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| 1 | TDD-net: a tiny defect detection network for printed circuit boards显示文摘Tiny defect detection (TDD) which aims to perform the quality control of printed circuit boards (PCBs) is a basic and essential task in the production of most electronic products. Though significant progress has been made in PCB defect detection, traditional methods are still difficult to cope with the complex and diverse PCBs. To deal with these problems, this article proposes a tiny defect detection network (TDD-Net) to improve performance for PCB defect detection. In this method, the inherent multi-scale and pyramidal hierarchies of deep convolutional networks are exploited to construct feature pyramids. Compared with existing approaches, the TDD-Net has three novel changes. First, reasonable anchors are designed by using k-means clustering. Second, TDD-Net strengthens the relationship of feature maps from different levels and benefits from low-level structural information, which is suitable for tiny defect detection. Finally, considering the small and imbalance dataset, online hard example mining is adopted in the whole training phase in order to improve the quality of region-of-interest (ROI) proposals and make more effective use of data information. Quantitative results on the PCB defect dataset show that the proposed method has better portability and can achieve 98.90% mAP, which outperforms the state-of-arts. The code will be publicly available. | Runwei Ding Linhui Dai Guangpeng Li Hong Liu | 2019 | CAAI Transactions on Intelligence Technology2019，4，2： | 27 |
| 2 | Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review显示文摘Background:An e-nose or an e-tongue is a group of gas sensors or chemical sensors that simulate human nose or human tongue.Both e-nose and e-tongue have showngreat promise and utility in improving assessments of food quality characteristics compared with traditional detection methods.Scope and approach:This review summarizes the application of e-nose and e-tongue in determining the quality-related properties of foods.The working principles,applications,and limitations of the sensors employed by electronic noses and electronic tongueswere introduced and compared.Widelyemployed pattern recognition algorithms,including artificial neural network(ANN),convolutional neural network(CNN),principal component analysis(PCA),partial least square regression(PLS),and support vector machine(SVM),were introduced and compared in this review.Key findings and conclusions:Overall,e-nose or e-tongue combining pattern recognition algorithms are very powerful analytical tools,which are relatively low-cost,rapid,and accurate.E-nose and e-tongue are also suitable for both in-line and off-line measurements,which are very useful in monitoring food processing and detecting the end product quality.The user of e-nose and e-tongue need to strictly control sample preparation,sampling,and data processing. | Juzhong Tan Jie Xu | 2020 | Artificial Intelligence in Agriculture2020，，1： | 26 |
| 3 | FAIR Principles:Interpretations and Implementation Considerations显示文摘The FAIR principles have been widely cited,endorsed and adopted by a broad range of stakeholders since their publication in 2016.By intention,the 15 FAIR guiding principles do not dictate specific technological implementations,but provide guidance for improving Findability,Accessibility,Interoperability and Reusability of digital resources.This has likely contributed to the broad adoption of the FAIR principles,because individual stakeholder communities can implement their own FAIR solutions.However,it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations.Thus,while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways,for true interoperability we need to support convergence in implementation choices that are widely accessible and(re)-usable.We introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible,robust,widespread and consistent FAIR implementations.Any self-identified stakeholder community may either choose to reuse solutions from existing implementations,or when they spot a gap,accept the challenge to create the needed solution,which,ideally,can be used again by other communities in the future.Here,we provide interpretations and implementation considerations(choices and challenges)for each FAIR principle. | Annika Jacobsen Ricardo de Miranda Azevedo Nick Juty Dominique Batista Simon Coles Ronald Cornet Melanie Courtot Merce Crosas Michel Dumontier Chris T.Evelo Carole Goble Giancarlo Guizzardi Karsten Kryger Hansen Ali Hasnain Kristina Hettne Jaap Heringa Rob W.W.Hooft Melanie Imming Keith G.Jeffery Rajaram Kaliyaperumal Martijn GKersloot Christine R.Kirkpatrick Tobias Kuhn Ignasi Labastida Barbara Magagna PeterMcQuilton Natalie Meyers Annalisa Montesanti Mirjam van Reisen Philippe Rocca-Serra Robert Pergl Susanna-Assunta Sansone Luiz Olavo Bonino da Silva Santos Juliane Schneider George Strawn Mark Thompson Andra Waagmeester Tobias Weigel Mark D.Wilkinson Egon L.Willighagen Peter Wittenburg Marco Roos Barend Mons Erik Schultes | 2020 | Data Intelligence2020，2，1： | 26 |
| 4 | ACP-based social computing and parallel intelligence: Societies 5.0 and beyond显示文摘 | XiaoWang Lingxi Li Yong Yuan Peijun Ye Fei-Yue Wang | 2016 | CAAI Transactions on Intelligence Technology2016，1，4： | 21 |
| 5 | Pigeon-inspired optimization:a new swarm intelligence optimizer for air robot path planning显示文摘Purpose–The purpose of this paper is to present a novel swarm intelligence optimizer—pigeoninspired optimization(PIO)—and describe how this algorithm was applied to solve air robot path planning problems.Design/methodology/approach–The formulation of threat resources and objective function in air robot path planning is given.The mathematical model and detailed implementation process of PIO is presented.Comparative experiments with standard differential evolution(DE)algorithm are also conducted.Findings–The feasibility,effectiveness and robustness of the proposed PIO algorithm are shown by a series of comparative experiments with standard DE algorithm.The computational results also show that the proposed PIO algorithm can effectively improve the convergence speed,and the superiority of global search is also verified in various cases.Originality/value–In this paper,the authors first presented a PIO algorithm.In this newly presented algorithm,map and compass operator model is presented based on magnetic field and sun,while landmark operator model is designed based on landmarks.The authors also applied this newly proposed PIO algorithm for solving air robot path planning problems. | Haibin Duan Peixin Qiao | 2014 | International Journal of Intelligent Computing and Cybernetics2014，7，1： | 17 |
| 6 | Multi-robot path planning based on a deep reinforcement learning DQN algorithm显示文摘The unmanned warehouse dispatching system of the‘goods to people’model uses a structure mainly based on a handling robot,which saves considerable manpower and improves the efficiency of the warehouse picking operation.However,the optimal performance of the scheduling system algorithm has high requirements.This study uses a deep Q-network(DQN)algorithm in a deep reinforcement learning algorithm,which combines the Q-learning algorithm,an empirical playback mechanism,and the volume-based technology of productive neural networks to generate target Q-values to solve the problem of multi-robot path planning.The aim of the Q-learning algorithm in deep reinforcement learning is to address two shortcomings of the robot path-planning problem:slow convergence and excessive randomness.Preceding the start of the algorithmic process,prior knowledge and prior rules are used to improve the DQN algorithm.Simulation results show that the improved DQN algorithm converges faster than the classic deep reinforcement learning algorithm and can more quickly learn the solutions to path-planning problems.This improves the efficiency of multi-robot path planning. | Yang Yang Li Juntao Peng Lingling | 2020 | CAAI Transactions on Intelligence Technology2020，5，3： | 15 |
| 7 | A study on key technologies of unmanned driving显示文摘 | Xinyu Zhang Hongbo Gao Mu Guo Guopeng Li Yuchao Liu Deyi Li | 2016 | CAAI Transactions on Intelligence Technology2016，1，1： | 14 |
| 8 | A survey on rough set theory and its applications显示文摘 | Qinghua Zhang Qin Xie Guoyin Wang | 2016 | CAAI Transactions on Intelligence Technology2016，1，4： | 13 |
| 9 | Deep reinforcement learning for dynamic computation offloading and resource allocation in cache-assisted mobile edge computing systems显示文摘Mobile Edge Computing(MEC)is one of the most promising techniques for next-generation wireless communication systems.In this paper,we study the problem of dynamic caching,computation offloading,and resource allocation in cache-assisted multi-user MEC systems with stochastic task arrivals.There are multiple computationally intensive tasks in the system,and each Mobile User(MU)needs to execute a task either locally or remotely in one or more MEC servers by offloading the task data.Popular tasks can be cached in MEC servers to avoid duplicates in offloading.The cached contents can be either obtained through user offloading,fetched from a remote cloud,or fetched from another MEC server.The objective is to minimize the long-term average of a cost function,which is defined as a weighted sum of energy consumption,delay,and cache contents’fetching costs.The weighting coefficients associated with the different metrics in the objective function can be adjusted to balance the tradeoff among them.The optimum design is performed with respect to four decision parameters:whether to cache a given task,whether to offload a given uncached task,how much transmission power should be used during offloading,and how much MEC resources to be allocated for executing a task.We propose to solve the problems by developing a dynamic scheduling policy based on Deep Reinforcement Learning(DRL)with the Deep Deterministic Policy Gradient(DDPG)method.A new decentralized DDPG algorithm is developed to obtain the optimum designs for multi-cell MEC systems by leveraging on the cooperations among neighboring MEC servers.Simulation results demonstrate that the proposed algorithm outperforms other existing strategies,such as Deep Q-Network(DQN). | Samrat Nath Jingxian Wu | 2020 | Intelligent and Converged Networks2020，1，2： | 13 |
| 10 | FAIR Data and Services in Biodiversity Science and Geoscience显示文摘We examine the intersection of the FAIR principles(Findable,Accessible,Interoperable and Reusable),the challenges and opportunities presented by the aggregation of widely distributed and heterogeneous data about biological and geological specimens,and the use of the Digital Object Architecture(DOA)data model and components as an approach to solving those challenges that offers adherence to the FAIR principles as an integral characteristic.This approach will be prototyped in the Distributed System of Scientific Collections(DiSSCo)project,the pan-European Research Infrastructure which aims to unify over 110 natural science collections across 21 countries.We take each of the FAIR principles,discuss them as requirements in the creation of a seamless virtual collection of bio/geo specimen data,and map those requirements to Digital Object components and facilities such as persistent identification,extended data typing,and the use of an additional level of abstraction to normalize existing heterogeneous data structures.The FAIR principles inform and motivate the work and the DO Architecture provides the technical vision to create the seamless virtual collection vitally needed to address scientific questions of societal importance. | Larry Lannom Dimitris Koureas Alex R.Hardisty | 2020 | Data Intelligence2020，2，1： | 12 |
| 11 | Social network search based on semantic analysis and learning显示文摘 | Feifei Kou Junping Du Yijiang He Lingfei Ye | 2016 | CAAI Transactions on Intelligence Technology2016，1，4： | 11 |
| 12 | AMiner:Search and Mining of Academic Social Networks显示文摘AMiner is a novel online academic search and mining system,and it aims to provide a systematic modeling approach to help researchers and scientists gain a deeper understanding of the large and heterogeneous networks formed by authors,papers,conferences,journals and organizations.The system is subsequently able to extract researchers’profiles automatically from the Web and integrates them with published papers by a way of a process that first performs name disambiguation.Then a generative probabilistic model is devised to simultaneously model the different entities while providing a topic-level expertise search.In addition,AMiner offers a set of researcher-centered functions,including social influence analysis,relationship mining,collaboration recommendation,similarity analysis and community evolution.The system has been in operation since 2006 and has been accessed from more than 8 million independent IP addresses residing in more than 200 countries and regions. | Huaiyu Wan Yutao Zhang Jing Zhang Jie Tang | 2019 | Data Intelligence2019，1，1： | 11 |
| 13 | Unique,Persistent,Resolvable:Identifiers as the Foundation of FAIR显示文摘The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ecosystem.Persistent,globally unique identifiers,resolvable on the Web,and associated with a set of additional descriptive metadata,are foundational to FAIR data.Here we describe some basic principles and exemplars for their design,use and orchestration with other system elements to achieve FAIRness for digital research objects. | Nick Juty Sarala M.Wimalaratne Stian Soiland-Reyes John Kunze Carole A.Goble Tim Clark | 2020 | Data Intelligence2020，2，1： | 11 |
| 14 | A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing显示文摘With large-scale production and the need for high-quality tomatoes to meet consumer and market standards criteria,have led to the need for an inline,accurate,reliable grading system during the post-harvest process.This study introduced a tomato grading machine vision system based on RGB images.The proposed system performed calyx and stalk scar detection at an average accuracy of 0.9515 for both defected and healthy tomatoes by histogramthresholding based on themean g-r value of these regions of interest.Defected regionswere detected by an RBF-SVMclassifier using the LAB color-space pixel values.Themodel achieved an overall accuracy of 0.989 upon validation.Four grading categories recognitionmodelswere developed based on color and texture features.The RBF-SVMoutperformed all the explored modelswith the highest accuracy of 0.9709 for healthy and defected category.However,the grading accuracy decreased as the number of grading categories increased.A combination of color and texture features achieved the highest accuracy in all the grading categories in image features evaluation.This proposed system can be used as an inline tomato sorting tool to ensure that quality standards are adhered to and maintained. | David Ireri Eisa Belal Cedric Okinda Nelson Makange Changying Ji | 2019 | Artificial Intelligence in Agriculture2019，，2： | 10 |
| 15 | CN-DBpedia2: An Extraction and Verification Framework for Enriching Chinese Encyclopedia Knowledge Base显示文摘Knowledge base plays an important role in machine understanding and has been widely used in various applications, such as search engine, recommendation system and question answering. However, most knowledge bases are incomplete, which can cause many downstream applications to perform poorly because they cannot find the corresponding facts in the knowledge bases. In this paper, we propose an extraction and verification framework to enrich the knowledge bases. Specifically, based on the existing knowledge base, we first extract new facts from the description texts of entities. But not all newly-formed facts can be added directly to the knowledge base because the errors might be involved by the extraction. Then we propose a novel crowd-sourcing based verification step to verify the candidate facts. Finally, we apply this framework to the existing knowledge base CN-DBpedia and construct a new version of knowledge base CN-DBpedia2, which additionally contains the high confidence facts extracted from the description texts of entities. | Bo Xu Jiaqing Liang Chenhao Xie Bin Liang Lihan Chen Yanghua Xiao | 2019 | Data Intelligence2019，1，3： | 9 |
| 16 | Reconfigurable intelligent surfaces for wireless communications:Overview of hardware designs,channel models,and estimation techniques显示文摘The demanding objectives for the future sixth generation(6G)of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity,as well as revolutionary communication and computing paradigms.Among the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces(RISs),which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of functionalities.Incorporating RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one,intended for various networking objectives,such as coverage extension and capacity boosting,spatiotemporal focusing with benefits in energy efficiency and secrecy,and low electromagnetic field exposure.Motivated by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments,in this paper,we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal propagation.We also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems,which constitute a prerequisite step for the optimized incorporation of RISs in future wireless networks.Finally,we discuss the relevance of the RIS technology in the latest wireless communication standards,and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches. | Mengnan Jian George C.Alexandropoulos Ertugrul Basar Chongwen Huang Ruiqi Liu Yuanwei Liu Chau Yuen | 2022 | Intelligent and Converged Networks2022，3，1： | 9 |
| 17 | Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues显示文摘Pesticide residue is an important factor that affects food safety.In order to achieve effective detection of pesticide residues in apples,a machine-vision-based segmentation algorithm and hyperspectral techniques were used to segment the foreground and background regions of the apple image.By calculating the roundness value and extracting the region with the highest roundness value in the connected region,a region of interest(ROI)maskwas created for the apple.Four pesticides(chlorpyrifos,carbendazimand two mixed pesticides)and an inactive control were used at the same concentration of 100 ppm(except for the control group),and the hyperspectral region of the corresponding sample image was extracted by obtaining the different types of pesticide residues in the ROI masks.To increase the diversity of the samples and to expand the dataset,Gaussianwhite noise with a varying signal-to-noise ratio was added to each of the hyperspectral images of the apple.The number of samples was increased from four types of 12 samples to four types of 72 samples,giving 4608 hyperspectral data images in each category.The structure and parameters of a convolutional neural network(CNN)were determined using theoretical analysis and experimental verification.All the extracted hyperspectral images of apples were normalized to 227×227×3 pixels as the input of the CNN network for pesticide residue detection.There were 18,432 sample data of four types for 72 samples.Of these,12,288 images were selected using a bootstrap sampling method as the training set,and 6144 as the test set,with no overlap.The test results showthatwhen the number of training epochswas 10,the accuracy of the test set detectionwas 99.09%,and the detection accuracy of the single-band average imagewas 95.35%.A comparison with traditional k-nearest neighbor(KNN)and support vectormachine classification algorithms showed that the detection accuracy for KNNwas 43.75%and the average time was 0.7645 s.These results demonstrate that our method is a small-sample,noncontact,fast,effective and low-cost technique that can provide effective pesticide residue detection in postharvest apples. | Bo Jiang Jinrong He Shuqin Yang Hongfei Fu Tong Li Huaibo Song Dongjian He | 2019 | Artificial Intelligence in Agriculture2019，，1： | 8 |
| 18 | YOLOP:You Only Look Once for Panoptic Driving Perception显示文摘A panoptic driving perception system is an essential part of autonomous driving.A high-precision and real-time perception system can assist the vehicle in making reasonable decisions while driving.We present a panoptic driving perception network(you only look once for panoptic(YOLOP))to perform traffic object detection,drivable area segmentation,and lane detection simultaneously.It is composed of one encoder for feature extraction and three decoders to handle the specific tasks.Our model performs extremely well on the challenging BDD100K dataset,achieving state-of-the-art on all three tasks in terms of accuracy and speed.Besides,we verify the effectiveness of our multi-task learning model for joint training via ablative studies.To our best knowledge,this is the first work that can process these three visual perception tasks simultaneously in real-time on an embedded device Jetson TX2(23 FPS),and maintain excellent accuracy.To facilitate further research,the source codes and pre-trained models are released at http://gffzz188fe103f8f1460aswbwu95ovqnc06n9v.ffgz.tsg.suse.edu.cn/hustvl/YOLOP. | Dong Wu Man-Wen Liao Wei-Tian Zhang Xing-Gang Wang Xiang Bai Wen-Qing Cheng Wen-Yu Liu | 2022 | Machine Intelligence Research2022，19，6： | 8 |
| 19 | An intelligent self-sustained RAN slicing framework for diverse service provisioning in 5G-beyond and 6G networks显示文摘Network slicing is a key technology to support the concurrent provisioning of heterogeneous Quality of Service(QoS)in the 5th Generation(5G)-beyond and the 6th Generation(6G)networks.However,effective slicing of Radio Access Network(RAN)is very challenging due to the diverse QoS requirements and dynamic conditions in the 6G networks.In this paper,we propose a self-sustained RAN slicing framework,which integrates the self-management of network resources with multiple granularities,the self-optimization of slicing control performance,and self-learning together to achieve an adaptive control strategy under unforeseen network conditions.The proposed RAN slicing framework is hierarchically structured,which decomposes the RAN slicing control into three levels,i.e.,network-level slicing,next generation NodeB(gNodeB)-level slicing,and packet scheduling level slicing.At the network level,network resources are assigned to each gNodeB at a large timescale with coarse resource granularity.At the gNodeB-level,each gNodeB adjusts the configuration of each slice in the cell at the large timescale.At the packet scheduling level,each gNodeB allocates radio resource allocation among users in each network slice at a small timescale.Furthermore,we utilize the transfer learning approach to enable the transition from a model-based control to an autonomic and self-learning RAN slicing control.With the proposed RAN slicing framework,the QoS performance of emerging services is expected to be dramatically enhanced. | Jie Mei Xianbin Wang Kan Zheng | 2020 | Intelligent and Converged Networks2020，1，3： | 8 |
| 20 | Ontology-based Access Control for FAIR Data显示文摘This paper focuses on fine-grained,secure access to FAIR data,for which we propose ontology-based data access policies.These policies take into account both the FAIR aspects of the data relevant to access(such as provenance and licence),expressed as metadata,and additional metadata describing users.With this tripartite approach(data,associated metadata expressing FAIR information,and additional metadata about users),secure and controlled access to object data can be obtained.This yields a security dimension to the“A”(accessible)in FAIR,which is clearly needed in domains like security and intelligence.These domains need data to be shared under tight controls,with widely varying individual access rights.In this paper,we propose an approach called Ontology-Based Access Control(OBAC),which utilizes concepts and relations from a data set's domain ontology.We argue that ontology-based access policies contribute to data reusability and can be reconciled with privacy-aware data access policies.We illustrate our OBAC approach through a proof-of-concept and propose that OBAC to be adopted as a best practice for access management of FAIR data. | Christopher Brewster Barry Nouwt Stephan Raaijmakers Jack Verhoosel | 2020 | Data Intelligence2020，2，1： | 8 |
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