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The monitoring of mechanical equipment systems contains an increasing number of complex content, expanding from traditional time and frequency information to three-dimensional data of the time, space and frequency information, and even higher-dimensional data containing subjects , experimental conditions. For high-dimensional data analysis, traditional decomposition methods such as Hilbert Transform, Fast Fourier Transformation and Gabor transformation not only lose the integrity of the data, but also increase the amount of calculation and introduce a lot of redundant information. The phenomenon of feature coupling, aliasing and redundancy between the mechanical multi-source data signals will cause the inaccuracy of the evaluation, diagnosis and prediction of industrial production operation status. The analysis of the three-way tensor composed of channel, frequency and time is called Parallel Factor Analysis (PARAFAC). The properties between the parallel factor analysis results and the input signals are studied through simulation experiments. Parallel factor analysis is used to decompose the third-order tensor composed of channel-time-frequency after continuous wavelet transformation of vibration signal into channel, time and frequency characteristics. Multi-scale parallel factor analysis successfully extracted nonlinear multi-dimensional dynamic fault characteristics by generating the spatial, spectral, time-domain signal loading value and three-dimensional fault characteristic expression. In order to verify the effectiveness of the space, frequency and time domain signal loading values of the fault characteristic factors generated by the centrifugal pump system after parallel factor analysis, the characteristic factors obtained after parallel factor analysis are used as the SPRT test sequence for identification and verification. The results indicate that the method proposed in this article improves the measurement accuracy and intelligence of mechanical fault detection.

Background:: The utilization of genetic data to investigate biological problems has recently become a vital approach. However, it is undeniable that the heterogeneity of original samples at the biological level is usually ignored when utilizing genetic data. Different cell-constitutions of a sample could differentiate the expression profile, and set considerable biases for downstream research. Matrix factorization (MF) which originated as a set of mathematical methods, has contributed massively to deconvoluting genetic profiles in silico, especially at the expression level. Objective: With the development of artificial intelligence algorithms and machine learning, the number of computational methods for solving heterogeneous problems is also rapidly abundant. However, a structural view from the angle of using MF to deconvolute genetic data is quite limited. This study was conducted to review the usages of MF methods on heterogeneous problems of genetic data on expression level. Methods: MF methods involved in deconvolution were reviewed according to their individual strengths. The demonstration is presented separately into three sections: application scenarios, method categories and summarization for tools. Specifically, application scenarios defined deconvoluting problem with applying scenarios. Method categories summarized MF algorithms contributed to different scenarios. Summarization for tools listed functions and developed web-servers over the latest decade. Additionally, challenges and opportunities of relative fields are discussed. Results and Conclusion: Based on the investigation, this study aims to present a relatively global picture to assist researchers to achieve a quicker access of deconvoluting genetic data in silico, further to help researchers in selecting suitable MF methods based on the different scenarios.

In 1993, the CPC Central Committee and the State Council made a decision to gradually promote the reform of the higher education system, put forward the eight character policy slogan of “readjustment, restructuring, consolidation, and improvement”, and successively merged 637 colleges and universities into 70 multidisciplinary and comprehensive colleges and universities. Jilin University was formed on June 12, 2000 by the merger of the former Jilin University, Jilin University of Technology, Norman Bethune Medical University, Changchun University of Science and Technology and Changchun Institute of Posts and Telecommunications. On August 29, 2004, the former Quartermaster University of the Chinese People’s Liberation Army was transferred to Jilin University. Jilin University is a typical case of university merger. It has been more than 30 years since the “wave of university merger” in the 1990s. Timely summarizing the relevant experience and lessons is conducive to providing reference experience for university decision-making in the future. Firstly, this paper analyzes the reasons for the merger of colleges and universities, analyzes the necessity of the merger of colleges and universities from three aspects: the lack of educational resources, the discipline structure not meeting the requirements of the time, and the reform of government institutions. The authors take Jilin University as an example to summarize the benefits of the merger of colleges and universities from both theoretical and empirical aspects. As a mode of university development, merger also has its problems in the choice of merger policy and the process of merger. Finally, in view of these existing problems, this paper summarizes the relevant solutions of Jilin University, and puts forward supplementary measures from the perspective of the government, universities and teachers and students, hoping to provide experience for the practice of university merger., В 1993 году ЦК КПК и Государственный совет приняли решение постепенно продвигать реформу системы высшего образования, выдвинули восьмизначную политику «совместного строительства, корректировки, сотрудничества и слияния» и последовательно объединили 637 колледжей и университеты в 70 многопрофильных и общеобразовательных колледжей и университетов. Цзилиньский университет был образован 12 июня 2000 года в результате слияния бывшего Цзилиньского университета, Цзилиньского технологического университета, Медицинского университета Нормана Бетьюна, Чанчуньского университета науки и технологий и Чанчуньского института почты и телекоммуникаций. 29 августа 2004 г. бывший квартирмейстерский университет Народно-освободительной армии Китая был переведен в Цзилиньский университет. Университет Цзилинь – типичный случай объединения университетов. Прошло более 30 лет с момента «волны слияния университетов» в 1990-х годах. Своевременное обобщение соответствующего опыта и уроков способствует предоставлению справочного опыта для принятия университетских решений в будущем. Во-первых, в данной статье анализируются причины слияния колледжей и университетов, анализируется необходимость слияния колледжей и университетов с трех сторон: нехватка образовательных ресурсов, структура дисциплин, не отвечающая требованиям времени, и реформа системы образования государственных учреждений. На примере Университета Цзилинь суммируются преимущества слияния колледжей и университетов как с теоретических, так и с эмпирических аспектов. Как способ развития университета, слияние также имеет свои проблемы в выборе Политики слияния и процесса слияния. Наконец, с учетом этих существующих проблем, в этом документе суммируются соответствующие решения Университета Цзилинь и предлагаются дополнительные меры с точки зрения правительства, университетов, преподавателей и студентов, надеясь предоставить опыт для практики слияния университетов.

In this paper, we propose a novel girth measurement system based on multi-view stereo images for garment design. Our system is set in a fixed location to capture three pairs of stereo images for the subject by six calibrated and synchronously triggered cameras. An important feature of this system is the use of an optimized semantic segmentation network that can efficiently segment the girth region in the captured six-view stereo images. Another important feature of this system is the use of color subspace classification and coordinate clustering that can effectively constrain the stereo matching within the scope of markers. Then, the system performs only on the corresponding clusters to extract stereo matching point pairs of markers correctly. The space coordinates of 3D point corresponding to each stereo matching point pair are calculated in each coordinate system of stereo cameras. The unified coordinates of these 3D markers are transformed from three different coordinate systems into one unified coordinate system. Girth is measured by curve fitting of these markers and calculating the length of the fitting curve. Our proposed system performs passive and intelligent girth measurement in garment design, and overcomes the problem of too many invalid stereo matching point pairs in girth measurement. Experimental results demonstrate its accuracy. Our system has a maximum bust measurement error of 1.28cm for woman and 1.31cm for man and a maximum waist measurement error of 1.18cm for woman and 0.99cm for man, which are within the error limit regulated by China national standards GB/T 2664-2017, 2665-2017, 2666-2017 and textile industry standard FZ/T 81004-2012.

Centered on parallel factor analysis(PARAFAC) and support vector machine(SVM), a method of centrifugal pump(CP) fault diagnosis is proposed. A non-stationary multi-fault mode single-source signal feature extraction method is proposed, based on the theory of parallel factor analysis, i.e. spatial information is introduced in time-frequency analysis, and after parallel factor analysis, the spatial signal, the spectral signal (mode2) and the time-domain signal (mode3) are generated. The latter two can obviously describe the natural or fault state of the mechanical equipment by comparing the experimental analysis, and this function is used to evaluate the mapping relationship between the centrifugal pumps in different states and their respective mode2 and mode3. A total of six components in mode2 and mode3 are used as feature vectors, which are input to the SVM classifier, optimized by the improved particle swarm algorithm for fault classification, by constructing an experimental platform to collect the nonlinear multi-fault mode feature signals of centrifugal pumps. Compared with the wavelet packet energy features, the proposed method is more convenient for feature extraction, the classification accuracy of the proposed classifier is significantly improved, and the complexity of the proposed classifier is not significantly increased.

A robust vehicle speed measurement system based on feature information fusion for vehicle multi-characteristic detection is proposed in this paper. A vehicle multi-characteristic dataset is constructed. With this dataset, seven CNN-based modern object detection algorithms are trained for vehicle multi-characteristic detection. The FPN-based YOLOv4 is selected as the best vehicle multi-characteristic detection algorithm, which applies feature information fusion of different scales with both rich high-level semantic information and detailed low-level location information. The YOLOv4 algorithm is improved by combing with the attention mechanism, in which the residual module in YOLOv4 is replaced by the ECA channel attention module with cross channel interaction. An improved ECA-YOLOv4 object detection algorithm based on both feature information fusion and cross channel interaction is proposed, which improves the performance of YOLOv4 for vehicle multi-characteristic detection and reduces the model parameter size and FLOPs as well. A multi-characteristic fused speed measurement system based on license plate, logo, and light is designed accordingly. The system performance is verified by experiments. The experimental results show that the speed measurement error rate of the proposed system meets the requirement of the China national standard GB/T 21555-2007 in which the speed measurement error rate should be less than 6%. The proposed system can efficiently enhance the vehicle speed measurement accuracy and effectively improve the vehicle speed measurement robustness.