Abstract:

To study the effects of pulse modulated radio frequency electromagnetic field on locomotor activity and neurotransmitter concentration in different brain regions of mice. Wild type C57BL/6J mice were randomly divided into intervention group (n＝9) and control group (n＝6). The intervention group was given pulse modulated RF electromagnetic field stimulation, while the control group was not given stimulation; 30 minutes a day for 5 days. The behavior of mice was recorded by camera, and the concentration of various neurotransmitters in mouse brain was measured by HPLC-MS. After the intervention, the locomotor activity of mice in the intervention group decreased every day compared with that before the intervention (P＜0.05); Intracortical concentrations of γ-aminobutyric acid, acetylcholine and other neurotransmitters, tryptophan and phenylalanine changed significantly (P＜0.05), the level of serotonin exhibited a tendency of decrease, but the concentration of glutamate did not change significantly. There were no significant changes in the locomotor activity, concentration of neurotransmitters, tryptophan and phenylalanine in the brain of control group animals. The 5-day continuous intervention had no negative effect on the anxiety level and autonomous behavior of mice. Pulse modulated RF electromagnetic field could exhibit rapid impact on the locomotor activity of mice, as well as the concentration of various neurotransmitters in different brain regions. The increased level of introcortical γ-aminobutyric acid may be involved in the reduced locomotor activity of mice following such RF electromagnetic field treatment.

Abstract:

Photothrombotic ischemia is a common experimental ischemic stroke model. In response to light stimulation, activated photosensitive dyes produce reactive oxygen species, which in turn induces damage to the vascular endothelial cells, causing platelet adhesion, aggregation and thrombosis. Since the conventional photothrombotic ischemia model produces only a tiny ischemic penumbra which can’t properly represent the clinical pathology, a modified proximal middle cerebral artery occlusion model was established in this study. The mouse proximal middle artery was irradiated by laser for 3 minutes to induce thrombosis following injection with the light-sensitive dye Rose Bengal and subsequently evaluated by 2,3,5-triphenyltetrazolium chloride staining, immunofluorescence, and flow cytometry. The results showed that this model produced a stable infarct area of 9% to 15% in the striatal and cortical regions, which is larger than the conventional photothrombotic ischemia. Resident microglia, infiltrating myeloid cells, and lymphocytes in the infarcted tissue were identified by flow cytometry. It is suggested that the modified proximal middle artery occlusion model can be applied to study the pathological and immune mechanisms after ischemic stroke injury.

Abstract:

Electrical nerve stimulation is an effective method for certain treatments by affecting the central or peripheral nervous system. The instability of electrical nerve stimulation is a critical problem in clinical practice. It is a widely held view that the mechanism of the instability is that the electrical stimulation disturbs the membrane potential of nerve axons. However, due to the lack of a computable macro model for electrical nerve stimulation, it is difficult to effectively study the specific impact of its membrane potential disturbance on electrical stimulation for a long time. Based on the previously proposed circuit-probability theory, this study qualitatively analyzes the instability of electrical nerve stimulation to effectively research the influence of membrane potential disturbance on electrical stimulation. The results show that the current-instability curve of animal experimental data and qualitative simulation is highly consistent, which further indicates that the circuit-probability theory might explain the membrane potential disturbance caused by electrical stimulation and has instructive significance for the practical application of electrical nerve stimulation.

Abstract:

Blood pressure is a physiological indicator of human body. Continuous measurement of arterial blood pressure in each cardiac cycle is an important basis for real time diagnoses. Most of the cuffless continuous blood pressure measurements are performed according to the predictive models based on the pulse wave and electrocardiogram signals. However, they may produce errors due to the limited measurements. In this paper, multiple physical signs, such as impedance cardiogram, are explored to improve the measured accuracy of blood pressure. Experiments were conducted upon 55 volunteers, and results show that the random forest model based on multi-parameter feature fusion outperformed the linear model based on a single feature, with mean absolute errors of 2.56 mmHg and 1.91 mmHg for the prediction of systolic and diastolic blood pressure, respectively. It proves that the proposed cuffless blood pressure prediction model based on the multi-feature fusion could improve the accuracy of blood pressure prediction.

Abstract:

Underwater optical imaging has the problem of “not seeing far” and “not seeing clearly” due to the absorption and scattering of the water body. Underwater laser range-gated imaging technology can improve the underwater optical imaging distance and image contrast. The paper presents the research of underwater long-range target intelligent identification system based on underwater laser range-gated imaging technology. Laboratory test results show imaging distances in excess of 7 times the attenuation length. The study combines deep learning algorithms to achieve quasi-real-time detection of targets in power-constrained hardware conditions. The combination of underwater laser range-gated imaging technology and deep learning algorithms is expected to enable underwater optical imaging to “seeing far” and “seeing clearly”, while “seeing fast” and “seeing accurately”.

Abstract:

The sentence embeddings using Siamese BERT-Networks pre-trained language model has two shortcomings in its presentation layer for text matching, that is, (1) two queried texts are directly computed after they are represented in vectors by the BERT Encoder, (2) such computation does not consider the needs to refine the granular representation of the two queried texts. As such presented semantics could be deviated and it is also difficult to assess the importance of single words in text matching. This paper proposes an improved text similarity matching model SBMAA based on SBERT pre-trained language model. Firstly, the hidden layer vectors of the two queries passing through the SBERT model are obtained, and then the similarity matrix between the two is calculated. The attention mechanism is used to encode the tokens in the two sentences again to obtain interactive features and pool them. Finally, the fully connected layer is connected for prediction. This method introduces the multi-head attention alignment mechanism, which is a common way of interactive text matching algorithm, and strengthens the correlation degree between similar texts, so that the model can achieve more accurate matching effect. The experimental results on ATEC 2018 NLP data set and CCKS 2018 Webank Customer Question Matching dataset show that compared with the five popular text similarity matching models ESIM, ConSERT, BERT-whitening, SimCSE and Baseline model SBERT, The proposed SBMAA model achieves 84.7% and 90.4% in F1 evaluation index, 18.6% and 8.7% higher than Baseline, respectively. It also shows good effect in accuracy and recall rate, and has certain robustness.

Abstract:

Nowadays, triboelectric nanogenerators have shown their potential in energy harvesting research with far-reaching impacts, since they have simple structure and wide applicability. However, several drawbacks have yet to be overcome for further extension of its application and commercialization. One major issue is friction, the origin of energy generation and a major factor in limiting energy conversion efficiency. The friction induces energy loss by heat dissipation and also causes the loss of friction layers, lowering the device’s durability. Meanwhile, the friction also increases the threshold force required to drive the device. A multi-layer stacked device with increased friction area will be difficult to be powered by slight shaking generated by wind or human walking. This study proposes a shaking pulse generator based on the principle of programmable Triboelectric Nanogenerator to solve the above-mentioned issues. Even if the material of the friction layers is the same, such as the PTFE film, hundreds of volts can still be achieved. Unlike the traditional theory, there is no real contact friction which minimizes energy loss, reduces driving energy, and improves energy conversion efficiency.

Abstract:

Space special environment can cause potential damage to astronauts, thus the monitoring of physiological indicators is crucial for the study of damage mechanisms and means of protection. Microgravity as one of the space special environments can lead to mitochondrial dysfunction. Meanwhile, mitochondrial membrane potential is an important indicator of normal mitochondria, it is meaningful to monitor mitochondrial membrane potential under simulated microgravity (SMG) quickly and easily. In this work, a mitochondria-targeting aggregation-induced emission (AIE) probe (TPE-Ph-In) is developed to monitor mitochondrial membrane potential under SMG. At the same time, in order to overcome the problem of insecure cell apposition under a prolonged time of SMG, an AIE probe-hydrogel 3D imaging system is constructed by seeding the cells into Matrigel and imaging the cells with TPE-Ph-In. This work provides a new method and idea to investigate the microgravity effect of cells.

Abstract:

Blood pressure is a major physiological index of human body. Continuous measurement of arterial blood pressure in each cardiac cycle is an important basis for medical staff to diagnose patients in real time. However, the current cuffless continuous blood pressure estimation method has been faced with the problem of low accuracy, which is difficult to apply to the clinic. Therefore, the continuous non-invasive blood pressure monitoring method and technology for medical staff has become a research hotspot in academia and industry.Most of the existing cuffless continuous blood pressure measurement methods are based on the characteristics of pulse wave and electrocardiogram signals. But they cannot cover a variety of factors affecting blood pressure, their models have some errors. Based on the traditional pulse wave and electrocardiogram signal, this paper introduces more information of physical signs to explore the factors affecting the accuracy of blood pressure measurement. Through the experiment of 55 volunteers, it is proved that the cuffless blood pressure prediction model based on multi-feature fusion can improve the accuracy of blood pressure prediction.

Abstract:

Under the "Internet ＋" environment, the integration of government big data with public services and social sensor data has transformed the urban management model from single to comprehensive, the urban service system from isolation to sharing, and the urban decision-making model from mechanical to intelligent, which is the development trend of government service. In order to solve the problems of insufficient integration of government affairs data, public service data and social sensing data, insufficient sharing, low utilization efficiency and lack of technical platforms, the overall framework of the "Internet ＋" government affairs big data intelligent service platform is proposed. This paper designs the platform construction theory, system architecture, technical architecture, core technology and service application, etc., and realizes the transparent access of the "Internet ＋ " government big data intelligent service platform, city information unit, transparent intelligent agent, transparent management and microservices, etc. Exploratory applications have been carried out on platforms such as Digital Guangdong and Dandong Social Security in Liaoning, showing that the platform can effectively support the efficient sharing, management and utilization of government affairs data, public service data and social sensor data, and improve department service effectiveness and capability. The overall design scheme of the platform proposed can provide valuable reference for provincial and municipal governments to carry out the construction of "Internet ＋" government big data intelligent service.

Abstract:

Nowadays, with the rapid development of the Internet, more and more new applications appear gradually, the scale of network expand constantly, and the architecture of network is more and more complicated. As one of the basic technologies for enhancing network controllability, traffic classification can not only provide better QoS for ISPs, but also supervise and manage network effectively, which can ensure the security of the Internet. In this paper we review the research methods and achievements in the field of traffic classification, compare these traditional methods, and point out their advantages and disadvantages. On the other hand, for the real challenges of real-time classification of high-speed network environment, encrypted traffic classification, fine-grained traffic classification, and dynamically changed protocols classification, we describe and analyze the related research progress. Finally, we look ahead the future research direction.

Abstract:

The technology of the robot represents a national high-tech level and the degree of automation. It is helpful to develop the industry of service robots in China if we know the current situation and development trend of service robots research clearly. Recently, robotic cleaners and educational robots have been in great demand. Entertainment robots and surveillance robots are developed rapidly and the market expands quickly. Medical robots begin to enter the modern life and have played an important role in the modern surgery. To satisfy the great market and shorten the distance between China and developed countries, it is necessary to capture the development trend of the technology of service robots. R&D on service robots should focus on the integrated technologies on intelligence, modularization and network.

Abstract:

Gene is the genetic material basis. All life phenomena, like disease and death, are related to Gene. Gene sequencing is a way to read life. With the development of new generation high-throughput sequencing technology, TB or more sequence data will be generated daily. It’s more difficult to interpret these big and complex data than to acquire them. Sequence data interpretation is a critical step in current biological research and has great practical significance. It’s a great challenge for current computer systems and computing models to store, process and analysis massive high throughput sequence data. With survey, especially from BGI (Beijing Genome Institute), the current status, problems and measures taken to process high throughput sequence data will be discussed. However, the challenge is too big to be solved unless more people in different fields work together in depth for a long term.

Abstract:

This paper gives a comprehensive introduction to the status of current machine translation research and technology, and analyzes the key problems to be resolved. Finally our idea of the future trends and prospects of machine translation are put forward.

Abstract:

With the increasing concerns of global warming and resource constraints, electric vehicles (EVs) have made great progress during the past decade. The electric driving system of EVs has dinstinct advantages, such as quick response, easy measurement , and precise control of motor torque, available flexible driving architecture, and regenerative braking, etc. Such advantages can be used to improve the performance of vehicle dynamic control. This paper presents the recent research efforts on electric vehicle dynamic control in terms of parameters estimation and dyanmic control scheme and methodology, especially focusing on the tire-road friction estimaion , novel traction control methods. The lateral dynamic control including the electrical differential control, direct yaw moment control, and the integratin chassis cotrol is proposed. Several prospects for vehicle dynamic control are proposed.

Abstract:

For the advantages of noninvasive, real-time and quantitative detection, ultrasonic transient elastography has important clinical application value. This work investigates the transient elastography in a few ways and aims to design a transient imaging system. The Displacement tracking algorithm based on correlation techniques and the parabolic interpolation algorithm is proposed to improve the accuracy. A novel match filter is designed to convolute with the estimated displacement in the time direction to boost the SNR of the displacement for a better strain image mapping. The convoluted result shows the match filter can significantly improve the strain image quality and help getting more accurate Youngs modulus estimation. The Time Gain Compensation (TGC) circuit is designed to compensate the attenuated power of the ultrasound signal. And a modified polyacrylamide gel based tissue-mimicking phantom is also developed in this paper, both indentation testing and transient elastography are used to characterize the elastic properties of this phantom. The results are almost consistent with each other.

Abstract:

Hadoop job schedulers typically use a single resource abstraction and resources are allocated at the level of fixed-size partition of the nodes, called slots. These job schedulers ignore the different demands of jobs and fair allocation of multiple types of resources, leading to poor performance in throughput and average job completion time. This paper studies and implements a Muti-resource Fair Scheduler (MFS) in Hadoop. MFS adopts the idea of Dominant Resource Fairness (DRF). It uses a demand vector to describe demands for resources of a job and allocates resources to the job according to the demand vector. MFS uses resources more efficiently and satisfies multiple jobs with heterogeneous demands for resources. Experiment results show that MFS has higher throughput and shorter average job completion time compared to Hadoop slot-based Fair Scheduler and Capacity Scheduler.

Abstract:

Automatic drive is an important application field of artificial intelligence. In this paper, a novel training strategy for self-driving vehicles was investigated based on the deep reinforcement learning model. The proposed method involves a Q-learning algorithm with filtered experience replay and pre-training with experiences from professional drivers, which accelerates the training process due to reduced exploration spaces. By resampling the input state after clustering, generalization ability of the strategy can be improved due to the individual and independent distribution of the samples. Experimental results show that, in comparison with conventional neural fitted Q-iteration algorithm, the training efficiency and controlling stability can be improved more than 90% and 30% respectively by the proposed approach. Experimental results with more complex testing tracks show that, average travel distance can be improved more than 70% in comparison with the Q-learning algorithm by the proposed method.

Abstract:

With the rapid increase in numbers and scales of deep web sites on the Internet, search for data or information from deep web sites by submiting queries to and obtaining results from the backend databases has become a major means in information retrieval from the Web. This area has attracted many researchers to devote their efforts on development of technologies to make better use of information in th deep web. One challenge is searching for and integration of data from various databases in deep web. Since deep web is dominated by text data, research and development of technologies for text information retrieval from deep web have a broad application potential. In this paper, we review the state-of-the-art of deep web research in details and propose some future research directions.

Abstract:

Wireless Capsule Endoscope (WCE) is a very promising tool for the examination of the gastrointestinal (GI) tract. However, there are some problems to be solved for the existed WCE, and one key problem is the accurate localization and tracking of the WCE. Among the possible localization methods, the magnet-based localization technique has its advantages: no need for power, not much space occupation, continuously tracking ability, and no negative effect. In this paper, we present the localization method for the magnet objective inside the WCE based on the magnetic sensor array outside the human body. Through the algorithm and system design we realize real time tracking of 3D position and 2D orientation of the magnet based on the magnetic dipole model. In order to overcome the interference of the human body movement, we propose the multi-magnets’ localization method; also, the 3D positioning and 3D orientation method is proposed, which can be used to make the 3D recovery of the GI tract and the accurate computation of the physiological tissue parameters. The real experiments show that the proposed localization system can run well and obtain the accuracy with 2~3mm for the magnet.

Abstract:

Several of the top ranked supercomputers are based on the hybrid architecture consisting of a large number of CPUs and GPUs. High performance has been obtained for problems with special structures, such as FFT-based imaging processing or N-body based particle calculations. However, for the class of problems described by partial differential equations (PDEs) discretized by finite difference (or other mesh based methods such as finite element) methods, obtaining even reasonably good performance on a CPU/GPU cluster is still a challenge. In this paper, we propose and test an hybrid algorithm which matches the architecture of the cluster. The scalability of the approach is implemented by a domain decomposition method, and the GPU performance is realized by using a smoothed aggregation based algebraic multigrid method. Incomplete factorization, which performs beautifully on CPU but poorly on GPU, is completely avoided in the approach. Numerical experiments are carried out by using up to 32 CPU/GPUs for solving PDE problems discretized by FDM with up to 32 millions unknowns.

Abstract:

Effectively reducing power line interference is always an important issue in electromyography (EMG) signal recordings and analysis. In this study, four commonly used de-noising methods, including digital notch, LMS based adaptive filter, Kalman filter and S transform, which may be suitable for the reduction of power line interference in real-time EMG recordings, were chosen and their performance in reducing the power line interference from EMG signal recordings were quantitatively analyzed and compared. The pilot results of this study showed that Kalman filter presented the best whole performance in attenuating power line interference from EMG signals and S transform de-noising method illustrated the best performance when the power line interference was severe.

Abstract:

This paper introduces the scientific computing grid, ScGrid, and it’s middleware SCE. ScGrid is built as one virtual supercomputer, integrating computing resource from more than 30 institutes. It provides unified,?easy to use and reliable scientific computing services. SCE is a lightweight grid middleware, which supports global job scheduling and unified data view. It provides multiple user interfaces including command line, grid portal and APIs. At present, ScGrid has been very successfully used in Chinese Academy of Sciences and widely accepted by more than 200 users.

Abstract:

Foot drop is the inability to voluntarily dorsiflex the ankle during the swing phase of gait and is usually caused by weakness and damages of the peroneal nerve. The consequences of the foot drop include the decreasing of gait quality, the limiting of mobility, the increasing of falling risk, and great increasing of energy expenditure during walking. Firstly biosignal sensors are used in the drop foot stimulator to detect foot movements. Then the surface drop foot stimulator produces a predefined stimulation profile to the peroneal nerve or tibialis anterial to elicit a dorsiflexion of the foot synchronized with the swing phase of gait to lift the foot. This paper reviewed the fundamentals and current researches of drop foot stimulators. Moreover, the development trends of the closed loop drop foot stimulator were also discussed in the paper.

Abstract:

Thirty years ago, the invention and volume shipment of IBM PC significantly enlarged the user population of computing. For the next thirty years, what is the most fundamental challenge of the computing field? What paradigm shift is needed? What is the most significant industrial problem? What are the most needed scientific breakthroughs? This article addresses these questions by discussing a dozen essential issues of computing for the masses. The most fundamental challenge is the computing market’s growth stagnation. Computing for the masses is proposed to reverse this trend and should be a fundamental future direction. It has three features of value-augmenting, affordability, and sustainability. The most basic paradigm shift is human-cyber-physical ternary computing. The most significant industrial problem is the Insecta Classis paradox. Computing for the masses needs five pillars of science support, including ternary computing science, universal compute account, efficient sea-network-cloud computing platforms, information ecosystem science, and national information accounts. This article helps outline the problem space for future computer science research, with a discussion on related transformative research projects.

Abstract:

Transcranial magnetic stimulation (TMS) is a non-invasive technique that can be used for brain studying and clinical therapy. Firstly, the technology feature and application of the TMS instrument were introduced. Then several TMS coil positioning methods were evaluated and several key problems about TMS coil positioning were discussed. The aim of this study was to propose a new method for TMS coil positioning. The new method combines three aspects of quantitative information including the brain scalp, brain anatomy and brain function and has great advantages and broad application prospects.

Abstract:

Human action recognition acts as an important role in human machine interaction. This paper proposes a human body recognition method from depth image based on part size and position features. Random forest classifiers are trained with different parameters. Experimental results demonstrate the feasibility of proposed approach. Recognition accuracy is about 91% and the computation time is about 0.96 us per pixel.

Abstract:

In the past work for long time, since the computation required on object deformation and interaction is intensive, when fluid is interacted with rigid bodies, or especially with animating figure, the demand of real-time simulation and rendering could be hardly achieved. This paper presents a novel approach for generating effects simulated by fluid dynamics and interacted by the figure motions. In order to handle the interaction between fluid effect and deformed figure, firstly, the motion trajectory of character is tracked, and then the fluid dynamics is simulated by the model of Smoothed-Particle Hydrodynamics (SPH). Moreover, during the fluid simulation, an efficient algorithm for particle searching is also proposed, in virtue of parallel processing by GPU. Consequently, the simulation of 3D fluid effects with realistic character interaction can be rendered on a consumer-level PC in real-time.

Abstract:

Nanomaterials and nanotechnology play more and more important role in the field of new generation electronics packaging. The unique electrical, magnetic and optical properties of the nanomaterials along with their composites can enhance and improve the physical and mechanical properties of the components. Current issues of electronic packaging, especially those related to materials, are introduced and evaluated in this review. The nanomaterials, nanocomposites and nanotechnology have advantages in solving these issues and their future development direction. This review also focuses on the application of new materials such as, conductive metal particles, silica, carbon nanotubes, graphene, etc. in the high density system level packages.

Abstract:

Random Forests is an important ensemble learning method and it is widely used in data classification and nonparametric regression. In this paper, we review three main theoretical issues of random forests, i.e., the convergence theorem, the generalization error bound and the out-of-bag estimation. In the end, we present an improved Random Forests algorithm, which uses a feature weighting sampling method to sample a subset of features at each node in growing trees. The new method is suitable to solve classification problems of very high dimensional data.

Abstract:

Morphological parameter measurement of Pseudosciaena Crocea plays an important role in its genetic selection and quality improvement. In this paper, an automatic detecting system which can measure the Pseudosciaena Crocea morphological parameters such as weight, length and body width was developed based on the machine vision and weighing sensor technology. The system can automatically detect the external morphology parameters by the machine vision, and get weight parameters through the weighing sensor. The mean errors of dimensional measurement and weighting are 0.28% and 0.74% respectively, which shows that the developed system can completely meet the requirements of morphological parameter measurement for Pseudosciaena Crocea. It is a new effective method to the automatic detection of fish morphology parameters.

Abstract:

In this work the thermal behavior of the LiNi1/3Co1/3Mn1/3O2 cathode material for soft packed lithium-ion power batteries during charging and discharging at different C-rate were conducted using the ARC (accelerating rate calorimeter) to provide an adiabatic environment. The overall heat generated by the lithium-ion battery during use, is partly reversible and partly irreversible, due to entropy change and joule heating, respectively. It indicates that the heating generation of lithium-ion cell is decided by the C-rate of charge and discharge. The heat is smaller at low C-rate of charge and discharge. For example, the heating generation of battery increases 7.16℃ at 0.2C-rate and the entropy change heat is clearly embodied. The joule heating is more remarkable than the entropy change during charging and discharging at high C-rate. For instance, the heating generation of cell increased 25.63℃ at 1C-rate. The heat generation of charge is less than discharge at the same C-rate. The DC inter insistence of cell at the SOC (State of Charge) of 0 to 10% increases suddenly, so the heating generation power will reach its maximum in this period during discharge. It is valuable for the design of heat dissipation in lithium-ion battery thermal management.

Abstract:

This paper gives a comprehensive introduction to the status of current machine translation research and technology, and analyzes the key problems to be resolved. Finally our idea of the future trends and prospects of machine translation are put forward.

Abstract:

Smart city and smart planet are the new stages of global information development. As the core technology for the development of smart city, high performance computing got new challenges and opportunities. The challenges of smart city to high performance computing include: massive data computing, realtime computing, social computing and effective computing. Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences, is working on these areas; the latest development and infrastructure built at SIAT are summarized in this paper.