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| 1 | Human Cyber Physical Systems(HCPSs)in the Context of New-Generation Intelligent Manufacturing显示文摘An intelligent manufacturing system is a composite intelligent system comprising humans,cyber systems,and physical systems with the aim of achieving specific manufacturing goals at an optimized level.This kind of intelligent system is called a human-cyber-physical system(HCPS).In terms of technology,HCPSs can both reveal technological principles and form the technological architecture for intelligent manufacturing.It can be concluded that the essence of intelligent manufacturing is to design,construct,and apply HCPSs in various cases and at different levels.With advances in information technology,intelligent manufacturing has passed through the stages of digital manufacturing and digital-networked manufacturing,and is evolving toward new-generation intelligent manufacturing(NGIM).NGIM is characterized by the in-depth integration of new-generation artificial intelligence(AI)technology(i.e.,enabling technology)with advanced manufacturing technology(i.e.,root technology);it is the core driving force of the new industrial revolution.In this study,the evolutionary footprint of intelligent manufacturing is reviewed from the perspective of HCPSs,and the implications,characteristics,technical frame,and key technologies of HCPSs for NGIM are then discussed in depth.Finally,an outlook of the major challenges of HCPSs for NGIM is proposed. | Zhou Ji Zhou Yanhong Wang Baicun Zang Jiyuan | 2019 | Engineering2019，5，4： | 98 |
| 2 | 6G Visions:Mobile Ultra-Broadband,Super Internet-of-Things,and Artificial Intelligence显示文摘With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation(6G) mobile communications be on the eve of the fifth-generation(5G) deployment. To pave the way for the development of 6G and beyond, we provide 6G visions in this paper. We first introduce the state-of-the-art technologies in 5G and indicate the necessity to study 6G. By taking the current and emerging development of wireless communications into consideration, we envision 6G to include three major aspects, namely, mobile ultra-broadband, super Internet-of-Things(IoT), and artificial intelligence(AI). Then, we review key technologies to realize each aspect. In particular, teraherz(THz) communications can be used to support mobile ultra-broadband, symbiotic radio and satellite-assisted communications can be used to achieve super IoT, and machine learning techniques are promising candidates for AI. For each technology, we provide the basic principle, key challenges, and state-of-the-art approaches and solutions. | Lin Zhang Ying-Chang Liang Dusit Niyato | 2019 | China Communications2019，16，8： | 52 |
| 3 | A review:On path planning strategies for navigation of mobile robot显示文摘This paper presents the rigorous study of mobile robot navigation techniques used so far.The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap.The classical approaches such as cell decomposition(CD),roadmap approach(RA),artificial potential field(APF);reactive approaches such as genetic algorithm(GA),fuzzy logic(FL),neural network(NN),firefly algorithm(FA),particle swarm optimization(PSO),ant colony optimization(ACO),bacterial foraging optimization(BFO),artificial bee colony(ABC),cuckoo search(CS),shuffled frog leaping algorithm(SFLA)and other miscellaneous algorithms(OMA)are considered for study.The navigation over static and dynamic condition is analyzed(for single and multiple robot systems)and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches.It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm.Hence,reactive approaches are more popular and widely used for path planning of mobile robot.The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics. | B.K. Patle Ganesh Babu L Anish Pandey D.R.K. Parhi A. Jagadeesh | 2019 | Defence Technology(防务技术)2019，15，4： | 60 |
| 4 | Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring显示文摘Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering is presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges. | Billie F. Spencer Jr. Vedhus Hoskere Yasutaka Narazaki | 2019 | Engineering2019，5，2： | 44 |
| 5 | New development thoughts on the bio-inspired intelligence based control for unmanned combat aerial vehicle显示文摘Bio-inspired intelligence is in the spotlight in the field of international artificial intelligence,and unmanned combat aerial vehicle(UCAV),owing to its potential to perform dangerous,repetitive tasks in remote and hazardous,is very promising for the technological leadership of the nation and essential for improving the security of society.On the basis of introduction of bioinspired intelligence and UCAV,a series of new development thoughts on UCAV control are proposed,including artificial brain based high-level autonomous control for UCAV,swarm intelligence based cooperative control for multiple UCAVs,hy-brid swarm intelligence and Bayesian network based situation assessment under complicated combating environments, bio-inspired hardware based high-level autonomous control for UCAV,and meta-heuristic intelligence based heterogeneous cooperative control for multiple UCAVs and unmanned combat ground vehicles(UCGVs).The exact realization of the proposed new development thoughts can enhance the effectiveness of combat,while provide a series of novel breakthroughs for the intelligence,integration and advancement of future UCAV systems. | DUAN HaiBin 1 ,SHAO Shan 2 ,SU BingWei 3 &ZHANG Lei 41 State Key Laboratory of Science and Technology on Holistic Flight Control,School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics,Beijing 100191,China 2 Flight Control Department,Shenyang Aircraft Design and Research Institute,Shenyang 110035,China 3 Beijing Institute of Near Space Vehicle’s System Engineering,Beijing 100076,China 4Integration and Project Section,Air Force Equipment Academy,Beijing 100085,China | 2010 | Science China(Technological Sciences)2010，53，8： | 32 |
| 6 | Artificial intelligence in medical imaging of the liver显示文摘Artificial intelligence(AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians' workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques. | Li-Qiang Zhou Jia-Yu Wang Song-Yuan Yu Ge-Ge Wu Qi Wei You-Bin Deng Xing-Long Wu Xin-Wu Cui Christoph F Dietrich | 2019 | World Journal of Gastroenterology2019，25，6： | 21 |
| 7 | Functional gastrointestinal disorders and gut-brain axis: What does the future hold?显示文摘Despite their high prevalence, lack of understanding of the exact pathophysiology of the functional gastrointestinal disorders has restricted us to symptomatic diagnostic tools and therapies. Complex mechanisms underlying the disturbances in the bidirectional communication between the gastrointestinal tract and the brain have a vital role in the pathogenesis and are key to our understanding of the disease phenomenon. Although we have come a long way in our understanding of these complex disorders with the help of studies on animals especially rodents, there need to be more studies in humans, especially to identify the therapeutic targets. This review study looks at the anatomical features of the gut-brain axis in order to discuss the different factors and underlying molecular mechanisms that may have a role in the pathogenesis of functional gastrointestinal disorders. These molecules and their receptors can be targeted in future for further studies and possible therapeutic interventions. The article also discusses the potential role of artificial intelligence and machine learning and its possible role in our understanding of these scientifically challenging disorders. | Kashif Mukhtar Hasham Nawaz Shahab Abid | 2019 | World Journal of Gastroenterology2019，25，5： | 15 |
| 8 | Artificial intelligence in gastrointestinal endoscopy:The future is almost here显示文摘Artificial intelligence(AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy. The most promising of these efforts have been in computeraided detection and computer-aided diagnosis of colorectal polyps, with recent systems demonstrating high sensitivity and accuracy even when compared to expert human endoscopists. AI has also been utilized to identify gastrointestinal bleeding, to detect areas of inflammation, and even to diagnose certain gastrointestinal infections. Future work in the field should concentrate on creating seamless integration of AI systems with current endoscopy platforms and electronic medical records, developing training modules to teach clinicians how to use AI tools, and determining the best means for regulation and approval of new AI technology. | Muthuraman Alagappan Jeremy R Glissen Brown Yuichi Mori Tyler M Berzin | 2018 | World Journal of Gastrointestinal Endoscopy2018，10，10： | 14 |
| 9 | Artificial intelligence in breast ultrasound显示文摘Artificial intelligence(AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women,severely threatening women's health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound. | Ge-Ge Wu Li-Qiang Zhou Jian-Wei Xu Jia-Yu Wang Qi Wei You-Bin Deng Xin-Wu Cui Christoph F Dietrich | 2019 | World Journal of Radiology2019，11，2： | 12 |
| 10 | Application of artificial intelligence in gastroenterology显示文摘Artificial intelligence(AI) using deep-learning(DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis,prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias(class imbalance) have the possibility of overestimating the accuracy,external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification,prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability.Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed. | Young Joo Yang Chang Seok Bang | 2019 | World Journal of Gastroenterology2019，25，14： | 12 |
| 11 | Toward Intelligent Machine Tool显示文摘With the development of modern information technology-and particularly of the new generation of artificial intelligence(AI)technology-new opportunities are available for the development of the intelligent machine tool(IMT).Based on the three classical paradigms of intelligent manufacturing as defined by the Chinese Academy of Engineering,the concept,characteristics,and systemic structure of the IMT are presented in this paper.Three stages of machine tool evolution-from the manually operated machine tool(MOMT)to the IMT-are discussed,including the numerical control machine tool(NCMT),the smart machine tool(SMT),and the IMT.Furthermore,the four intelligent control principles of the IMT-namely,autonomous sensing and connection,autonomous learning and modeling,autonomous optimization and decision-making,and autonomous control and execution-are presented in detail.This paper then points out that the essential characteristic of the IMT is to acquire and accumulate knowledge through learning,and presents original key enabling technologies,including the instruction-domain-based analytical approach,theoretical and big-data-based hybrid modeling technology,and the double-code control method.Based on this research,an intelligent numerical control(INC)system and industrial prototypes of IMTs are developed.Three intelligent practices are conducted,demonstrating that the integration of the new generation of AI technology with advanced manufacturing technology is a feasible and convenient way to advance machine tools toward the IMT. | Jihong Chen Pengcheng Hu Huicheng Zhou Jianzhong Yang Jiejun Xie Yakun Jiang Zhiqiang Gao Chenglei Zhang | 2019 | Engineering2019，5，4： | 12 |
| 12 | Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology显示文摘It is essential to utilize deep-learning algorithms based on big data for the implementation of the new generation of artificial intelligence. Effective utilization of deep learning relies considerably on the number of labeled samples, which restricts the application of deep learning in an environment with a small sample size. In this paper, we propose an approach based on a generative adversarial network (GAN) combined with a deep neural network (DNN). First, the original samples were divided into a training set and a test set. The GAN was trained with the training set to generate synthetic sample data, which enlarged the training set. Next, the DNN classifier was trained with the synthetic samples. Finally, the classifier was tested with the test set, and the effectiveness of the approach for multi-classification with a small sample size was validated by the indicators. As an empirical case, the approach was then applied to identify the stages of cancers with a small labeled sample size. The experimental results verified that the proposed approach achieved a greater accuracy than traditional methods. This research was an attempt to transform the classical statistical machine-learning classification method based on original samples into a deep-learning classification method based on data augmentation. The use of this approach will contribute to an expansion of application scenarios for the new generation of artificial intelligence based on deep learning, and to an increase in application effectiveness. This research is also expected to contribute to the comprehensive promotion of new-generation artificial intelligence. | Yufei Liu Yuan Zhou Xin Liu Fang Dong Chang Wang Zihong Wang | 2019 | Engineering2019，5，1： | 11 |
| 13 | Parallel Building: A Complex System Approach for Smart Building Energy Management显示文摘These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i.e., artificial systems,computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper. | Abdulaziz Almalaq Jun Hao Jun Jason Zhang Fei-Yue Wang | 2019 | IEEE/CAA Journal of Automatica Sinica2019，6，6： | 10 |
| 14 | Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review显示文摘BACKGROUND Artificial intelligence,such as convolutional neural networks(CNNs),has been used in the interpretation of images and the diagnosis of hepatocellular cancer(HCC)and liver masses.CNN,a machine-learning algorithm similar to deep learning,has demonstrated its capability to recognise specific features that can detect pathological lesions.AIM To assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of CNNs and their performance.METHODS The databases PubMed,EMBASE,and the Web of Science and research books were systematically searched using related keywords.Studies analysing pathological anatomy,cellular,and radiological images on HCC or liver masses using CNNs were identified according to the study protocol to detect cancer,differentiating cancer from other lesions,or staging the lesion.The data were extracted as per a predefined extraction.The accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed.The primary outcomes of the study were analysing the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.RESULTS A total of 11 studies that met the selection criteria and were consistent with the aims of the study were identified.The studies demonstrated the ability to differentiate liver masses or differentiate HCC from other lesions(n=6),HCC from cirrhosis or development of new tumours(n=3),and HCC nuclei grading or segmentation(n=2).The CNNs showed satisfactory levels of accuracy.The studies aimed at detecting lesions(n=4),classification(n=5),and segmentation(n=2).Several methods were used to assess the accuracy of CNN models used.CONCLUSION The role of CNNs in analysing images and as tools in early detection of HCC or liver masses has been demonstrated in these studies.While a few limitations have been identified in these studies,overall there was an optimal level of accuracy of the CNNs used in segmentation and classification of liver cancers images. | Samy A Azer | 2019 | World Journal of Gastrointestinal Oncology2019，11，12： | 10 |
| 15 | Electronic Nose and Its Applications: A Survey显示文摘In the last two decades,improvements in materials,sensors and machine learning technologies have led to a rapid extension of electronic nose(EN)related research topics with diverse applications.The food and beverage industry,agriculture and forestry,medicine and health-care,indoor and outdoor monitoring,military and civilian security systems are the leading fields which take great advantage from the rapidity,stability,portability and compactness of ENs.Although the EN technology provides numerous benefits,further enhancements in both hardware and software components are necessary for utilizing ENs in practice.This paper provides an extensive survey of the EN technology and its wide range of application fields,through a comprehensive analysis of algorithms proposed in the literature,while exploiting related domains with possible future suggestions for this research topic. | Diclehan Karakaya Oguzhan Ulucan Mehmet Turkan | 2020 | International Journal of Automation and computing2020，17，2： | 9 |
| 16 | 健康和医学领域的人工智能:期许、伦理挑战和治理(英文)显示文摘Artificial intelligence (AI) is rapidly being applied to a wide range of fields,including medicine,and has been considered as an approach that may augment or substitute human professionals in primary healthcare.However,AI also raises several challenges and ethical concerns.In this article,the author investigates and discusses three aspects of AI in medicine and healthcare:the application and promises of AI,special ethical concerns pertaining to AI in some frontier fields,and suggestive ethical governance systems.Despite great potentials of frontier AI research and development in the field of medical care,the ethical challenges induced by its applications has put forward new requirements for governance.To ensure “trustworthy” AI applications in healthcare and medicine,the creation of an ethical global governance framework and system as well as special guidelines for frontier AI applications in medicine are suggested.The most important aspects include the roles of governments in ethical auditing and the responsibilities of stakeholders in the ethical governance system. | 关健 | 2019 | Chinese Medical Sciences Journal2019，34，2： | 9 |
| 17 | Update on thyroid ultrasound: a narrative review from diagnostic criteria to artificial intelligence techniques显示文摘Objective Ultrasound imaging is well known to play an important role in the detection of thyroid disease, but the management of thyroid ultrasound remains inconsistent. Both standardized diagnostic criteria and new ultrasound technologies are essential for improving the accuracy of thyroid ultrasound. This study reviewed the global guidelines of thyroid ultrasound and analyzed their common characteristics for basic clinical screening. Advances in the application of a combination of thyroid ultrasound and artificial intelligence (AI) were also presented. Data sources An extensive search of the PubMed database was undertaken, focusing on research published after 2001 with keywords including thyroid ultrasound, guideline, AI, segmentation, image classification, and deep learning. Study selection Several types of articles, including original studies and literature reviews, were identified and reviewed to summarize the importance of standardization and new technology in thyroid ultrasound diagnosis. Results Ultrasound has become an important diagnostic technique in thyroid nodules. Both standardized diagnostic criteria and new ultrasound technologies are essential for improving the accuracy of thyroid ultrasound. In the standardization, since there are no global consensus exists, common characteristics such as a multi-feature diagnosis, the performance of lymph nodes, explicit indications of fine needle aspiration, and the diagnosis of special populations should be focused on. Besides, evidence suggests that AI technique has a good effect on the unavoidable limitations of traditional ultrasound, and the combination of diagnostic criteria and AI may lead to a great promotion in thyroid diagnosis. Conclusion Standardization and development of novel techniques are key factors to improving thyroid ultrasound, and both should be considered in normal clinical use. | Xiao-Wen Liang Yong-Yi Cai Jin-Sui Yu Jian-Yi Liao Zhi-Yi Chen | 2019 | Chinese Medical Journal2019，，16： | 7 |
| 18 | 产学研用协作促进中国医学影像AI产业健康发展(英文)显示文摘In recent years,artificial intelligence (AI) has developed rapidly in the field of medical imaging.However,the collaborations among hospitals,research institutes and enterprises are insufficient at the present,and there are various issues in technological transformation and value landing of products in this area.To solve the core problems in the developmental path of medical imaging AI,the Chinese Innovative Alliance of Industry,Education,Research and Application of Artificial Intelligence for Medical Imaging compiled the White Paper on Medical Image AI in China.This article introduces the current status of collaboration,the clinical demands for medical imaging AI technique,and the key points in AI technology transformation:robustness,usability and security.We are facing challenges of lacking industry standards,data desensitization standard,assessment system,as well as corresponding regulations and policies to realize the application values of AI products in medical imaging.Further development of AI in medical imaging requires breakthroughs of the core algorithm,deep involvement of doctors,input from capitals,patience from societies,and most importantly,the resolutions from government for multiple difficulties in links of landing the technology. | 萧毅 刘士远 | 2019 | Chinese Medical Sciences Journal2019，34，2： | 7 |
| 19 | An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression显示文摘The identification of important nodes in a power grid has considerable benefits for safety. Power networks vary in many aspects, such as scale and structure. An index system can hardly cover all the information in various situations. Therefore, the efficiency of traditional methods using an index system is case-dependent and not universal. To solve this problem, an artificial intelligence based method is proposed for evaluating power grid node importance. First, using a network embedding approach, a feature extraction method is designed for power grid nodes, considering their structural and electrical information. Then, for a specific power network, steady-state and node fault transient simulations under various operation modes are performed to establish the sample set. The sample set can reflect the relationship between the node features and the corresponding importance. Finally, a support vector regression model is trained based on the optimized sample set for the later online use of importance evaluation. A case study demonstrates that the proposed method can effectively evaluate node importance for a power grid based on the information learned from the samples. Compared with traditional methods using an index system, the proposed method can avoid some possible bias. In addition, a particular sample set for each specific power network can be established under this artificial intelligence based framework, meeting the demand of universality. | Hui-fang WANG Chen-yu ZHANG Dong-yang LIN Ben-teng HE | 2019 | Frontiers of Information Technology & Electronic Engineering2019，20，6： | 6 |
| 20 | Computational neuroscience in China显示文摘The ultimate goal of Computational Neuroscience(CNS) is to use and develop mathematical models and approaches to elucidate brain functions.CNS is a young and highly multidisciplinary field.It heavily interacts with experimental neuroscience and such other research areas as artificial intelligence,robotics,computer vision,information science and machine learning.This paper reviews the history of CNS in China,its current status and the prospects for its future development.Examples of CNS research in China are also presented. | WU Si1 & LIANG PeiJi2 1Institute of Neuroscience,Shanghai Institutes for Biological Sciences,State Key Laboratory of Neurobiology,Chinese Academy of Sciences,Shanghai 200031,China 2Department of Biomedical Engineering,Shanghai Jiaotong University,Shanghai 200240,China | 2010 | Science China(Life Sciences)2010，53，3： | 6 |
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