Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the improved network structures and loss functions of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of different networks are analyzed by multiple comparative experiments. Finally, we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.     

A Novel Method of Heart Failure Prediction Based on DPCNNXGBOOST Model

The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients. Existing methods depend on the judgment of doctors, the results are affected by many factors such as doctors’ knowledge and experience. The accuracy is difficult to guarantee and has a serious lag. In this paper, a mixture prediction model is proposed for perioperative adverse events of heart failure, which combined with the advantages of the Deep Pyramid Convolutional Neural Networks (DPCNN) and Extreme Gradient Boosting (XGBOOST). The DPCNN was used to automatically extract features from patient’s diagnostic texts, and the text features were integrated with the preoperative examination and intraoperative monitoring values of patients, then the XGBOOST algorithm was used to construct the prediction model of heart failure. An experimental comparison was conducted on the model based on the data of patients with heart failure in southwest hospital from 2014 to 2018. The results showed that the DPCNN-XGBOOST model improved the predictive sensitivity of the model by 3% and 31% compared with the text-based DPCNN Model and the numeric-based XGBOOST Model.     

Flower-shaped ZnO nanomaterials for low-temperature operations in NOX gas sensors

In this study, we synthesized nanostructured zinc oxide (ZnO) by using various concentrations (0–0.05 M) of cetyltrimethylammonium bromide (CTAB) as a surfactant to optimize its morphology for gas sensor applications. The optimization process was used to elucidate the morphology effects (rod-shaped and flower-shaped morphologies). The morphologies were investigated through scanning electron microscopy, in which the assembly of nanorods leading to a spherical microstructure with a CTAB concentration of 0.005 M was observed. Brunauer–Emmett–Teller isotherm measurements revealed a surface area of 7.928 g/m² for the flower-like morphology, which was relatively higher than those of other CTAB-assisted morphologies. Such morphological features were expected to contribute toward high-performance gas-sensing. The effect of morphology variation on the resistance of ZnO microstructures was used for gas measurements. Among the varied morphologies, a sample with a spherical flower-shaped morphology exhibited a very high response at low temperatures (~29 at 25 °C) toward NO_X gas (0.75 ppm) and a high selectivity toward NO_x among ammonia (NH₃), toluene (C₆H₅CH₃), carbon monoxide (CO), acetone (CH₃COCH₃), and ethanol (C₂H₅OH). Raman and photoluminescence spectroscopy analyses unraveled the presence of a high density of oxygen vacancies in the sample, thereby suggesting a close link between the defective nature of the sample and the high response of the flower-like ZnO at low temperatures.     

基于深度学习的传感器优化布置方法

针对传感器优化布置(optimal sensor placement,简称OSP)问题,提出了一种新的使用深度神经网络的解决方案,并以简化的桥梁形状的桁架结构中的振动测试传感器优化为例进行了验证。首先,选择一种传统的传感器优化布置方法,对自动化生成的大量不同的桁架结构分别进行传感器优化布置计算,将所得优化布置结果在进行数据预处理后构建出深度学习方法所需要的训练集与验证集;其次,使用Python语言和深度学习框架TensorFlow设计实现与本研究问题适配的深度神经网络模型并训练;然后,随机生成了新的桁架结构参数;最后,将深度神经网络输出的传感器布置结果和传统方法的计算结果进行了比较,验证了本研究方法的有效性以及在速度上、可移植性与可扩展性方面的性能优势。     

Pressure sensor matrix for indirect measurement of grip and push forces exerted on a handle

To date, a large part of workers is exposed to vibrations (23% in Europe) which can negatively impact on their health. This work discusses the importance of measuring grip and push forces in the context of hand-arm vibration tests, bearing in mind the state-of-art of current standards. It proposes a method for indirect measurement of coupling forces using a matrix of polymeric pressure capacitive sensors and discusses the model used for defining these quantities. The matrix of pressure sensors is wrapped around the tool handle and the acting forces, exchanged with the handle, are derived from the pressure values measured by the matrix. Calibration is presented and the effect of curvature is discussed. The work continues with the experimental validation of the model proposed for push force measurements carried out through lifting tests using known masses with a cylindrical handle. An experimental correction coefficient is defined in correlation to the type of grip. The method for measuring the push force, thus corrected, is assessed by means of push force tests on an instrumented handle. Finally the experimental data are analysed in order to assess the uncertainty of the proposed method for measuring the push force, highlighting the contribution of the different sources of uncertainty. The proposed measurement method allows to measure the push and the grip force (known influencing quantities for the measurement of the hand-arm vibration) during tool test and without modifying the handles.     

Parallel color-coding

We present new parallelization and memory-reducing strategies for the graph-theoretic color-coding approximation technique, with applications to biological network analysis. Color-coding is a technique that gives fixed parameter tractable algorithms for several well-known NP-hard optimization problems. In this work, by efficiently parallelizing steps in color-coding, we create two new biological protein interaction network analysis tools: Fascia for subgraph counting and motif finding and FastPath for signaling pathway detection. We demonstrate considerable speedup over prior work, and the optimizations introduced in this paper can also be used for other problems where color-coding is applicable.     

Local weather prediction system for a heating plant using cognitive approaches

Present-day requirements emphasize the need of saving energy. It relates mainly to industrial companies, where the minimization of energy consumption is one of their most important tasks they face. In our paper, we deal with the design of the so-called weather prediction system (WPS) for the needs of a heating plant. The primary task of such a WPS is timely predicting expected heat consumption to prepare the technology characterized by long delays in advance. Heat prediction depends primarily on weather so the crucial part of WPS is the weather, especially temperature, prediction. However, a prediction system needs a variety of further data, too. Therefore, WPS must be regarded as a complex system, including data collection, its processing, own prediction and eventual decision support. This paper gives the overview about existing data processing systems and prediction methods and then it describes a concrete design of a WPS with distributed data measuring points (stations), which are processed using a structure of neural networks based on multilayer perceptrons (MLP) with a combination of fuzzy logic. Based on real experiments we show that also such simple means as MLPs are able to solve complex problems. The paper contains a basic methodology for designing similar WPS, too.