Abstract:

In recent years, the vigorous development of “Internet ＋ Government Services” has promoted the deep integration of the Internet and the government public service system, and the construction of digital government has achieved initial results. How to achieve standardized, efficient, safe management, sharing and application of Internet ＋ big data in government affairs, and promote the solution of the “Three Integrations and Five Crossings Issues”, is the key to building a higher quality digital government and promoting the modernization of national governance system and capacity. This paper first summarizes the development of government data management at home and abroad. Secondly, it expounds the collection technology, storage technology, fusion technology, computing technology, application technology and security technology in the full life cycle of Internet ＋ government big data management, and summarizes the evolution of mainstream data management technology. Finally, this paper discusses some urgent problems in Internet ＋ government data management, and the development trend of Internet ＋ government data management technologies.

Abstract:

Cross-domain collaboration of government services is a new governance model, which has been spawned by the combination of digital transformation of government and cross-domain governance. This model is aimed at achieving the value goal of governance of government services. However, due to the different specific business and functions of each government department, each department has an independent data management system, and each information system has diverse storage, complex data formats and different business processes. As a result, sharing and utilizing the heterogeneous data between departments in a safe and reliable way has become a challenging research problem. Traditional government data sharing usually adopts a centralized sharing mode, which is prone to a series of issues such as data privacy leakage, departmental authority problems, and single point of failure. To address this issue, this paper proposes a government data sharing scheme that combines attribute-based encryption and blockchain. Firstly, an access control policy is formulated by the data owner to restrict the attributes of data requesters. Subsequently, fine-grained access control as well as key update in secure data sharing is achieved by using subset overlay technology, which is combined with linear secret sharing to achieve complete hiding of the access policy. The inter planetary file system distributed network is used to store the ciphertext after symmetric encryption to relieve the storage pressure of the blockchain system. Finally, the hash of the retrieved data ciphertext is re-encrypted using the Keccak algorithm to achieve data integrity verification. Security analysis and experimental analysis show that the proposed scheme can meet the requirements of secure sharing of government data in terms of security and efficiency, and thus realize the secure and traceable sharing of government data.

Abstract:

The construction of smart cities can effectively improve urban governance and operation capacity and break the urban development dilemma. To explore how to provide intelligent services for urban management through spatio-temporal big data in the physical-digital space intersection, this paper proposes a transparent fusion framework for multi-source spatio-temporal big data based on the analysis of the semantic relationships of multi-source, multi-dimensional, and heterogeneous spatio-temporal big data.To achieve this goal, a concept of “city information unit” is further proposed as the basis for building the data organization of physical-digital spatial integration. In particular, the multi-source, multi-dimensional,and heterogeneous spatio-temporal big data are first actively aggregated, semantically resolved, and then the geographic knowledge is constructed spatiotemporally; based on the unique data code, the data information is mapped to the city information unit; Next, in this paper, data matching model and association model are found, and a transparent data fusion framework is constructed. Combined with multi-source heterogeneous data element matching technology, a transparent spatio-temporal data fusion rule base is constructed. Finally, with the support of various fusion methods, the transparent fusion of urban entity and spatio-temporal multi-source spatio-temporal data is realized. With the help of urban information unit and data coding, we realize the dynamic integration system of urban entity and spatio-temporal big data, so as to provide users with intelligent information services.

Abstract:

Government data resources are characterized by a wide range of sources, diverse types, large data volumes and unclear data distribution, which can lead to lacks of unified management, high efficiency in data access and use, and ability to continuously promote the data release value. In order to solve the above problems and realize the correlative fusion of data from different sources and different types, this paper adopts the fusion of multi-source heterogeneous data to provide technical means for the exchange and sharing of government data. This paper proposes the concept of information channel and channel coupling, and provides a multisource heterogeneous information channel coupling method, which includes three module for constructing the Initial channel coupling knowledge graph, realizing the channel data coupling based on the channel coupling knowledge graph, and realizing the knowledge updating based on the channel coupling knowledge graph, respectively. In the proposed method, the technologies of atlas construction, knowledge extraction, knowledge fusion, knowledge processing and knowledge updating are integrated together, and a top-down sorting retrieval model is proposed to accelerate the speed and accuracy of knowledge fusion and data retrieval.

Abstract:

With the continuous improvement of digital construction of government affairs, the need for realizing “knowledge-based, personalized and intelligent” government services based on knowledge graph is gradually being awakened. At present, the application of knowledge graph in the government domain is often oriented to a single scenario, making it difficult to establish connections between different scenarios with respect to government knowledge. The search, management and approval efficiency of government services based on traditional databases is still not high. To expand the scope of government services and improve the efficiency of search, management, examination and approval, this paper proposes a set of top-down mapping methods to construct a multi-layer government knowledge graph. Specifically, this method first construct the conceptual model of government knowledge from the perspective of government service, and then obtain the government knowledge, data preprocessing and knowledge fusion according to the conceptual model; finally, forming a multi-layer government knowledge graph with top-down relationships of concept, business service, social service and information sharing. With the visual display of Neo4j and the deployed services, paper validate the proposed method by taking the search of real estate approval, the examination and approval of occupied forest land and the social service for public complaints as examples, which proves that the method is efficient and feasible. It not only provides knowledge graph support and association for different government scenarios, but also helps to realize the fusion and sharing of multi-source government data, thus providing a reference for the subsequent construction of government knowledge graph.

Abstract:

Subject to the friction and other uncertain factors, the multi-manipulator force & position hybrid controlling method is still a challenging issue. To investigate this problem, this paper proposes a hybrid control method based on fuzzy adaptive robust sliding mode algorithm. Firstly, the multi-manipulator system model is constructed with consideration of both multi-manipulator and the object dynamics equations. Secondly, the fuzzy algorithm is combined with the adaptive sliding mode algorithm to compensate for uncertain factors and unknown nonlinear terms, so as to improve the system reliability. Simulation results showed that, both controlling accuracy and responding time of the system can be improved by the proposed method.

Abstract:

Arrhythmia classification is a hot topic in physiological signal analysis. Arrhythmias are very common in clinical practice, and they are accompanied by abnormal patterns and rhythms in the heartbeat of the electrocardiogram signal. Correct and timely detection of arrhythmias and accurate early warning of cardiovascular diseases are of particular importance in the early stage of clinical diagnosis. However, the lack of real time diagnosis of electrocardiogram may delay the best time for patient treatment. Implementing heart rate disorder classification algorithms at edge-side smart terminals such as wearable devices enable real-time analysis and processing of electrocardiogram signals. In addition, they improve the flexibility and safety of the devices as well. By far, the field programmable gate array devices have been widely used in physiological signal processing as a form of edge computing due to its capability of real-time pipeline operation. Whereas, the field programmable gate array implementation needs a long development cycle, has high cost and is difficult to debug. To address these problems, the new high-level synthesis tool Vivado HLS from Xilinx is used to implement the arrhythmia five classification algorithm based on the MIT-BIH dataset. By using a Xilinx Zynq field programmable gate array, an average classification accuracy of 99.12% on the electrocardiogram signal test set is achieved. Moreover, an average of 3.185 ms required to classify a single heartbeat is realized, which leads to a speedup of more than 5.64 times compared to a single ARM core on the pure PS side.

Abstract:

In radiation oncology, it is usually difficult and time-consuming to manually profile the targets in the head and neck. Therefore, it is very necessary to develop an automatic medical image segmentation method, which not only saves time and energy, but also avoids the subjective variations among different physicians. In this work, we used positron emission computed tomography and computed tomography image data to segment head and neck tumors, and realized more accurate segmentation by using the complementary information between them. The network was developed based on the U-Net architecture, and an inception module was added into the encoder module. In addition, dense modules and spatial attention are added to the decoder to improve the network performance. Experimental results show that our method outperforms the other U-Net networks. Quantitatively, the dice similarity coefficient, recall rate and Jaccard similarity coefficient are found to be 0.782, 0.846 and 0.675, respectively. Compared with the original U-Net, these results corresponds to an improvement by 6.8%, 13.4% and 9.8%, respectively. The 95% Hausdorff distance is found to be 5.661, which is 1.616 smaller than the original U-Net. In conclusion, this study demonstrates that the inception spatial-attention dense U-Net model can effectively improve the segmentation accuracy on the head and neck tumor PET-CT images.

Abstract:

Terahertz wave is defined as the far-infrared electromagnetic (EM) radiation between 0.1 and 10 THz. It has attracted increasing attention in both fundamental research and technological applications for its unique characteristics. In terahertz telecommunication, radar and imaging systems, terahertz antenna is key for the performance. To date, reported terahertz antennas suffer from limited phase modulation, relatively low efficiency and small beaming angle. Here we address these challenges by first designing three types of terahertz graphene antennas with the smallest size of 5 μm. We then propose a novel type of antenna with dual resonances, and achieve dynamic phase modulation within the full 2π range and meanwhile high efficiency above 20%. This performance surpasses antennas with single resonance since dual resonances alleviate the contradiction between large phase modulation range and high efficiency. We prepared terahertz graphene antennas with standard micro-fabrication processes, and experimentally obtained terahertz dynamic phase modulation of 1.03 THz with reflection efficiency above 23%, which agree with the simulation results. Making use of the phase modulation metasurfaces tuned according to continuous phase coding, we numerically realized terahertz beam steering with dynamic range of -25°~25°. We expect this work will provide a strategy for achieving large-range phase modulation and beam steering beyond the terahertz regime.

Abstract:

After low temperature sintering, nano Cu paste produces the homogeneous interconnection structure with high temperature resistant, high electrical conductivity and thermal conductivity, which can not only avoid service reliability with the bridging problems caused by short circuit and electromigration of tin based solder layer and sintered Ag layer, but also solve the problem of thermal expansion coefficient mismatch of heterogeneous interconnection structure, and has important application value in integrated circuits and power device packaging. In recent years, the sintering interconnection technology of nano Cu paste has made significant research progress in the stability of Cu nanoparticles and low-temperature sintering performances. However, compared with the sintering interconnection technology of nano Ag paste, it still has much room for improving the stability, low temperature sintering performance and reliability of nano Cu paste. This paper describes the important research progress of nano Cu paste sintering interconnection technology from the sintering interconnection mechanism, sintering process optimization, Cu nanoparticle surface modification, nano Cu based composite paste, interconnection reliability, and packaging application, and prospects the follow-up technology development and research ideas.

Abstract:

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to notable advancements in the pharmaceutical sector regarding the development of mRNA vaccines. These vaccines have gained considerable attention given their straightforward production process, improved safety profile compared to DNA vaccines, and efficient expression of mRNA-encoded antigens within cells. In addition, mRNA vaccines offer the advantage of not requiring transcription within the nucleus, thereby eliminating the risk of integration into the host genome. Nevertheless, mRNA vaccines also have limitations, such as possible allergy, kidney failure, and other serious side effects, or may rapidly degrade after injection or cause a cytokine storm. These factors present substantial challenges concerning the immunogenicity and delivery of mRNA vaccines. The purpose of this article is to primarily focus on the molecular design, delivery systems, and current clinical status of mRNA vaccines, aiming to provide valuable insights for future advancements in this field.

Abstract:

Chemerin, derived from tazarotenib-induced gene2 (TIG2), is an endogenous ligand for the orphan G protein-coupled receptor chemokine-like receptor 1 (CMKLR1). Chemerin/CMKLR1 signaling system plays an important role in multiple tissues and organs，and there are multiple chemerin isoforms in vivo due to the C-terminal proteolysis by several proteases. In this paper, we predicted and modeled the structure of six isoforms of chemerin by Alphafold, and modeled three active isoforms in complex with CMKLR1,to elucidate the different binding sites of different isoforms. Additionally,the known small molecule antagonist of CMKLR1, 2-(α-naphthoyl) ethyltrimethylammonium iodide (α-NETA), was also modeled to dock with CMKLR1, and the binding sites of α-NETA with CMKLR1 were analyzed. From the protein molecular structure level, our results provide: 1. The mode of interaction between active Chemerin and CMKLR1；2. The mode of interaction between α-NETA and CMKLR1. This study provides theoretical basis and experimental basis for the design of targeted drugs for CMKLR1.

Abstract:

As a representative low-level vision problem, image super resolution (SR) aims to reconstruct the high-resolution image from its low-resolution counterpart. For a long time, the analysis of SR tasks is based on the whole image, while little works observe the input partition. In this paper, we find that the restoration quality of a certain position is inseparable from its surrounding image background. This phenomenon provides us a new perspective to explain the networks by splitting the input image. We construct a new hybrid dataset, of which the foreground and background contain only one kind of texture information. And then, we prove that the similar background could benefit the network restoration. By analyzing similarity and difference between the attention mechanism and the traditional CNN network, we show that the attention structure could help the network focus on long-range effective information. Moreover, a data enhancement method to improve the network final performance and potential future works are also proposed.

Abstract:

Robots are important equipment in modern industrial manufacturing and production. As products move towards small batch, multi-variety, and flexibility, robot collaboration systems based on multi-information fusion enable high-end precision manufacturing industries. This research focuses on the precision alignment and insertion technology of a hand-eye cooperation system in the field of precision electronic component assembly. By establishing a contact state model between electronic components and heterogeneous plates, the motion characteristics of force and displacement are analyzed. A compound control algorithm integrating visual, force sense, and encoder information is proposed, combined with visual inspection and tracking technology. Component insertion comparison experiments and assembly experiments based on algorithm fusion are carried out on the electronic component assembly platform. The results show that the positioning accuracy in the alignment phase is within 0.185 pixels. The contact state judgment and adjustment algorithm in the assembly stage ensures the safe and effective assembly of components.

Abstract:

Over the past several decades，Brain-computer interfaces have made remarkable progress.In terms of the capability of implementing the technology and the functional scenarios it serves, Brain-computer interfaces technology will soon face clinical application.In this process, there will be new challenges in translating scientific achievements into commercial products.In this paper, We first introduced the characteristics of electroencephalogram, electrocortical graph, and intracortical electrical signal commonly used in Brain-computer interfaces.Then,we describe the decoding capability and the bidirectional closed-loop of information, and discuss the stability and biocompatibility problems of designing commercial brain-computer interface products.At the end of the article, we discuss the policy, fund and technical route in the industrialization development.This review aims to discuss the challenges faced by brain-computer interface technology in the process of application for the medical field, and the corresponding technical route and industrial development plan.

Abstract:

The purpose of automatic judgment document summarization is to allow computers to automatically select, extract, and compress important information from legal texts so as to reduce workload of practitioners. Currently, most summarization algorithms based on pre-trained language models have limitations on the length of the input text, so they cannot effectively summarize long texts. In this thesis, we propose a new extractive summarization algorithm, which uses a pre-trained language model to generate sentence vectors. Based on the Transformer encoder structure, the summarization task can be completed by fused information including sentence vectors, position and length of sentences. Experimental results showed that, the algorithm can effectively handle the task of summarizing long texts. In addition, the model was tested on the 2020 CAIL (Challenge of AI in Law) summarization dataset, and results showed that compared to the baseline model, the proposed model showed significant improvement in the ROUGE-1, ROUGE-2, and ROUGE-L metrics.

Abstract:

In augmented reality, virtual reality and the metaverse, three dimensional (3D) reconstruction technologies play important roles in acquiring the content information. Among them, the structured light method has been widely used due to the advantages of high precision and not being affected by the texture of the surface material on the object. Traditional structured light 3D reconstruction mainly uses digital light processing (DLP) based projectors to project coded patterns. However, the shortcomings of DLP projectors such as large size, high power, and high price limit their applications. Therefore, more 3D scanning systems start to use a micro-electro-mechanical system (MEMS) mirror as a structured light projector, which has small size, low cost, and high frame rate. In this paper, the phase-height model is proposed to complete the 3D scanning system based on MEMS mirror. To deal with the noise caused by the speckle effect, anti-noise performance of three time-phase unwrapping algorithms commonly used for MEMS mirror are experimentally compared. Results show that the multi-frequency hierarchical method and the negative exponential fitting method show better anti-noise capability and higher precision, while the multi-frequency heterodyne method shows poor anti-noise performance. This study provides a guide in choosing proper phase resolution method for MEMS based 3D scanning systems.

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:

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:

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:

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:

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:

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:

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:

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:

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:

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:

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.