The efficient classification of remote sensing images (RSIs) has become the key of remote sensing application. To tackle the high computational cost in the traditional classification method, in this paper we propose a new RSI classification method based on improved convolutional neural network (CNN) and support vector machine (SVM) (CNN-SVM). In this method, we first designed a seven-layer CNN structure and took the ReLU function as the activation function. We then inputted the RSI into the CNN model and extracted feature maps and replaced the output layer of the CNN network via training the feature maps in the SVM classifier. Next, taking the simulation experiments of MNIST handwritten digital dataset and UC Merced Land Use remote sensing dataset as examples, we tested and verified the proposed method in this experiment. Finally, the empirical study of volcanic ash cloud (VAC) classification from moderate resolution imaging spectroradiometer (MODIS) RSI was carried out and evaluated. The experimental results show that compared with the traditional methods, the proposed method has lower loss value and better generalization in modeling training; the total classification accuracy of VAC and Kappa coefficient reached 93.5 and 0.8502, respectively, and achieved preferable VAC identification and visual effects. It will enhance the classification accuracy to the massive remote sensing data.

Tar formation is the main bottleneck for biomass gasification technology. A novel rotary kiln type biomass gasification process was proposed. The concept design was based on air staging and process separation. This concept was demonstrated on a pilot scale rotary kiln reactor under ambient pressure and autothermic conditions. The pilot scale gasifier was divided into three different reaction regions, which were oxidative degradation, partial oxidation and char gasification. A series of tests was conducted to investigate the effect of key parameters. The results indicate that under optimum operating conditions, a fuel gas with high heat value of about 5500 kJ/Nm³ and gas production rate of 2.32 Nm³/kg could be produced. Tar concentration in the fuel gas could be reduced to 108 mg/Nm³ (at the gasifier outlet) and 38 mg/Nm³ (after gas conditioning). The cold gas efficiency and carbon conversion rate reached 75% and 78%, respectively. The performance of this gasification system shows considerable potential for implementation in distributed electricity and heat supply projects.

Multiple cell death is involved in ischemic brain injury. Necroptosis, a recently reported cell death, may be the most suitable cell death mechanism in a subpopulation of neurons under ischemic injury. It reported that a small molecule, necrostatin-1 (Nec-1), has a potent inhibitory effect on necroptotic cell death in vivo and in vitro. The aim of the current study was to investigate the role of Nec-1 on cognitive function in chronic ischemic stroke mice induced by bilateral common carotid artery stenosis (BCAS). Here, 12-week-old C57BL/6 mice received intragastric administration with Nec-1 or vehicle for two weeks after stroke, and then, the effect and possible mechanism were determined. We demonstrated that inhibition of necroptosis prevented cognitive impairment and reduced inflammatory response in the ischemic brain injury mouse model. These data suggested that inhibition of necroptosis provided a potential therapeutic option for cognitive rehabilitation in chronic ischemic stroke.

Safe operation control systems are the most important aspect of medical device development apart from the machine’s function itself. Due to the fact that machines are used on people, how people can protect themselves from intelligent/programmed automatic or semiautomatic operating systems is an unavoidable problem. The Massage Robot application is based on its medical potential, lightweight nature and underlying Internet of Things (IoT) device properties. Massage robots are currently going into development in clinical trials. Handheld system interruption equipment and the robot’s physical characteristics can help people achieve harmless use. In this paper, we systematically test the safety of a massage robot in an entirely real-world setting. This paper focusses on safe control operations in order to keep exploring the most reasonable safe operating modes and interrupt modes for medical devices, as well as the security interrupt priority.

The localization of sensing nodes is most pronounced in the application of wireless sensor networks. To address this issue, a node localization algorithm called the DMA is proposed in this paper. This algorithm identifies the node position by using the estimation matrix and distance matrix together with the optimized linear transforming function. With the integration of GA, the position of the node can be accurately determined. The conducted simulation outcomes and the corresponding analysis verify the high accuracy and low energy consumption of the proposed algorithm, which can outperform other widely used approaches. This study designs and deploys the proposed algorithm and shows its sensor node localization theory, which makes it a promising basis for the realization of positioning in WSNs.

Biometric identification has verified its effectiveness in personal identity verification because of the uniqueness and noninvasion. In this research, we tend to apply the detection of biometric information to a remote sensing system for the purpose of security area monitoring. Our system is established by collecting signals from the coming individuals via the remote measurement in the specific condition where both kinds of data are detected to determine the identity. Specifically, the measuring of gait signals and facial images is integrated to provide a way of improving the detection accuracy and the robustness. In addition, the fuzzy association rule (FAR) is employed for data analysis in line with the outcomes of different methods. As such, the signals are integrated and transmitted for further processing and remote identification. Experiments are conducted to demonstrate the capability of the proposed system. With the training data increases, a high detection accuracy of 95.2 is obtained, which makes it a promising basis for the realization of remote identity verification.