Analysis on Training Model of Intellectual Property Personnel in an Enterprise

With the rapid development of the knowledge economy, intellectual property personnel training has become a national strategy. A new requirement was put forward to the enterprises during the “12th Five-Year Plan of IP talent”. However, now the quality of IP personnel has different levels, and the quantity of the IP professionals is always unqualified. The enterprises need the IP talents with a higher level of quantity and quality. This phenomenon is closely related to the current intellectual property personnel training. It is obvious that the problem’s solution just rely on the professional education in university is shortcoming. As the main part of IP talent demand group，enterprises should pay more attention to their own cultivation. Based on this background , the article from the enterprise’s perspective define the concepts which is related with intellectual property personnel training and analyze the current problems of IP personnel training, such as the sense, systems, resources and other aspects. At last, the article point out that the key to solve these problems is to build a systematic multi-level and multi-channel IP talent training model. It can meet the demand of enterprises about intellectual property professionals and that has a great significance to improve the level of personnel quality. And it also has a great significance for enterprises to enhance their capabilities of creation and application to intellectual property, so that the enterprises could be able to achieve the goal of the innovative development. Key words: Intellectual property; Enterprise personnel training; Personnel training mode

Physics-Informed Optical Kernel Regression Using Complex-valued Neural Fields

Lithography is fundamental to integrated circuit fabrication, necessitating large computation overhead. The advancement of machine learning (ML)-based lithography models alleviates the trade-offs between manufacturing process expense and capability. However, all previous methods regard the lithography system as an image-to-image black box mapping, utilizing network parameters to learn by rote mappings from massive mask-to-aerial or mask-to-resist image pairs, resulting in poor generalization capability. In this paper, we propose a new ML-based paradigm disassembling the rigorous lithographic model into non-parametric mask operations and learned optical kernels containing determinant source, pupil, and lithography information. By optimizing complex-valued neural fields to perform optical kernel regression from coordinates, our method can accurately restore lithography system using a small-scale training dataset with fewer parameters, demonstrating superior generalization capability as well. Experiments show that our framework can use 31% of parameters while achieving 69$\times$ smaller mean squared error with 1.3$\times$ higher throughput than the state-of-the-art.Comment: Accepted by DAC2

Laboratory Study on Properties of Diatomite and Basalt Fiber Compound Modified Asphalt Mastic

In order to improve the performance of asphalt mastic, some researchers have added diatomite or basalt fiber as a modifier to the asphalt mastic, and the results show that some properties of the asphalt mastic were improved. For the simultaneous addition of diatomite and basalt fiber, two kinds of modifier, compound modified asphalt mastic had not been reported; in this paper, thirteen groups of diatomite and basalt fiber (DBFCMAM) compound modified asphalt mastic with different content were prepared to study the performance. Softening point, cone penetration, viscosity, and DSR tests were conducted, for the high temperature performance evaluation of DBFCMAM, whereas force ductility and BBR tests were used in the low temperature performance study of the DBFCMAM. The results demonstrated that the high temperature performance of DBFCMAM was increased; moreover, the low temperature performance of DBFCMAM improved by diatomite and basalt fiber according to the results of the force ductility test; however, the conclusion of the BBR test data was inconsistent with the force ductility test. In summary, the high temperature and low temperature properties of DBFCMAM had been improved

Application of Virtual Reality Based on 3D-CTA in Intracranial Aneurysm Surgery

As a popular technology in the field of human-computer interaction, virtual reality (VR) brings a brand new sensory experience to users by generating the environment. In recent years, while introducing the application of virtual reality technology, researchers have done a lot of work around virtual reality in many fields, such as the application of virtual reality technology in medical procedures. Combining the immersive and expandable features of virtual reality can improve the safety and accuracy of surgery. This article mainly introduces the application of 3D-CTA virtual reality technology in intracranial aneurysm surgery and aims to provide some ideas and directions for the improvement and progress of intracranial aneurysm surgery. This paper presents a research method based on virtual reality technology 3D-CTA in intracranial aneurysm surgery, including the application overview of 3D-CTA in intracranial aneurysm surgery and the virtual reality algorithm based on 3D-CTA for intracranial arteries. In addition, there is also the application of virtual reality CTA technology in the design of the intracranial aneurysm application system. Experimental results show that the average accuracy of 3D-CTA diagnosis based on virtual reality is 90.81%, and it can be put into use in the next step

Experimental Study and Numerical Simulation on Failure Process of Reinforced Concrete Box Culvert

Culvert is an important part of roads whose healthy operation is related to the efficiency and safety of road transportation. Therefore, it is very important to evaluate the safety of culvert structure by load test. Four types of prefabricated reinforced concrete box culverts (integral BC, round hinged BC, flat seam BC, and mortise BC) were designed in this paper. By designing a scale model test, the sensor system was used to test the mechanical properties of BC, which included dial indicators, strain gauges, and a pressure sensor. The finite element analysis based on material nonlinearity and contact nonlinearity of round hinged BC and integral BC was carried out. After validating the finite element models, mechanical properties of reinforcement and concrete of BCs were analyzed. The experimental results show that the failure mode of BC was tensile failure of concrete at the bottom of top slab under bending action, and integral BC had the maximum carrying capacity. The contact behaviour of sliding and rotating at hinge joints caused the first principal tensile stress of concrete at the internal surface of the side wall below hinge joints

Battery Grouping with Time Series Clustering Based on Affinity Propagation

Battery grouping is a technology widely used to improve the performance of battery packs. In this paper, we propose a time series clustering based battery grouping method. The proposed method utilizes the whole battery charge/discharge sequence for battery grouping. The time sequences are first denoised with a wavelet denoising technique. The similarity matrix is then computed with the dynamic time warping distance, and finally the time series are clustered with the affinity propagation algorithm according to the calculated similarity matrices. The silhouette index is utilized for assessing the performance of the proposed battery grouping method. Test results show that the proposed battery grouping method is effective

Locating Sensors in Complex Engineering Systems for Fault Isolation Using Population-Based Incremental Learning

Fault diagnostics aims to locate the origin of an abnormity if it presents and therefore maximize the system performance during its full life-cycle. Many studies have been devoted to the feature extraction and isolation mechanisms of various faults. However, limited efforts have been spent on the optimization of sensor location in a complex engineering system, which is expected to be a critical step for the successful application of fault diagnostics. In this paper, a novel sensor location approach is proposed for the purpose of fault isolation using population-based incremental learning (PBIL). A directed graph is used to model the fault propagation of a complex engineering system. The multidimensional causal relationships of faults and symptoms were obtained via traversing the directed path in the directed graph. To locate the minimal quantity of sensors for desired fault isolatability, the problem of sensor location was firstly formulated as an optimization problem and then handled using PBIL. Two classical cases, including a diesel engine and a fluid catalytic cracking unit (FCCU), were taken as examples to demonstrate the effectiveness of the proposed approach. Results show that the proposed method can minimize the quantity of sensors while keeping the capacity of fault isolation unchanged