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In 6G era, service forms in which computing power acts as the core will be ubiquitous in the network. At the same time, the collaboration among edge computing, cloud computing and network is needed to support edge computing service with strong demand for computing power, so as to realize the optimization of resource utilization. Based on this, the article discusses the research background, key techniques and main application scenarios of computing power network. Through the demonstration, it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing, storage and network, and adapt to the integration needs of computing power and network invarious scenarios, such as user oriented, government enterprise oriented, computing power open and so on.

Deep neural networks (DNNs) can be utilized maliciously for compromising the privacy stored in electronic devices, e.g., identifying the images stored in a mobile phone connected to the Internet of Things (IoT). However, recent studies demonstrated that DNNs are vulnerable to adversarial examples, which are artificially designed perturbations in the original samples for misleading DNNs. Adversarial examples can be used to protect the DNN-based privacy leakage in mobile phones by replacing the photos with adversarial examples. To avoid affecting the normal use of photos, the adversarial examples need to be highly similar to original images. To handle a large number of photos stored in the devices at a proper time, the time efficiency of a method needs to be high enough. Previous methods cannot do well on both sides. In this article, we propose a broad class of selective gradient sign iterative algorithms to make adversarial examples useful in protecting the privacy of photos in IoT devices. By neglecting the unimportant image pixels in the iterative process of attacks according to the sort of first-order partial derivative, we control the optimization direction meticulously to reduce image distortions of adversarial examples without leveraging high time-consuming tricks. Extensive experimental results show that the proposed methods successfully fool the neural network classifiers for the image classification task with a small change in the visual effects and consume little calculating time simultaneously.

Medical image computing techniques are essential in helping the doctors to support their decision in the diagnosis of the patients. Due to the complexity of the brain structure, we choose to use MR brain images because of their quality and the highest resolution. The objective of this article is to detect brain tumor using convolution neural network with fuzzy c-means model, the advantage of the proposed model is the ability to achieve excellent performance using accuracy, sensitivity, specificity, overall dice and recall values better than the previous models that are already published. In addition, the novel model can identify the brain tumor, using different types of MR images. The proposed model obtained accuracy with 98%.

Many of the leading hospitality and tourism degree programs around the globe are nested within business schools accredited by the Association to Advance Collegiate Schools of Business (AACSB). Thes...

The process of diagnosing brain tumors is very complicated for many reasons, including the brain’s synaptic structure, size, and shape. Machine learning techniques are employed to help doctors to detect brain tumor and support their decisions. In recent years, deep learning techniques have made a great achievement in medical image analysis. This paper proposed a deep wavelet autoencoder model named “DWAE model”, employed to divide input data slice as a tumor (abnormal) or no tumor (normal). This article used a high pass filter to show the heterogeneity of the MRI images and their integration with the input images. A high median filter was utilized to merge slices. We improved the output slices’ quality through highlight edges and smoothened input MR brain images. Then, we applied the seed growing method based on 4-connected since the thresholding cluster equal pixels with input MR data. The segmented MR image slices provide two two-layer using the proposed deep wavelet auto-encoder model. We then used 200 hidden units in the first layer and 400 hidden units in the second layer. The softmax layer testing and training are performed for the identification of the MR image normal and abnormal. The contribution of the deep wavelet auto-encoder model is in the analysis of pixel pattern of MR brain image and the ability to detect and classify the tumor with high accuracy, short time, and low loss validation. To train and test the overall performance of the proposed model, we utilized 2500 MR brain images from BRATS2012, BRATS2013, BRATS2014, BRATS2015, 2015 challenge, and ISLES, which consists of normal and abnormal images. The experiments results show that the proposed model achieved an accuracy of 99.3%, loss validation of 0.1, low FPR and FNR values. This result demonstrates that the proposed DWAE model can facilitate the automatic detection of brain tumors.

BackgroundObjective response rate (ORR) under mRECIST criteria after transarterial chemoembolization (TACE) has been identified as a surrogate endpoint of overall survival (OS). However, its optimal time-point remains controversial and may be influenced by tumor burden. We aim to investigate the surrogacy of initial/best ORR in relation to tumor burden. MethodsA total of 1549 eligible treatment-naïve patients with unresectable hepatocellular carcinoma (HCC), Child-Pugh score ≤7, and performance status score ≤1 undergoing TACE between January 2010 and May 2016 from 17 academic hospitals were retrospectively analyzed. ResultsBoth initial and best ORRs interacted with tumor burden defined as our previously proposed “six-and-twelve” criteria. Both initial and best ORRs could equivalently predict and correlated with OS in low (adjusted HR: 2.55 and 2.95, respectively, both P

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

To solve the failure of hub bearings, a Semi-simplified finite element method (FEM) is proposed to study the sealing performance based on the rigid-flexible combined seal groove. The impact of compression ratio, seal groove geometry, O-ring material hardness and O-ring inner-diameter on Von Mises stress and contact pressure is studied with the FEM. The results indicate that the designed sealing mechanism with the optimized parameters can meet the sealing requirement. The Semi-simplified FEM is effective to study the rigid-flexible combined seal groove. Compression ratio and O-ring material hardness have a vital influence on the sealing performance. The relationship between the maximum operation pressure and the compression ratio is validated with the dynamic oil injection test.

A detailed nondestructive analytical method for quantitative food analysis was established by using a self-fabricated NIR-LED light source combined with Mg3Ga2GeO8 (MGGO) phosphor and a blue LED ch...

Since the inception of the “going global” strategy two decades ago, China has attached increasing importance to agricultural diplomacy. International agricultural cooperation has become both an end and a means of China’s diplomacy, as well as an integral part of China’s “grand diplomacy.” China’s agricultural diplomacy has many features, including: the central government playing a leading role; ministerial agencies undertaking specific programs; local governments being responsible for implementation; and enterprises acting as active participants. All these contribute to China’s overall diplomacy and help maintain food security of the target countries and promote the overseas interests of Chinese enterprises. Through various practices, China’s agricultural diplomacy has been working well with the mutual reinforcement of bilateral and multilateral approaches; and interactions among diversified diplomatic entities are encouraged. China’s agricultural diplomacy has enriched the meaning of “major-country diplomacy with Chinese characteristics” and constitutes an important means for China to participate in global governance and to align with the development plans of target countries. Since China’s agricultural diplomacy is still faced with multiple challenges, it is imperative for China to improve related mechanisms, explore new ways of agricultural cooperation in third countries, and formulate new patterns for agricultural diplomacy along the “Belt and Road.”

Rapid urbanization in China has brought about large-scale factory relocation. Severe environmental ecological and human health risks are caused by a large number of contaminated legacies left in the city. To comprehensively review the pollution and assess the health risk of industrial legacies in China, a total of 625 polluted industrial legacies were compiled by document retrieval. Legacies are mainly located in the southwest of China, the North China Plain, Yangtze River Basin, Yangtze River Delta, and Pearl River Delta with a mean operation time of 35 years, and legacies of chemical manufacturing take the biggest proportion of all sites. Health risk assessments considering the uncertainty of exposure and toxic factors reveal that the soil heavy metal pollution in China is serious, with Pb, Cd, Zn, Ni, and As as dominant pollutants. Legacies of chemical manufacturing, ferrous metal processing, non-ferrous metal processing, and mines should be priority controlled for their large number and serious risks. Children are the most vulnerable people with more serious non-carcinogenic and carcinogenic risks, while males are slightly surpassed by females. Insights for better risk management of legacies are provided based on the comprehensive assessment of pollution and human health risk in this study.

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging, first, the difficulty of brain structure and the intertwining of tissues in it, and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

In the high-speed water-entry shock experiment, we need to store and recover the acceleration data generated during the water-entry shock process. In response to this need, we designed a two-dimensional acceleration measurement combined device system. The system uses the IEPE sensor as the sensitive element, and designs an efficient constant current source circuit and a low-power signal conditioning circuit. Use high-precision A/D (ADS1256) converter to achieve analog-to-digital conversion, The data communication between the main control unit and ADC is realized through the serial peripheral interface SPI, and high-speed data transmission is completed. The main control unit stores the collected digital signals in the corresponding memory card through a new polling storage technology. The experimental results show that the system adopts the disconnection trigger method to effectively avoid the false trigger phenomenon during testing. The polling storage technology of real-time cache solves the problem of partial data loss caused by power outage in a specific water impact environment. By comparing the waveform energy difference with the data obtained by dynamic simulation and verifying by mathematical analysis, it can be known that there is no frame error or frame loss in the transmission data. The data recovery device can meet the conditions for high-speed acceleration data recovery, and can provide reliable basic data support for water impact related research.

Transcranial magnetic-acoustical-electrical stimulation is a new noninvasive brain modulation method with high spatial resolution and high penetration depth. However, the effect of electric field focalization induced by transcranial magneto-acoustical-electrical stimulation parameters is still unclear. In this paper, the electric field generated by transcranial magnetic-acoustical-electrical stimulation is simulated. The influence of the phased array transducer parameters on the induced electric field distribution is analyzed. The induced electric field distribution caused by different phased array parameters was calculated. The distribution characteristics of the induced electric field are analyzed from the radial and axial directions of the preset focusing point. The simulation results show that induced electric field intensity in the focusing region increases with the length and width of array elements and decreases with the array gap widening. There is a significant negative correlation between the size of focal region and the gap of the array elements. The correlation between phased array parameters and focusing performance is analyzed in this paper, which provides reference and theoretical guidance for research on improving the focusing property of induced electric field.

With the increase of mining height, the problem of coal wall spalling in the working face gradually worsens. Hydraulic support and its face guard structure are the key pieces of equipment to restrain the coal wall spalling. However, at present, the hydraulic jack is mostly considered as rigid in the analysis of protection mechanism. This simplification cannot effectively reflect the true bearing state of the face guard. In order to improve the accuracy of analysis, this study considers the face guard jack as a flexible spring and establishes a rigid-flexible coupling analysis model of the face guard mechanism. First, based upon the multibody dynamics software ADAMS®, the multibody numerical model of the face guard of the hydraulic support was established. The influence of the two kinds of structures on the coal wall disturbance was analyzed and compared. Then, the rigid model was meshed. The hydraulic jacks were equivalent to the spring system, and the rigid-flexible coupling model was established. Based upon the application load on different positions of the rigid-flexible model, the load-bearing characteristics and hinge point force transfer characteristics of the two face guards were analyzed. The results show that the support efficiency of the integral type was higher than that of the split type. In the vertical support attitude, the dynamic disturbance of the coal wall, produced by the two kinds of face guards, was small. The four-bar linkage effectively improved the ultimate bearing capacity of the integral face guard. The results provide theoretical support for the design and optimization of the face guard.

Objective: Medical image processing is an exciting research area. In this paper, we proposed new brain tumor detection and classification model using MR brain images to help the doctors in early detection and classification of the brain tumor with high performance and best accuracy. Materials: The model was trained and validated using five databases, including BRATS2012, BRATS2013, BRATS2014, BRATS2015, and ISLES-SISS 2015. Methods: The advantage of the hybrid model proposed is its novelty that is used for the first time; our new method is based on a hybrid deep convolution neural network and deep watershed auto-encoder (CNN-DWA) model. The method consists of six phases, first phase is input MR images, second phase is preprocessing using filter and morphology operation, third phase is matrix that represents MR brain images, fourth is applying the hybrid CNN-DWA, fifth is brain tumor classification, and detection, while sixth phase is the performance of the model using five values. Results: The novelty of our hybrid CNN-DWA model showed the best results and high performance with accuracy around 98% and loss validation 0, 1. Hybrid model can classify and detect the tumor clearly using MR images; comparing with other models like CNN, DNN, and DWA, we discover that the proposed model performs better than the above-mentioned models. Conclusion: Depending on the better performance of the proposed hybrid model, this helps in developing computer-aided system for early detection of brain tumors and helps the doctors to diagnose the patients better.

The increasing popularity of online shopping not only facilitates people's life, but also stimulates people's desire for consumption, causing college students to buy a lot of idle items, resulting in a waste of resources. By studying the relatively common trading methods of second-hand items that currently exist, it is found that they all have certain limitations and are not convenient and practical enough for the special needs group of college students. Therefore, designing a convenient second-hand trading platform for college students has market potential and practical significance. This article has developed and designed a set of offline intelligent trading cabinets based on Raspberry Pi. The system is mainly composed of the master Raspberry Pi, 4G wireless communication module, WeChat applet platform and intelligent trading cabinets. The entire system processes data in the cloud, which is relatively stable in operation. The operation page is simple and beautiful, and it is convenient and time-saving to use. The system can help college students use the old things around them and make a practical contribution to green environmental protection. At the same time, direct income can also help students earn the first pot of gold in their lives.

Body-centric wireless communication has become an important part of the fifth-generation communication systems. Many modern applications such as, telemedicine, health monitoring and personal entertainment are adding to its popularity. In this work, a wearable finger ring antenna array radiating at 28GHz has been proposed for the 5G/mmWave communication system. The antenna is composed of a microstrip patch array, which is mounted to a Teflon ring base, to make it wearable. CST Microwave Studio has been used to model, simulate and optimize the antenna. The simulated results illustrate that the proposed design provides a good return loss at the desired frequency with a peak gain of 5.14dB. Good performance in terms of bandwidth and radiation characteristics, along with a miniaturized form factor makes it a suitable candidate as a wearable antenna to communicate at the mmWave frequency spectrum.

Early kick detection is important to reduce the possibility of well blowouts, which have serious safety and financial implications in deepwater drilling. In this paper, a novel pattern recognition model for kick diagnosis is proposed. In the model, trend detection is used to make good decisions based upon noisy drilling data. The integrated model comprises two parts: (i) a dynamic wellbore flow model, which extracts the kick mode via multiphase flow simulation; and (ii) improved piecewise approximation and similarity measure algorithms. The proposed model successfully diagnoses gas kick faults in real time when it is applied in a field well. It offers significant sensitivity improvements while reducing the false alarm rate caused by ambiguous data, as both kick and non-kick events are accurately extracted and rapidly identified. This model is a new attempt to combat the problem of early kick detection via pattern recognition.

A micro-scale radio frequency (RF) energy harvesting and power management solution is proposed in this work to reduce the startup power of energy collection. Analysis of existing energy harvesting solutions determines that controllable connection between the energy storage and boosting modules can maximize energy harvesting. On this basis, a low-power voltage monitoring circuit with a latch function and a feedback control loop circuit with a startup maintenance function are designed. Taking RF energy harvesting as an example, a prototype is designed and fabricated in combination with low-power Wi-Fi scattering communication technology. Measured results show that the system startup voltage is less than 0.7 V, the startup current is less than 200 nA, and the startup power is less than 140 nW, which is more than 20 times lower than the existing energy harvesting solutions. Therefore, the solution can effectively improve energy collection sensitivity, significantly expand the scope of energy collection, and provide an improved and low-cost environmental energy collection solution for hundreds of millions of passive Internet of things devices.

Long-Range (LoRa) scatter communication has wide coverage, long communication distance, low power consumption, low cost, and a wide application prospect in the field of Internet of things. The key to realizing LoRa scattering communication is to generate a square or sine wave with continuously changing frequency with time as the baseband signal. The existing technical solution is to control the voltage-controlled oscillator (VCO) using an analog-to-digital converter to generate a sine wave with continuously varying frequency. On the one hand, the aforementioned solution is sensitive to environmental changes, such as temperature. On the other hand, the power consumption is relatively large, and meeting the power requirements of passive communication is difficult. Based on the idea of direct digital frequency synthesis, this study proposes a method for generating square wave signals with continuous frequency variation with time using a microcontroller with high frequency stability and low system power consumption. Test results show that the device can realize LoRa scattering communication at any position between the base station and the receiving terminal, which are 210 meters apart, and is compatible with the existing commercial LoRa radio chipset. The standby current at the scattering end is 0.39 μA, and the continuous current during transmission is 140 μA, which reduces the power consumption of the existing active LoRa by three orders of magnitude.

As a clean and effective renewable energy source, PV has been widely used in power systems. The application of VSG technology can effectively improve the system inertia reduction problem caused by the grid connection of PV and energy storage units. The virtual inertia and damping coefficient in VSG control have the unique advantages of being flexible and controllable. This paper designs a control strategy in which the virtual inertia and damping coefficient can be flexibly adjusted according to the system frequency, which further improves the operating performance of the PV and energy storage units based on VSG control. The frequency quality of the system is maintained. Finally, the effectiveness of the proposed flexible parameter adjustment strategy was verified through the simulation platform, which played a role in popularizing the application of the proposed strategy in engineering.

The purpose of this paper is to research the oil film thickness between apex seal and epitrochoidal housing in a small Wankel rotary engine. Firstly, a quasi-static equilibrium equation of apex seal is derived with considering interacting forces. It is supposed that oil pressure is the only force supporting the apex seal against other loads. Then, the oil film thickness is evaluated by solving the equilibrium equation and Reynolds equation within a cycle. In addition, the operation speed's influence on the oil film thicknesses is studied. Results indicate that the oil film thickness is a comprehensive parameter affected by many factors, and these factors exert varying degree of influence on the oil film thickness at different positions within a cycle.

A sophisticated olfactory system is part of the explanation for the prominence of insects among animals because of the essential roles of the olfactory system in foraging, host seeking, mating, ovipositing and avoiding toxic substances. In this study, we sequenced and analysed the transcriptomes of olfactory tissue (antennae) and non-olfactory tissue (legs) of the scarab beetle, Holotrichia oblita Faldermann, which is a serious underground pest in China. We obtained approximately 80.2 million 150bp reads that were assembled into 61,038 unigenes with an average length of 890bp. Among the transcripts, 70% of the unigenes were annotated. A total of 44 odorant receptors (ORs) and 9 ionotropic receptors (IRs) were identified based on homology searches. Then, quantitative real-time PCR experiments were performed to investigate the expression patterns of 32 putative chemosensory genes. The results showed that these genes were highly expressed in olfactory organs (antennae) and might play a key role in the olfaction-related behaviours in H. oblita. Based on the results of our phylogenetic analysis and the detailed tissue and sex-biased expression characteristics, the different roles of the receptor proteins in the olfactory system were also indicated. The results of this study will provide the foundation for further understanding of the olfactory odorant receptors of H. oblita at the molecular level and ultimately help to develop novel targets for manipulating this pest.

Carbon Capture and Storage (CCS) is essential to achieve global cumulative CO2 reductions to meet the two degree scenario in 2050. To accelerate the development of CCS technology in China, the biggest full chain demonstration project is under construction in Guoneng Jinjie Power plant. This facility is designed to capture 150,000 tons of CO2 per year from coal fired PC boiler flue gas for EOR. The advanced amine absorbents combined with the integrated processes such as inter-cooling, rich solution split process and Mechanical Vapor Recompression flashing (MVR flashing) were used to reduce the energy penalty and operating costs. The system can attain following parameters including the CO2 capture rate>90%, captured CO2 purity>99% and regeneration energy penalty

This article takes the reducer with the involute helical gear as the research object, the diagnostic method of the broken tooth fault and the deterioration trend is researched by vibration spectrum graph, demodulation waveform graph, time domain waveform graph, demodulation spectrum graph. The research results show that vibration spectrum graph has the worst guidance for the broken tooth fault of the involute helical gear reducer, demodulation waveform graph has the poor guidance in the initial stage of the broken tooth fault, with the deepening of the severity of the fault, the fault characteristics of the waveform gradually become obvious, time domain waveform graph and demodulation spectrum graph are the effective means to discover the initial fault and monitor the fault changes.

Limestone is normally added into fluidized bed boiler furnace as sulfur sorbent. Water vapor, alkali and alkaline earth metals (AAEMs) within coal have great influence on desulfurization mechanism. In this paper, the impacts of water vapor and AAEMs on limestone desulfurization during coal combustion are studied. In the experiments, Jincheng anthracite (JCA) and Zhundong lignite (ZDL) were used as fuel with limestone addition under different concentrations of water vapor in a bench-scale fluidized-bed combustor (FBC). SEM, ICP-AES and XRD techniques were employed to analyze the micro-morphology and chemical compositions of deposited ash to understand the mechanism. The results show that water vapor and AAEMs within coal accelerate the limestone desulfurization during coal combustion. ZDL has a stronger propensity of self-desulfurization than JCA. The grain size of coal ash grows with water vapor addition, and this phenomenon is further intensified under the condition of desulfurization or ZDL combustion. The mechanism is probably that water vapor and AAEMs improve the solid state diffusion during desulfurization and improve growth of ash grains.

The optimization design of engine is always one of the top concerns in motorcycle industry. In this paper, effective torque and fuel consumption ratio are defined as the performance evaluation indexes of engine, while air-fuel ratio, intake valve timing angle, exhaust valve timing angel, pressure, and temperature are defined as input variables. With the application of DOE methodology, a full factorial DOE is conducted to estimate the regression model and identify the statistical significant factors. And then, with the selection of additional experimental points, RSM is introduced to construct the precise regression model between input variables and performance indexes. Based on that, an optimum solution that can satisfy both performance requirements are brought forward and testified.

A novel ternary-alkaline red emitting fluoride phosphor K2NaGaF6:Mn4+ was successfully synthesized through co-precipitation method. The crystal structure, morphology, electronic band structure and luminescence properties of K2NaGaF6:Mn4+ phosphors were investigated in details by using Rietveld refinement of X-ray diffraction data, scanning electron microscopy (SEM), density functional theory (DFT) calculation and different reaction parameters. The K2NaGaF6 host has a cubic unit cell with the space group Fmm and lattice parameters of a = 8.2577 (4) A, Z = 4, Vcell = 563.08 (8) A3. Under blue light excitation, Mn4+ activated K2NaGaF6 exhibits a bright narrow-band red emission. The PL properties of the K2NaGaF6:Mn4+ red phosphors were optimized with different Mn4+ concentrations and aging times. A warm-white LED device was fabricated using a blue LED chip combined with commercial yellow YAG:Ce3+ phosphor and synthesized K2NaGaF6:Mn4+ red phosphor. The color rendering index (CRI, Ra = 81.6) and corresponding color temperature (CCT = 3643 K) easily reached the commercial warm white light LED standards (Ra > 80 and CCT < 4000 K). All these results indicate that K2NaGaF6:Mn4+ phosphor would be a suitable red phosphor candidate for warm-white LED applications.

Wave power in China has an energy density that is one-tenth of that of the wave power in Europe. Therefore, Chinese developers cannot simply replicate and transplant European devices and facilities to East Asia. Instead, to exploit the conditions in East Asia, a combined-oscillating-buoy-based wave energy convertor (WEC) is proposed as an array-type WEC. Sea trials of a 10 kW pilot device confirmed the feasibility of its buoy array and hydraulic pressure system (HPS) for wave-energy conversion. Field test data indicated that the energy-converting efficiency of the direct-drive design was relatively low, suggesting the urgency of developing energy storage systems to the increase of power take-off productivity. A hydraulic accumulator system (HAS) was designed to replace the original direct-drive operating mechanism for a 100 kW final-prototype device. It was expected that the system will exhibit an improved ability to collect and store energy from smaller waves to enhance the total converting efficiency. Experimental studies were performed to investigate the hydrodynamic characteristics of the heaving buoys under the damping effects, and the operating performance of the accumulator system. A numerical wave-to-wire model based on Simulink, Matlab was set-up to study the energy output of the HPS-HAS under the wave conditions of the test site. It was shown that four buoys could charge ten accumulators in half an hour under local actual sea conditions. Furthermore, in a same running time, the output energy of the HPS-HAS is over forty times larger than that of the HPS without any energy storage device. In addition, energy discharging from three accumulators only could reach about 20 kW output power. The numbers of buoys and accumulators should be optimized to improve wave-energy storage and power generation in the future.

Single-switch contactless resonant converter is an optional choice for wireless power converter, which has the advantage of easy-driving, simple structure and cost-effective. This paper proposes a novel single-switch contactless resonant converter with secondary LCC compensation. Detailed characteristics analysis is performed. The design method of primary resonant capacitor, ensuring soft switching achieving, voltage stress limitation and power transfer capability, is given in the paper. A 500W prototype is then fabricated, with the efficiency of 93.8% for the coupling coefficient of 0.3. Experimental results verify the effectiveness of the analysis.

We present a complete low-power Wi-Fi backscatter communication solution which based on a single microcontroller, and for the first time we demonstrate the battery-free Wi-Fi device prototype which is built using commercial-off-the-shelf components on a printed circuit board. It can operate on power that is harvested from ambient light, and its power consumption is 2-3 orders of magnitude lower than that of traditional active Wi-Fi chipset. The instantaneous communication current is only 900uA, the standby current is 1.25uA, and the communication distance is 50 feet. The solution we proposed not only has the advantages of system simplification, low cost, and no need for battery, but also enables the rapid development of Wi-Fi IoT products with existing commercial microcontrollers.

This paper introduces an intelligent groundwater metering control system consisting of the lower computer, which takes electromagnetic flowmeter, temperature sensor, power meter as the main data acquisition unit and the development board as the main data processing unit, and the host computer with the corresponding software installed. The development board uses the Modbus Remote Terminal Unit (RTU) protocol as the main data communication format. The digital and analog modules read the data in the acquisition unit in real time, and after recording and analyzing the data, it can independently complete some control and realize some functions, and with the help of General Packet Radio Service (GPRS) Data Transfer Unit's fully transparent transmission mode, relying on GPRS and Internet network, it uploads data and running logs to the management system of the host computer, realizing remote, real-time, wireless processing and recording of data. The actual operation shows that the system can solve the inconvenience of groundwater resource management well and has certain use value and promotion significance.

Mining-induced water damage has become one of wide concerned issues in contemporary society. Knowing the main causes to water resource damage (especially the superficial aquifer damage) as well as the damage mode is the basis for realizing water conservation. In this paper, the mining height and position of aquifuge are regarded as the main influencing factors in analyzing the change of aquifer. Taking the geological conditions of Yili No. 4 coal mine as an example, a total of 18 numerical analysis models with different mining heights (3, 5, 8, 10, 15, 20 m) and different positions of aquifuge (upper, middle and bottom) as changing conditions were established using universal distinct element code discrete element simulation software. The variation characteristics of water resources in aquifers after disturbance were studied. The variation of water pressure with mining height and position of aquifuge was analyzed. The variation of water pressure in aquifer was proposed as a novel criterion for distinguishing the development degree of mining-induced fracture. The criterion for determining the failure of aquifer was established based on the ratio of water pressure change and mining height (P/H), which is a pioneering research on the relationship between mining-induced fracture and aquifer failure. This study provides a reference for evaluating the development degree of mining-induced fracture and provides theoretical basis for analyzing the coupling relationship between mining and water resource change in aquifer and for water conservation at ecologically fragile mining area.

Based on the mining practice in an extremely close coal seam, theoretical analysis was conducted on the vertical stress distribution of the floor strata under a large coal pillar. The vertical stress distribution regulation of a No. 5 coal seam was revealed. To obtain the optimum position of the roadway that bears the supporting pressure of a large coal pillar, numerical modeling was applied to analyze the relation among the stress distribution of the roadway surrounding the rock that bears the supporting pressure of a large coal pillar, the plastic zone distribution of the roadway surrounding the rock, the surrounding rock deformation, and the roadway layout position. The theoretical calculation results of the stress value, stress variation rate, and influencing range of the stress influencing angle showed that the reasonable malposition of the No. 5 coal seam roadway was an inner malposition of 4 m. The mining practice showed the following: the layout of No. 25301 panel belt roadway at the position of the inner malposition of 4 m was reasonable, the roadway support performance was favourable without deformation, and ground pressure was not obvious. The research achievement of this study is the provision of a reference for roadway layouts under similar conditions.

The development of high-speed rail (HSR) has had a major impact on the power supply grid. Based on the monitored waveforms of HSR, a pattern recognition approach is proposed for the first time in this paper to identify the operating conditions. To reduce the data dimensions for monitored waveforms, the principal component analysis (PCA) algorithm was used to extract the characteristics and their waveforms from the monitored waveforms data. The dynamic time wrapping (DTW) algorithm was then used to identify the operating conditions of the HSR. Cases studies show that the proposed approach is effective and feasible, and that it is possible to identify the real-time operating conditions based on the monitored waveforms.

A single-layer frequency selective surface (FSS) with wide stop band is proposed and applied in reducing the radar cross section (RCS) of the spiral antenna in this article. The element of the FSS consists of toothed spindle-shaped cross and rings. With the spindle-shaped cross and the toothed edges, the operating band of the FSS is enhanced and extended to the high frequency range. The metal rings are laid between the arms to decrease the coupling. The results show that the stop band of the FSS is 3.4–5.8 GHz under the condition of |S21

With the progress of science and technology, wireless transmission has entered people’s field of vision as a new transmission method. This paper describes the background of wireless transmission technology, and then introduced in detail the short distance transmission of electromagnetic induction, the basic principle of magnetic resonance coupling transmission, and the long-distance transmission of microwave laser, and then discusses the development of wireless transmission technology in detail, and then introduced the important technology that need to be focused on nowadays. Finally, the impact of wireless transmission technology on the grid and its future development direction are illustrated from three perspectives including smart grid introduction, power transmission and distribution, and distributed power supply.

The designed device uses high voltage capacitor in series with a voltage transformer to take power. It uses the capacitance voltage resistance and the transforming characteristics of voltage transformer, and the monitoring system is supplied with power by subsequent rectifier filtering and switching circuit. As high voltage capacitor step-down has the problem of limited energy, the set of device uses voltage transformer to increase power output, and the required power are provided to the load end on the basis of the changing of the load end power, and through the relay’s on or off to control the connection or disconnection of the front-end resistance. 10kv voltage transformer are used for 110kv line, and the primary side voltage of the voltage transformer is controlled at 10kV by using the voltage stabilizing function of the rear-connected circuit. Through The overall test of power output is stable, the device operates well.can provide stable and effective low-voltage power supply for the on-line detection device of 110kV high-voltage multiple circuit..

Femoral neck-shaft angle (NSA) is the angle included by the femoral neck axis (FNA) and the femoral shaft axis (FSA), which is a critical anatomic measurement index for evaluating the biomechanics of the hip joint. Aiming at solving the problem that the physician's manual measurement of the NSA is time consuming and irreproducible, this paper proposes a fully automatic approach for evaluating the femoral NSA on radiographs. We first present an improved deep convolutional generative adversarial network to automatically segment the femoral region of interest on radiographs of the pelvis. Then based on the geometrical characteristic of the femoral shape, the FNA and FSA are fitted, respectively, and thus, the NSA can be evaluated conveniently. The average accuracy of the proposed approach for NSA evaluation is 97.24%, and the average deviation is 2.58° as compared to the measurements manually evaluated by experienced physicians. There is no significant statistical difference (P = 0.808) between the manual and automatic measurements, and Pearson's correlation coefficient is 0.904. It is validated that the proposed approach can provide an effective and reliable tool for automatically evaluating the NSA on radiographs.

Saline-alkali soil is an arable land resource on which transgenic Bacillus thuringiensis (Bt) cotton has been planted on a large scale in accordance with food security strategies. There are, however, concerns about the insecticidal effects of Bt cotton on target insect pests. In this study, a Bt cotton variety, GK19, and its nontransgenic parent variety, Simian-3, were used as experimental models for investigating the effect of the expression of exogenous insecticidal proteins in Bt cotton under NaCl stress on the feeding behavior and nutritional parameters of Helicoverpa armigera. The results showed that the expression of exogenous insecticidal proteins in GK19 was significantly inhibited under NaCl stress. However, the feeding, crawling, resting and spinning down behavior of the 5th instar H. armigera larvae on GK19 Bt cotton, as well as the amount of food consumed and feces produced by these larvae, did not markedly differ under different NaCl concentrations. In contrast, the mean relative growth rate (MRGR), relative growth rate (RGR), approximate digestibility (AD), efficiency of conversion of ingested food (ECI) and efficiency of conversion of digested food (ECD) of the larvae markedly decreased in response to NaCl stress. Under the same concentration of NaCl, the nutritional parameters of the bollworm larvae on GK19 Bt cotton or Simian-3 nontransgenic cotton were different. However, the interaction between salt stress and cotton variety had no significant effect on the feeding behavior or nutritional parameters of H. armigera larvae. These results may provide a scientific basis for determining the effect of exogenous insecticidal protein expression in Bt cotton under NaCl stress on H. armigera and can therefore be useful for the effective application of Bt cotton in saline-alkali soils to prevent and control H. armigera.