IUA was performed directly in UBDO, the efficiency of UBDO with IUA (UBDO-IUA) cannot satisfy the requirements of actual engineering. To tackle this problem, in this study, an UBDO-IUA strategy based on the Taylor expansion is proposed. The particle swarm global optimization algorithm is also introduced in the given method as the optimization solver to further improve the computational efficiency. Two examples are introduced to show the utilization of the given method. more...

Generally, the traditional uncertainty-based multidisciplinary design optimization (UBMDO) methods are based on the probability distribution information of random design variables and parameters. However, the probability distribution information needs to be obtained based on a large number of sample points. In many engineering structure design problems, for some uncertainties, it is difficult to obtain enough experimental samples to construct their accurate probability distribution, only the variation interval of them can be known. In this situation, mixed uncertainties exist in the engineering systems. Furthermore, with the increase in the complexity of engineering systems, these mixed uncertainties will even produce cumulative effects along with the internal coupling of the systems themselves. To tackle these challenges, the random and interval variables (RIV) are considered and a strategy of UBMDO with RIV (UBMDO-RIV) is proposed in this study. In the given method, the evaluation of uncertainty constraints is performed in the worst case scenario due to interval uncertainties. Meanwhile, the classic decoupling strategy for UBMDO, the framework of sequential design and uncertainty evaluation (SDUE), is introduced into UBMDO-RIV to reduce the computational burden. The engineering case study is utilized to illustrate the application of the proposed strategy. more...

With the high speed developing technologies of artificial intelligence technology and Internet multimedia, the environment of social public opinion tends to be complex, which puts forward new challenges to the ideological and political education (IPE) of colleges and universities (IPECU) as well as the innovation and entrepreneurship education (IEE) of college students. Under the platform of artificial intelligence and multimedia teaching, the integration of IPE and IEE of college students should be adjusted appropriately according to the actual situation. The goal, content and mode of college students’ IEE and IPE have natural commonality under the platform of artificial intelligence and multimedia teaching, and they promote and influence each other. This paper is to further explore the integration strategy of artificial intelligence and multimedia teaching in IPECU of entrepreneurship education through the analysis of the existing problems and causes in colleges and universities. The author summarizes the core coupling of the current situation of IPECU in entrepreneurship education, as well as the influencing factors of the external environment, and puts forward the corresponding countermeasures. more...

In this paper, the mathematical model and algorithm based on knowledge forgetting curve are studied. Through the analysis of the current mathematical modeling and application of “knowledge forgetting curve”, the artificial intelligence method of fuzzy mathematics knowledge and differential modeling is adopted. This paper puts forward the mathematical model and algorithm design of the new “knowledge forgetting curve”, which aims to improve the intelligence of the software and bring a new learning experience for the teaching evaluation of the education system in colleges and universities. The fuzzy logic theory is applied to the teaching evaluation system of higher learning pedagogy, according to pedagogy and other related theories, combined with the current teaching evaluation indicators of colleges and universities, the teaching evaluation indicators of higher learning education are set according to certain requirements. The sample wood data is divided into two parts by using the fuzzy logic principle, and the training model is obtained by training the sample data in the evaluation system, and the training model is used to intelligently evaluate and analyze the prediction data. more...

Shared resources facilitate stealthy communication channels, including side channels and covert channels, which greatly endanger the information security, even in cloud environments. As a commonly shared resource, DRAM memory also serves as a source of stealthy channels. Existing solutions rely on two common features of DRAM-based channels, i.e., high cache miss and high bank locality, to detect the existence of such channels. However, such solutions could be defeated.In this paper, we point out the weakness of existing detection solutions by demonstrating a new advanced DRAM-based channel, which utilizes the hardware Intel SGX to conceal cache miss and bank locality. Further, we propose a novel neural network based solution DRAMD to detect such advanced stealthy channels. DRAMD uses hardware performance counters to track not only cache miss events that are used by existing solutions, but also counts of branches and instructions executed, as well as branch misses. Then DRAMD utilizes neural networks to model the access patterns of different applications and therefore detects potential stealthy communication channels. Our evaluation shows that DRAMD achieves up to 99% precision with 100% recall. Furthermore, DRAMD introduces less than 5% performance overheads and negligible impacts on legacy applications. more...

Microarchitectural timing channels, e.g., timing-based side channels or covert channels, endanger victims' data confidentiality by accurately measuring the time difference of accessing shared microarchitectural resources (e.g., cache and DRAM). Lowering the accuracy of such timers is a feasible and widely discussed direction for mitigating such channels. However, few solutions have paid attentions to the special dedicated thread timer, which measures the time by utilizing a dedicated thread to increment a time counter in an endless loop.In this paper, we present a novel approach, DegradeTimer, to degrade the dedicated thread timer. It first eliminates local cache sharing between the attacker thread and the timer thread in case the CPU's hyper-threading feature is enabled, by modifying Linux kernel's thread scheduling policy to enforce that any two threads sharing writable memory are dispatched to different physical cores. Then, it applies two novel cache coherence protocols, M-MESI and M-MOSEI, to randomly delay responses to remote cache requests. In this way, the attacker thread and the timer thread are dispatched to different cores and provided with fuzzy cache time transmission. We have implemented a prototype of DegradeTimer for both x86 and ARM architecture using the Linux kernel and the gem5 full system simulator. The evaluation results show that DegradeTimer provides a strong security guarantee against microarchitectural timing channels. Furthermore, the performance overhead introduced by DegradeTimer is less than 6%. more...

Nowadays, masquerade attack caused by identity misuse is a kind of severe insider threat and a common security problem for most organizations. Although the anomaly detection based on machine learning has been applied as a common method for this problem, most existing approaches for detecting masquerade attack usually use host-based data to profile user behavior and detect anomaly. However, host-based data are too sensitive to collect, which results in poor universality and makes it difficult for enterprises to deploy. In this paper, we propose a cheap and general masquerade detection method with high accuracy to profile user network behavior and detect anomaly, using network packet sketches (IP, port, protocol, etc As network packet headers with standardized format are non-sensitive, our method is suitable for most enterprises and much cheaper to deploy. To validate the method, we collected more than 10 TB normal user data and 5 GB simulated masquerader data in real enterprise environment. The experimental results show that our method achieves good performance with average AUC up to 0.988 and the approach of modeling user network behavior by network packet sketches results in good time efficiency. more...

This paper constructs a teaching resource sharing system based on big data platform technology, which provides a useful solution for the preservation and sharing of a large number of teaching resources at present and in the future, and accelerates the use of teaching resources and data, making accurate analysis and search become possible. While maintaining the stability of the system, we will improve the security and scalability of the big data platform system, and upgrade education and education informatization to a new level to support big data technology. This article details the software optimization of real-time embedded intelligent system based on non-volatile hybrid memory. This article introduces the main model and problem definition, the pulse-based distance accumulation (PBDA) algorithm proposed in the auxiliary case analysis and the detailed introduction of the auxiliary probability-based data allocation algorithm. The online golden course resources part of online and offline hybrid education, that is, the establishment of online public courses, has been recorded in schools at all levels, but there is no relatively complete warranty policy for offline golden course resources. The offline classroom teaching part is a particularly important part of the university’s mixed teaching, and this part is the embodiment of classroom teaching. The use of data can provide accurate decision-making for training, thereby providing teachers with personalized training and overall training support, and can more reasonably make teaching assistant work a demand of the times. However, the accumulation of data volume does not indicate the value of data. The current results are not satisfactory. One of the reasons is that data quality must be improved, and improving the quality of education data through data governance is one of the focuses of the education industry. more...