• The rapid development of biomaterials and biotechnology has provided important means for revealing life phenomena and life processes, which is the basis for tissue and organ regeneration and reconstruction, and so is the catalyst for a second life. This special issue “Development and Exploration of Biomaterials in Shenzhen” has published wonderful reports and extended content of the 2021 Shenzhen Biomedical Materials Annual Conference, so that the readers who were unable to attend due to the COVID-19 epidemic could also take a glimpse of this annual conference via the special issue. [MORE]
  • The ocean plays an important role in the future development. The Shenzhen Institute of Advanced Technology(SIAT), Chinese Academy of Sciences has deeply involved in the field of marine science. This special issue introduces recent research of SIAT’s team, covering marine engineering technology, underwater acoustic technology, underwater wireless transmission technology, marine biochemical sensing technology and seawater desalination technology, etc. In addition, low-power marine instrument recovery communication beacons developed by Professor Yang Ting’s team [MORE]
  • Advanced electronic material is one of the three main elements of integrated circuit and is the foundation and support of electronic information industry. Trade frictions occurred in recent years fully illustrate the strategic importance of materials, especially electronic materials used in integrated circuit industry. In this context, we specially invited Professor Rong Sun, director of the Shenzhen Institute of Advanced Electronic Materials, as the guest editor to organize the special issue focused on high-end electronic packaging materials for integrated circuit [MORE]
  • In recent years, the Chinese government has provided strong support for new energy vehicles and intelligent connected vehicles in terms of scientific and technological research, industrial development, application demonstration, and market promotion. Interestingly, China has become one of the most active countries in the field of new energy vehicles. Although the new energy vehicle industry has shown a good momentum in China, it has to overcome core technological barriers. [MORE]
  • With the rise of 5G communication, Internet of Things, new energy automotive electronics, wearable devices, and smart cities, affiliated electronic devices are developing towards the directions of miniaturization, high-power density, and multi-functionality. This will continue to increase the risk of overheating with related electronic devices. The development of high-performance thermal management materials is crucial to improve the heat dissipation of electronic devices, and it has become the biggest challenge faced by academia and application industry in electronic devices. [MORE]
  • Recently, with the maturity and popularization of technologies such as Internet of Things, cloud computing, mobile internet, and Internet of Vehicles, massive data in various formats like images, audiovisual materials, and health files are rapidly generated. The International Data Corporation (IDC) predicted that global data volume would reach 175 ZB (approximately 175 billion TB) by 2025, which indicated that more than 99% of all data in human civilization were generated in recent years. [MORE]
  • This special issue majorly reports the research exploration made by the key members from Guangdong Innovation Team of Advanced Functional Film Materials and Industrial Applications, which includes the analysis and discussion of preparation methods and growth mechanism of high-preferred orientation diamond film and high-quality single crystal diamond, the research of diamond film in cemented carbide tools, the latest research progress on film thermal expansion coefficient and residual stress testing technology. [MORE]
  • Big data is leading a new round of technological innovation, and it has brought new impetus and opportunities for the transformation and upgrading of social economy and the enhancement of national competitiveness. Therefore, many countries have proposed initiatives to develop big data. In recent years, big data has triggered extensive studies in a variety of disciplines and brought changes in terms of technology, model and ideology to different industries. The special issue was organized around big data platforms and supporting technologies, and big data applications, security and privacy [MORE]
  • Intelligent connected vehicles are equipped with advanced on-board sensors, controllers, actuators and other devices, and integrate modern communication and network technologies to realize information sharing between vehicles, roads, people, and clouds to achieve "safe, efficient, comfortable and energy-saving" driving. Although the industry shows a positive trend of comprehensive development, it is facing several technical adjustments on core technology level, including bicycle perception and decision-making, vehicle-road cooperation, human-machine co-driving [MORE]
Journal of Integration Technology (CN 44-1691/T, ISSN 2095-3135) is a peer-reviewed open-access journal published bi-monthly. It focuses on multidisciplinary integration especially involved in the fields of information technology, biotechnology, new energy and new materials. Its publishing scope includes but is not limited to the big data, artificial intelligence, computer, synthetic biology, brian science, biomedicine, biomedical engineering, new energy, advanced materials, smart driving, smart city, internet of things, electric vehicles, ocean technology. [MORE]
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    Volume 13, No. 3 | 2024
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    • YUAN Tao, QU Qiang, JIANG Qingshan

      2024,13(3):4-24, DOI: 10.12146/j.issn.2095-3135.20231101001


      In this era of massive data, DNA serves as a promising new medium for information storage. Compared to traditional physical storage media, it possesses inherent advantages such as low energy consumption, high storage density, and long storage lifespan. With the rapid development of DNA storage, ensuring information security under new technologies becomes crucial. In this regard, this paper combines research in the fields of encryption and DNA coding, proposing a DNA encryption coding method based on chaotic systems and fountain codes. The encryption principle of chaotic systems is utilized during the DNA fountain code encoding process, preserving the characteristics of DNA fountain codes while ensuring the security of encoded information. This method is applicable to any types of data, achieving high information density and DNA encoding under arbitrary constraints. Furthermore, through simulation experiments, it is demonstrated that this method can effectively resist various cryptographic attacks and possesses errorcorrection capabilities for data errors generated during the DNA storage process.

    • LIU Yangyi, ZHANG Yi, LIU Kai

      2024,13(3):25-38, DOI: 10.12146/j.issn.2095-3135.20231031002


      DNA molecules exhibit highly promising properties of high storage density and extended lifespan as a medium for next generation digital data storage. Thus, it is expected to act as an alternative to address the global issue of insufficient data storage materials. However, the current advances of DNA-based information storage are mainly focused on “the cold storage of information”, so that it is difficult to realize quick data processing in DNA, such as rewriting, updating, deleting, and erasure. Based on the current situation of “cold storage” technology, the reasons why DNA information storage media is difficult to achieve “hot storage” applications has been summarized. In that context, the data processing including destruction, encryption, rewriting, regeneration, decay, recovery, and arithmetic recording can be realized. The feasibility of using DNA media as an information processing carrier has been comprehensively demonstrated, and corresponding advantages and disadvantages were emphasized. This review aims to highlight the importance of DNA storage technology with the potential low energy consumption, high accuracy, high efficiency, and high security. Additionally, it will show the perspective regarding the integration of DNA characteristics for the next generation of intelligent information storage and processing systems.

    • YIN Xiaohe, ZHANG Shuying, ZHANG Ruifeng, SHANG Linchun, LI Lingjun

      2024,13(3):39-53, DOI: 10.12146/j.issn.2095-3135.20231031001


      At present, traditional storage technologies mainly use silicon-based materials as storage media, but the existing silicon resources in the world cannot meet the growing data storage needs of data. With the development of the data era, innovation in storage technology has faced challenges. DNA molecules store rich genetic information, and from the perspective of chemical biology, DNA molecules can be used as a medium for data information storage. This provides a new opportunity for storage technology. Unnatural base nucleotides can expand the genetic alphabet and increase the storage capacity, but there are still many issues to be resolved in their practical applications. This article reviews the progress of DNA storage technology, analysing the current state of DNA storage, unresolved technical challenges, and development prospects. Furthermore, it introduces unnatural base pairs (UBPs) as a new direction in synthetic biology, highlighting their potential advantages and technical challenges in the field of DNA information storage.

    • LIN Yisheng, WU Ruijun, QIAN Long, ZHANG Cheng

      2024,13(3):54-73, DOI: 10.12146/j.issn.2095-3135.20231030003


      In the era of data explosion, the global demand for data storage has far exceeded the existing storage capacity. DNA, as a natural carrier of genetic information, provides a stable, efficient, and sustainable data storage solution. The current process of DNA storage is divided into six main parts: encoding, writing, preservation, access, reading, and decoding; and nanopore sequencing technology has been widely used to read information stored in DNA. This review systematically introduces the principle and research history of nanopore-based DNA signal detection, and the applications of nanopore sequencing in DNA storage. Moreover, it summarizes the applications of machine learning in nanopore detection, particularly highlighting the integration of novel nanopore detection techniques with machine learning. This review presents a new direction for the development of nanopore technology and lays the foundation for building a better practical DNA storage system.

    • LIU Deruilin, SHEN Yue, PING Zhi

      2024,13(3):74-88, DOI: 10.12146/j.issn.2095-3135.20231030002


      With the advancement of modern science and technology, the volume of data generated through human production and daily life has surged. Traditional silicon-based storage media such as hard disks and flash memory are gradually becoming inadequate to meet the growing global data storage demands. Due to its remarkable advantages, such as incredibly high storage density, extremely long-term storage capabilities, and minimal energy consumption, DNA is regarded as the ideal next-generation storage medium. This review primarily focuses on DNA storage technology. It introduces the fundamental theory and workflow, and subsequently provides an overview of the research status regarding data security in DNA storage technology, within the context of conventional data security. This includes aspects like data encryption, data resilience, the detection of bio-cyber attacks and so on. The article also discusses the challenges and emerging trends in data security of DNA storage technology. Through interdisciplinary development, DNA storage technology is expected to resolve the conflict between massive data and limited conventional storage space and can be commercialized in multiple scenarios eventually, all while ensuring broad-spectrum and multi-tier data security.

    • XIONG Chenghe, LIU Xia, GAO Luna, HUANG Xiaoluo, MEI Hui

      2024,13(3):89-101, DOI: 10.12146/j.issn.2095-3135.20231107001


      The growing contradiction between the exponential increase in data amount and the limited storage capacity of existing media is becoming increasingly evident, necessitating the development of new types of media to address this issue. Due to its ultra-high density, low energy consumption and long lifetime for data storage, DNA has attracted much attention as an emerging storage medium, particularly for massive “cold data”, with the potential to replace current storage methods. In the process of data storage, the effective preservation of DNA plays a crucial role, directly impacting the DNA data of storage density, stability, storage time, as well as data writing and reading. Due to the limited information available on DNA preservation techniques in the current literature, this paper provides an overview of current research progress and strategies in DNA preservation technology for data storage, discusses the difficulties and challenges faced when applying existing preservation techniques in DNA data storage, and presents prospects for the implementation of DNA data storage.

    • WANG Yu, XU Jiaxin, LIN Mingxiang, CUI Junting, DAI Junbiao, WANG Yang, HUANG Xiaoluo

      2024,13(3):102-115, DOI: 10.12146/j.issn.2095-3135.20231120001


      Over the past few decades, the rapid development and widespread adoption of internet technology have propelled humanity into the digital information age, the internet has evolved into a crucial component of human life. With the emergence of the digital lifestyle, individuals are continuously generating massive amounts of digital information. Effective and convenient storage of this information is regarded as a significant challenge that needs to be overcome. Starting with introducing the existing storage methods and media, this article analyzes the current state of the storage field, followed by delving into the advantages, core technologies, and the potential applications of DNA as a big data storage medium in the coming days. Furthermore, this report proposes the future development trends and gives insights into DNA-based information storage, with aiming to offer new thoughts for the advancement of DNA-based data storage technology.

    • CHU Likang, HE Lei, HAN Da

      2024,13(3):116-127, DOI: 10.12146/j.issn.2095-3135.20231027001


      With the exponential growth of global data, the current information storage technologies are facing numerous drawbacks such as high maintenance costs and limited storage lifespan, which are gradually becoming more apparent in their inability to meet the increasing demands. Therefore, there is an urgent need to develop new information storage methods to address this issue. DNA, as a natural genetic information carrier, possesses advantages such as high storage density, potential low maintenance costs, and long lifespan, making it a potential new information storage medium. The aim of this work is to present an overview of the basic principles and processes of DNA data storage technology, along with a review of its historical development. Furthermore, the challenges that the field of DNA-based storage currently faces, such as slow data write and read speeds, as well as the potential solutions to these challenges, are summarized. Lastly, to fulfill the global demand for innovative storage solutions, the future directions for the DNA data storage technology were summarized.

    • SHI Yue, JIA Lijia, LIU Di

      2024,13(3):128-142, DOI: 10.12146/j.issn.2095-3135.20231128002


      Since the human civilization entered the information age, an exponential growth of digital information globally posed great challenges to data storage. Current data storage devices have many defects, such as limited data density, short lifespan, environment pollution and so on. Deoxyribonucleic acid (DNA), the natural carrier of genetic information, was proposed to be a reasonable alternative due to its high information density, robustness, long half-life and low maintenance cost. Although DNA storage currently faces the challenges of high reading and writing costs, slow speed and high error rate, it has unique advantages in many fields, such as long-time archival storage, military data encryption and so on. The potential future directions of DNA storage mainly include applications under special scenarios such as space and military, encoding-decoding algorithms robust to base errors, in vivo DNA storage, information retrieval without sequencing, and integrated DNA storage system as well as a unified evaluation standard. It is hoped that in the future, DNA storage can achieve large-scale application, and open a new era of data storage.

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    The "In Press" section displays the articles officially accepted after peer review. These articles are currently under copyediting process without volume/issue information, but are citable according to their Digital Object Identifiers(DOI).

    • xu tao,wang shun cheng,zhong jian wen,liu da bo,zhou yi longn,liu chang

      Doi: 10.12146/j.issn.2095-3135.20240307001


      Adenoid hypertrophy (AH) is a key contributor to pediatric obstructive sleep apnea syndrome (OSAS). Physicians rely on nasopharyngeal endoscopy to identify AH and the obstruction of adenoid to the airway. However, due to the limitations of 2D endoscope images, physicians have to infer the 3D structure of the adenoid region, which heavily relies on their expertise and the angle at which the adenoids are observed. The adenoid area is composed of mucosal tissue covered by nasal secretions, which may cause strong reflectivity, sparse features, smooth scenes, and blurred images. Based on these unique characteristics of the adenoids, this paper introduces a multi-view stereo algorithm based on endoscopic image sequences of the adenoid nasopharyngeal cavity. The algorithm employs multi-view stereo to first estimate a depth map corresponding to the images. Subsequently, it utilizes mesh surfaces to fit the rough depth information in the depth space, resulting in smooth and refined depth maps. This leads to a dense and precise reconstruction of the adenoid region. Both synthetic and real experimental results demonstrate that the algorithm can achieve accurate, dense, and smooth reconstruction of the adenoid area, surpassing the existing reconstruction algorithms significantly.

    • Xie Zhijun,Zhao Canming,Ke Xin,Xiao Yang,Wu Jing,Song Jialei

      Doi: 10.12146/j.issn.2095-3135.20240312002


      This paper presents a design of single-joint biomimetic robotic fish with compact structure and high swimming efficiency. It allows for convenient disassembly and assembly of pectoral fins, pelvic fins, and caudal fins. The influence of pectoral and pelvic fins on swimming performance was studied via underwater experiments. In the prototype swimming tests, a "binocular vision system" for tracking and recording the motion of the robotic fish was constructed using a high-speed camera and a flat mirror. It enabled tracking and recording of the three-dimensional position information of two marked points on the foremost end of the fish head and above its center of mass. This system provided data support for the quantitative analysis of the swimming performance, posture changes, and head stability of the robotic fish. The results indicated that the robotic fish have good performance in linear propulsion and turning. In the stability experiments, the head stability of the robotic fish equipped with pectoral fins and pelvic fins is better during low-frequency swimming. But no advantage is shown during high-frequency swimming, which is consistent with the phenomenon of various fins of fish in the natural environment being close to the body during high-frequency swimming except for the caudal fin.

    • Liang Zhanxiong,Sun Xudong,Cai Yonda,Zhang Yuming,Mai Langjie,He Yulin,Huang Zhexue

      Doi: 10.12146/j.issn.2095-3135.20240224001


      LOGO is a new distributed computing framework using a Non-MapReduce computing paradigm. Under the LOGO framework, big data distributed computing is completed in two steps. The LO operation runs a serial algorithm in a number of nodes or virtual machines to process independently the random sample data blocks, generating local results. The GO operation uploads all local results to the master node and integrate them to obtain the approximate result of the big data set. The LOGO computing framework eliminates data communication between nodes during iterations of the algorithm, greatly improving computing efficiency, reducing memory requirements, and enhancing data scalability. This article proposes a new distributed machine learning algorithm library RSP-LOGOML under the LOGO computing framework. A new distributed computing is divided into two parts: the serial algorithm executed by the LO operation and the ensemble algorithm executed in the GO operation. The LO operation can directly execute existing serial machine learning algorithms without the need to rewrite them according to MapReduce. The GO operation executes ensemble algorithms of different kinds depending on the ensemble tasks. In this article, the principle of LOGO distributed computing is introduced first, followed by the algorithm library structure, the method for packaging existing serial algorithms and the ensemble strategy. Finally, implementation in Spark, App development, and the results of performance tests for various algorithms are demonstrated.

    • Chetali Gurung,Aamir Nawaz,Dr. U Anjaneyulu,Pei-Gen Ren

      Doi: 10.12146/j.issn.2095-3135.20231206002


      The ability to mimic the microenvironment of the human body through fabrication of scaffolds itself a great achievement in the biomedical field. However, the search for the ideal scaffold is still in its infant stage and there are significant challenges to overcome. In the modern era, scientific communities are more attracted to natural substances due to their excess biological ability, low cost, biodegradability, and lesser toxic than synthetic lab made products. Chitosan is a well-known polysaccharide that has recently grabbed high amount of attention for its biological activities, especially in 3D bone tissue engineering (BTE). Chitosan greatly matches with the native tissues and thus stands out as a popular candidate for bioprinting. This review focuses on the potential of chitosan based scaffolds advancement and the drawbacks in bone treatment. Chitosan-based nanocomposites have exhibited strong mechanical strength, water-trapping ability, cellular interaction, and biodegradability characteristics. Chitosan derivatives have also encouraged and provided different routes of treatment and enhanced biological activities. 3D tailored bioprinting have opened new doors to design and manufacture scaffolds of biological, mechanical, and topographical properties.

    • chenwenxiong,lilele,yuzhibin

      Doi: 10.12146/j.issn.2095-3135.20240307002


      In today''s digital age, Nginx has emerged as the most prevalent web application server on Linux systems, securing the top position in market share. Given its critical role in ensuring the quality of service for users, optimizing the performance of Nginx servers is important. Despite the widespread deployment of Nginx servers across the two main hardware architectures, X86 and ARM, a comparative analysis of performance tuning on these architectures remains unexplored. This study aims to bridge this gap by employing automatic system parameter tuning on Nginx across these architectures, revealing the significant difference. When handling dynamic requests, the optimized performance of Nginx on X86 architecture significantly outperforms that of the ARM architecture. As a result, the optimized performance of Nginx on X86 architecture achieves a P99 latency of 515 milliseconds, which is performance improvement of 287% than that of the ARM architecture. Conversely, when processing static requests, the ARM architecture demonstrates superior performance, with a P99 latency of 220 milliseconds, resulting in a performance increase of 60% than that of X86 architecture. These findings highlight the distinct advantages of X86 and ARM architectures in handling different types of loads. It shows the significant impact of hardware architecture on optimizing Nginx’s performance. Therefore, to optimize the performance of Nginx web server, system administrators must consider the performance differences between static and dynamic requests of Nginx and the unique iterative efficiency over different hardware architectures.

    • PENG Mingxing,ZHAO Qilong,DU Xuemin

      Doi: 10.12146/j.issn.2095-3135.20230925001


      Cardiovascular disease is one of leading threats to human life and health. Tissue-engineered vascular scaffolds that can assist the regeneration/repair of disordered vessels have provided promising alternatives for cardiovascular disease treatment. However, existing tissue-engineered vascular scaffolds still confront grand challenges in interfacial adaptations, resulting in high risks of complications upon implantation and unsatisfactory translational application. Recently, tissue-engineered vascular scaffolds capable of programmed deformation have been emerging. Such scaffolds can not only dynamically adapt to three-dimensional vascular shapes with varying diameters but also orderly regulate behaviors and functions of vascular cells, offering new opportunities for addressing the grand challenges of interfacial adaptations. An overview of most-updated advances and perspectives of programmed deformed scaffolds for vascular tissue engineering will provide valuable inspirations to the development and translational applications of new generation of tissue-engineered vascular scaffolds.

    • He Xiaoxi,Cai Yunpeng

      Doi: 10.12146/j.issn.2095-3135.20240312001


      Artificial intelligence interpretability refers to the ability of people to understand and interpret the decision-making process of machine learning models. Research in this field aims to improve the transparency of machine learning algorithms, making their decisions more trustworthy and explainable. Interpretability is crucial in artificial intelligence systems, especially in sensitive and critical decision-making domains such as healthcare, finance, and law. By providing interpretability, people can better understand the reasoning behind the model''s decisions, ensuring that they are fair, robust, and ethical. In the continuously evolving field of artificial intelligence, enhancing the interpretability of models is a key step towards achieving trustworthy and sustainable AI. The article outlines the development history of interpretable artificial intelligence and the technical characteristics of various interpretability methods, with a particular focus on interpretability in the medical field. It provides a more in-depth discussion of the limitations of current methods on medical imaging datasets and proposes possible future directions for exploration.

    • Huang Jianxi,Liao Tongxin,Yu Zhuoyi,Wu Ruonan,Lu Min

      Doi: 10.12146/j.issn.2095-3135.20230703002


      As food plays an important role in people''s daily lives, a food map showing the geographical distribution of restaurants in a city is of great social value. Social media has covered every aspect of people''s lives; therefore, social media data provides a wealth of data to support automatic cartography. This work proposes an automatic generation method for urban food maps driven by social media data, integrating machine learning and cartographic algorithms to realize the intelligent generation of stylized urban food maps. A visualization system of urban food maps has been developed, which is applied to four cities, Wuhan, Guangzhou, Chongqing, and Chengdu, for case studies. The results show the effectiveness and good visual expressiveness of our method in presenting urban cuisine for cities.

    • Chen Jiang,Zhu Honglin,Meng Jintao,WEI Yanjie

      Doi: 10.12146/j.issn.2095-3135.20240202001


      Convolutional Neural Networks (CNNs), as a quintessential representation of deep learning, are the most commonly used neural networks in tasks such as computer vision. However, convolution operations typically account for over 90% of the runtime in CNNs, becoming a bottleneck for performance. Additionally, due to the complexity of current hardware and the diversity of workloads, specific optimizations in previous work often lack performance portability. To address this, we introduce BlazerML, an open-source convolution computation library based on auto-generated code templates from TVM, capable of automatically generating high-performance convolution implementations for any input shape. BlazerML is implemented based on the Winograd algorithm, known for its high performance in fast convolution algorithms. Experimental results demonstrate that BlazerML significantly outperforms current state-of-the-art open-source libraries. On x86 CPUs, running common deep learning network forward inferences, it is faster by 1.18~2.47, 1.18~2.27, and 1.01~1.66 times compared to OnnxRuntime, MNN, and the TVM community version, respectively. On ARM CPUs, for single-layer inference of common deep learning networks, it surpasses ACL and FastConv by 1.26~6.11 and 1.04~4.28 times, respectively.

    • LIU Tianhang,Yang Xiaoxue,ZHOU Hui,ZHAO Zhongying

      Doi: 10.12146/j.issn.2095-3135.20230731001


      Recommendation systems can effectively address the problem of information overload, attracting extensive attention from both academia and industry. Collaborative filtering algorithms based on graph neural networks have emerged as a widely adopted technique in recent years. These algorithms can effectively represent user and item features and learn intricate relationships between users and items. Therefore, they have become prevalent in the field of recommendation systems. In this paper, we first categorize the algorithms based on the problems that they aim to solve and then provides a comparison and analysis of representative algorithms within each category. We also summarize commonly used datasets in experiments and briefly introduce the key evaluation metrics. Finally, we discuss the challenges and potential research directions.

    • QinglinXu,Yu Qiao,Yali Wang

      Doi: 10.12146/j.issn.2095-3135.20231225001


      Action recognition in the dark is a challenging task in practice because it is difficult to learn robust action representations from low light environments. Furthermore, there is a domain gap between dark scenes and the data used by traditional pretrained models, which results in suboptimal results with the traditional pretrain-finetune approach, and pretraining from scratch is costly. To address this issue, a domain-adaptive pretraining method is proposed to improve action recognition performance in the dark environments. The method integrates an external vision enhancement model for de-darkening to introduce critical knowledge for dark scene processing. It also employs a cross-domain self-distillation framework to reduce the domain gap of visual representations between illuminated and dark scenes. Through extensive experiments in various dark environment action recognition settings, the proposed approach can achieve a Top-1 accuracy of 97.19% on the dark dataset of fully supervised action recognition. In the source-free domain adaptation on the Daily-DA dataset, the accuracy can be improved to 49.11%. In the multi-source domain adaptation scenario on the Daily-DA dataset, the Top-1 accuracy can reach 54.63%.

    • QinglinXu,Yu Qiao,Yali Wang

      Doi: 10.12146/j.issn.2095-3135.20231226001


      Effectively transferring knowledge from pre-trained models to downstream video understanding tasks is an important topic in computer vision research. Knowledge transfer becomes more challenging in open world due to poor data conditions. Many recent multimodal pre-training models are inspired by natural language processing and perform transfer learning by designing prompt learning. In this paper, we propose an LLM-powered domain context-assisted open-world action recognition method that leverages the open-world understanding capabilities of large language models. Our approach aligns visual representation with multi-level descriptions of human actions for robust classification, by enriching action labels with contextual knowledge in large language model. In the experiments of open-world action recognition with fully supervised setting, we obtain a Top-1 accuracy of 71.86% on the ARID dataset, and an mAP of 80.93% on the Tiny-VARIT dataset. More important, our method can achieve Top-1 accuracy of 48.63% in source-free video domain adaptation and 54.36% in multi-source video domain adaptation.

    • Chen Jiawen,CHEN Jinrong,CHEN Xing,MONG Yuchang

      Doi: 10.12146/j.issn.2095-3135.20230921001


      The absence of unified standards among smart device brands hinders collaborative management, as it requires dealing with different interfaces and communication protocols of each device, thus complicating the implementation of smart home service management. Moreover, the personalized differences due to lifestyle habits, climate conditions, and other factors also make it difficult for pre-set management rules to meet various requirements. To address these challenges, a spatial-temporal data-driven method for smart home service management is studied in this work. The method involves constructing a temporal knowledge graph for smart homes and utilizing a federated learning-based approach for smart home service management. By recording the state of concept instances in smart home scenarios, the temporal knowledge graph provides temporal data for environmental changes and service statuses. Through federated learning algorithms that incorporate model parameters from different households, personalized model updates and predictions of smart home service statuses are achieved. Experimental results showed that this method can effectively manage smart home devices, accurately meet user demands with satisfied accuracy and rate of convergence.

    • CEN Mengjia,ZHAO Xueqian,LI Ye,KONG Delai,LIU Yanjun

      Doi: 10.12146/j.issn.2095-3135.20231226002


      Metasurfaces are composed of sub-wavelength scale artificial nanostructures, which enhance the light-matter interaction at resonant wavelengths and improve the signal intensity from biochemical molecules in the near field. The chiral plasmonic metasurfaces can robustly and effectively enhance the chiral signal of chiral molecules and achieve the detection of trace molecules. The biochemical detection technology based on chiral plasmonic metasurfaces is increasingly studied to meet the requirements of high resolution and high sensitivity of detection devices in many fields such as biology, chemistry and environment. This review presented mechanism of biochemical detection of chiral plasmonic metasurfaces and the advances in chiral plasmonic metasurface. In terms of biochemical detection, the recent applications of chiral plasmonic metasurfaces in environmental media sensing, chirality detection, fluorescence detection and surface-enhanced Raman detection were introduced. Finally, the review discussed the application prospect of chiral plasmonic metasurfaces in biochemical detection field.

    • NIU Hongnan,WANG Jianming,HUANG Jieping,JI Jing

      Doi: 10.12146/j.issn.2095-3135.20231221001


      In this work, microporous membranes were prepared using polyvinylidene fluoride (PVDF) and dimethylacetamide as the solvent via non-solvent induced phase separation technique. The effects of dope compositions (i.e., various additives) and phase change conditions (i.e., evaporation time and relative humidity) on the membrane structure and performance were systematically investigated. It was found that hydrophobic PVDF membranes with large pore size and high permeance could be obtained by adding 16 wt.% isopropanol and 6 wt.% glycerol in the dope solution, followed by evaporation for 4 min at RH 80% before being immersed in water. The membrane possessed a completely open surface and bicontinuous interconnected structure, which contribute to little resistance in the filtration processes. The prepared PVDF membranes showed high pure water flux of 8650.74 LMH after pre-wetting and 200nm-polystyrene-microspheres rejection of more than 99%. In addition, the membrane was hydrophobic with water contact angle of 122°, which make the membrane as a promising candidate used for gas sterilization in the bio-pharmaceutical manufacturing process.

    • Ouyang Jie,Feng Song,Hou Linjun,Guo Shaokai,Li Haojie,Hu Xiangjian,Wang Di,Chen Menglin,Liu Yong,Feng Lulu

      Doi: 10.12146/j.issn.2095-3135.20230606002


      Aiming at the performance problems of photodetectors in optical communication, remote sensing and infrared thermal imaging, the research progress of photodetectors in the near-infrared band at home and abroad was discussed. Compared with traditional compound semiconductor materials, new materials such as silicon-based, graphene, tellurium compounds, transition metal dihalogenated compounds and perovskites have unique structures and properties, and are important materials for the preparation of low-power and high-performance photodetectors. This paper mainly expounds the research progress of silicon-based near-infrared photodetectors based on PN and PiN heterojunction structures, introduces the research progress of near-infrared photodetectors based on two-dimensional materials (graphene, tellurium compounds, transition metal dihalogenated compounds) and perovskite materials, and analyzes and compares the performance parameters of related near-infrared photodetectors, which provides ideas for the subsequent research of high-performance near-infrared photodetectors.

    • Wang Weijun,Xu Chuan,Huang Chen,Wang Jian,Ye Yuping

      Doi: 10.12146/j.issn.2095-3135.20230727001


      The forged wear-resistant steel balls in production often exhibit poor roundness and burrs, which significantly affect their grinding performance. To solve this problem, an industrial vision inspection method and system is proposed. Roundness of the ball is calculated by the maximum difference between the distances from the ball""s center to its contour. For the default of burr detection, a deep learning detection model is employed. Certain rules to distinguish burrs from the complex textures of the background regions are regulated, which enables the model to be trained effectively. Through analysis of burr features, it is found that burrs often appear as protrusions at the contours and exhibit stripe patterns in terms of brightness and slope. Additionally, capturing images of the high-temperature steel balls using digital filtering imaging effectively removes thermal radiation noise and obtains clear ball images. These images are applied to the YOLOv5 instance segmentation model, resulting in a burr detection rate of 95.3%.

    • Yan Yuling,Zhang Min,Jiu Xiuqin,Liu Jianbo

      Doi: 10.12146/j.issn.2095-3135.20231124001


      The construction of artificial cells with specific cell mimic functions helps to explore the complex biological reaction processes and cellular functions in natural biological cell systems, and provides convenience for the in-depth understanding of the origin of life. Artificial cell construction method, based on the top-down and bottom-up principle, in the past few decades have made great progress and extensive application. Build strategy based on artificial cells, human cells can be divided into "top-down" artificial cells and "bottom-up" cells. Bottom-up complementary branch of synthetic biology is a new, it sought from natural or synthetic ingredients to build artificial cells. One of the goals of bottom-up synthetic biology is to construct or mimic the complex pathways present in the cells of natural organisms. Artificial cells derived from lipids, polymer, lipid/polymer hybrid body, natural cell membrane, metal - organic frameworks and condensed matter and so on. With the real human cells can be various substances in the cell such as proteins, genes, such as mitochondria in the surface or wrapped in internal and are endowed with various functions. Moreover, artificial cells can be used as a drug delivery system and a carrier of information exchange. In addition, artificial cells can also replace the damaged cells to restore the normal operation of the body. Here, we first introduced the method based on bottom-up strategy to build artificial cells and classification; Then the various applications of artificial cells are discussed. Finally, the future development of artificial cells is prospected.

    • Han Chen,Meng Hongmin,Li Zhaohui

      Doi: 10.12146/j.issn.2095-3135.20231122001


      Organic afterglow material is an optical material possessing afterglow properties, and can emit light for an extended period following excitation, thereby increase imaging duration and sensitivity. Organic afterglow materials are commonly utilized in biological analysis due to their flexible design and good biocompatibility. Owing to the light-excitation-free luminescence, the afterglow luminescence can circumvent the interference of tissue auto-fluorescence, and provide higher signal-to-background ratio and sensitivity superior to fluorescence. This review provides an overview of the recent advances in organic afterglow materials, which comprehensively summarizes the reported medical applications of these materials in bioanalysis and afterglow imaging. Finally, the article discusses the prospects and challenges of using organic afterglow materials in molecular construction and clinical imaging.

    • Sun Yi,Wu Siman,Fang Wei,Wu Shuangqing,HU Chao

      Doi: 10.12146/j.issn.2095-3135.20231108001


      With the regulations of wearing helmets while driving the electric bicycle, it is urgent to develop a detection algorithm that can accurately detect whether the drivers are wearing helmets. This paper introduces a novel method to detect the helmets based on the YOLO framework. The branch absorption module is proposed to improve the residual backbone network, then the feature fusion is improved through the channel recombination. Finally, the designed structural fusion pruning is applied to further compress the hyper-parameters of the model. The experimental results showed that, the proposed algorithm has higher accuracy and faster speed. Performance of small targets detection also can be improved, with the average accuracy of multiple classification up to 88.8% and detection speed of 29.5fps, which can meet the demand of video surveillance in real applications.

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      • XIONG Gang,MENG Jiao,CAO Zi-gang,WANG Yong,GUO Li,FANG Bin-xing

        2012,1(1):32-42, Doi: 10.12146/j.issn.2095-3135.201205006


        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.

      • YAN Xiaoqing,CHEN Hongyun,WU Binbin,LIU Chunhua,LIANG Yan

        2015,4(4):87-93, Doi: 10.12146/j.issn.2095-3135.201504010


        The human gut is densely populated by the gut microbiota. There are accumulating evidences indicating that the gut microbiota plays a significant role in the function of the body, which including the metabolism and energy absorption, the development in the function of gastrointestinal, the modulation of immune system and so on. Many chronic diseases, such as obesity, obesity-associated inflammation, inflammatory bowel disease and depression, are related to gut microbiota dysbiosis. The research of the interaction between intestinal bacteria and human body is instructive to the prevention or treatment of many chronic diseases and maintaining health.

      • GE Ruiquan, WANG Pu, LI Ye,CAI Yunpeng

        2017,6(5):55-68, Doi: 10.12146/j.issn.2095-3135.201705006


        Repetitive sequences are prevalent in genomes. A large number of experiments have confirmed that they play an important role in biological evolution. At present, the discovery and detection of the repetitive sequences have been becoming a hot topic of genomics. This paper summarizes the research progress in this regard, and briefly analyses the associated tools. Finally, the development of repetitive sequences in future is prospected.

      • SONG Zhang-jun

        2012,1(3):1-9, Doi: 10.12146/j.issn.2095-3135.201209001


        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.

      • ZHANG Wen-li

        2012,1(3):20-24, Doi: 10.12146/j.issn.2095-3135.201209004


        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.

      • LIU Qun

        2012,1(1):48-54, Doi: 10.12146/j.issn.2095-3135.201205008


        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.

      • XIA Wei,LI Huiyun

        2017,6(3):29-40, Doi: 10.12146/j.issn.2095-3135.201703003


        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.

      • XU Guo-qing,XU Kun,LI Wei-min

        2012,1(1):6-14, Doi: 10.12146/j.issn.2095-3135.201205002


        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.

      • MA Xiao-yan,HONG Jue

        2012,1(3):66-71, Doi: 10.12146/j.issn.2095-3135.201209012


        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.

      • ZHOU Wu,XIE Yaoqin,TIAN Yangyang

        2014,3(1):68-76, Doi: 10.12146/j.issn.2095-3135.201401007


        The accurate contour delineation of the target and organs at risk (OAR) is essential in treatment planning for image guided radiation therapy. In clinical applications, the contour delineation is often done manually by clinicians, which may be accurate, but time-consuming and tedious for users. Although a lot of automatic contour delineation approaches have been proposed, few of them can fulfill the necessities of applications in terms of accuracy and efficiency. In this work, a novel approach of target delineation was proposed. Target delineation of OARs was achieved by using snake model and multiscale curve editing to obtain promising results. It allows users to quickly improve contours by a simple mouse click. Experimental results demonstrate the effectiveness of the proposed method for clinical target delineations.

      • SHEN Yang,LING Tao,YAO Hui,LI Yan-ming,JIN Qiao-feng,ZHENG Hai-rong

        2012,1(1):93-99, Doi: 10.12146/j.issn.2095-3135.201205015


        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.

      • GAO Ming,HUANG Zhe-xue

        2012,1(3):47-54, Doi: 10.12146/j.issn.2095-3135.201209009


        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.

      • HU Chao,SONG Shuang,YANG Wan-an,MENG Qing-hu,LI Bao-pu,ZENG De-wen,LI Xiao-xiao,ZHU Hong-mei

        2012,1(1):105-113, Doi: 10.12146/j.issn.2095-3135.201205017


        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.

      • WANG Hui

        2013,2(4):49-55, Doi: 10.12146/j.issn.2095-3135.201307009


        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.

      • LUO Li,YANG Chao,ZHAO Yu-bo,CAI Xiao-chuan

        2012,1(1):84-88, Doi: 10.12146/j.issn.2095-3135.201205013


        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.

      • ZHANG Hao-shi,WU Zhen-xing,TIAN Lan,YANG Lin,LI Guang-lin

        2012,1(1):114-118, Doi: 10.12146/j.issn.2095-3135.201205018


        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.

      • CHI Xue-bin,XIAO Hai-li,WANG Xiao-ning,CAO Rong-qiang,LU Sha-sha,ZHANG Hong-hai

        2012,1(1):68-76, Doi: 10.12146/j.issn.2095-3135.201205011


        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.

      • ZHAO Wen-chuang,CHENG Jun

        2012,1(3):10-14, Doi: 10.12146/j.issn.2095-3135.201209002


        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.

      • WU Zhen,ZHOU Hui,LI Guang-lin

        2013,2(4):56-60, Doi: 10.12146/j.issn.2095-3135.201307010


        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.

      • XU Tian-chen,WU En-hua

        2012,1(1):77-83, Doi: 10.12146/j.issn.2095-3135.201205012


        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.

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      • Dong Shishi, Huang Zhexue

        2012,2(1):1-7, Doi:


        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.

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      • Zheng Hongna, Zhu Yun, Wang LAN, Chen hui

        2015,2(1):23-28, Doi:


        In order to help the hearing loss children, we obtained hearing loss children’s fallible pronunciation texts and the confusing pronunciation text pairs form a good deal of hearing loss children’s audio pronunciation data. We designed a data-driven 3D talking head articulatory animation system, it was driven by the articulatory movements which were collected from a device called Electro-magnetic articulography (EMA) AG500, the system simulated Chinese articulation realistically. In that way, the hearing loss children can observe the speaker’s lips and tongue’s motions during the speaker pronouncing, which could help the hearing loss children train pronunciation and correct the fallible pronunciations. Finally, a perception test was applied to evaluate the system’s performance. The results showed that the 3D talking head system can animate both internal and external articulatory motions effectively. A modified CM model based synthesis method was used to generate the articulatory movements. The root mean square between the real articulatory movements and synthetic articulatory movements was used to measure the synthesis method, and an average value of RMS is 1.25 mm.

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      • YU Xinjie,WU Xiongfei,Wang Jianping,CHEN Li,WANG Lei

        2017,3(5):45-51, Doi: 10.12146/j.issn.2095-3135.201405006


        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.

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      • LIU Hengwei,LI Jianjun,XIE Xiaoyi,FANG Mou,WANG Li,HE Xiangming,OUYANG Minggao,LI Maogang

        2012,4(1):51-59, Doi: 10.12146/j.issn.2095-3135.201501007


        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.

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