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Sökning: WFRF:(Wei Wei) > Blekinge Tekniska Högskola

  • Resultat 1-10 av 27
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1.
  • Sun, Bin, et al. (författare)
  • An Improved k-Nearest Neighbours Method for Traffic Time Series Imputation
  • 2017
  • Konferensbidrag (refereegranskat)abstract
    • Intelligent transportation systems (ITS) are becoming more and more effective, benefiting from big data. Despite this, missing data is a problem that prevents many prediction algorithms in ITS from working effectively. Much work has been done to impute those missing data. Among different imputation methods, k-nearest neighbours (kNN) has shown excellent accuracy and efficiency. However, the general kNN is designed for matrix instead of time series so it lacks the usage of time series characteristics such as windows and weights that are gap-sensitive. This work introduces gap-sensitive windowed kNN (GSW-kNN) imputation for time series. The results show that GSW-kNN is 34% more accurate than benchmarking methods, and it is still robust even if the missing ratio increases to 90%.
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2.
  • Hagelbäck, Johan, et al. (författare)
  • Psychophysiological Interaction and Empathic Cognition for Human-Robot Cooperative Work (PsyIntEC)
  • 2014
  • Ingår i: Gearing Up and Accelerating Cross-Fertilization between Academic and Industrial Robotics Research in Europe. - Cham : Springer. - 9783319029337 ; , s. 283-299
  • Bokkapitel (refereegranskat)abstract
    • The aim of the PsyIntEC project is to explore affective and cognitive modeling of humans in human-robot interaction (HRI) as a basis for behavioral adaptation. To achieve this we have explored human affective perception of relevant modalities in human-human and human-robot interaction on a collaborative problem-solving task using psychophysiological measurements. The experiments conducted have given us valuable insight into the communicational and affective queues interplaying in such interactions from the human perspective. The results indicate that there is an increase in both positive and negative emotions when interacting with robots compared to interacting with another human or solving the task alone, but detailed analysis on shorter time segments is required for the results from all sensors to be conclusive and significant.
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3.
  • Jerčić, Petar, et al. (författare)
  • The Effect of Emotions and Social Behavior on Performance in a Collaborative Serious Game Between Humans and Autonomous Robots
  • 2018
  • Ingår i: International Journal of Social Robotics. - : Springer. - 1875-4791 .- 1875-4805. ; 10:1, s. 115-129
  • Tidskriftsartikel (refereegranskat)abstract
    • The aim of this paper is to investigate performance in a collaborative human–robot interaction on a shared serious game task. Furthermore, the effect of elicited emotions and perceived social behavior categories on players’ performance will be investigated. The participants collaboratively played a turn-taking version of the Tower of Hanoi serious game, together with the human and robot collaborators. The elicited emotions were analyzed in regards to the arousal and valence variables, computed from the Geneva Emotion Wheel questionnaire. Moreover, the perceived social behavior categories were obtained from analyzing and grouping replies to the Interactive Experiences and Trust and Respect questionnaires. It was found that the results did not show a statistically significant difference in participants’ performance between the human or robot collaborators. Moreover, all of the collaborators elicited similar emotions, where the human collaborator was perceived as more credible and socially present than the robot one. It is suggested that using robot collaborators might be as efficient as using human ones, in the context of serious game collaborative tasks.
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4.
  • Jiang, Yuning, 1993-, et al. (författare)
  • A Semantic Framework With Humans in the Loop for Vulnerability-Assessment in Cyber-Physical Production Systems
  • 2020
  • Ingår i: Risks and Security of Internet and Systems. - Cham : Springer. - 9783030415679 - 9783030415686 ; , s. 128-143
  • Konferensbidrag (refereegranskat)abstract
    • Criticalmanufacturingprocessesinsmartnetworkedsystems such as Cyber-Physical Production Systems (CPPSs) typically require guaranteed quality-of-service performances, which is supported by cyber- security management. Currently, most existing vulnerability-assessment techniques mostly rely on only the security department due to limited communication between di↵erent working groups. This poses a limitation to the security management of CPPSs, as malicious operations may use new exploits that occur between successive analysis milestones or across departmental managerial boundaries. Thus, it is important to study and analyse CPPS networks’ security, in terms of vulnerability analysis that accounts for humans in the production process loop, to prevent potential threats to infiltrate through cross-layer gaps and to reduce the magnitude of their impact. We propose a semantic framework that supports the col- laboration between di↵erent actors in the production process, to improve situation awareness for cyberthreats prevention. Stakeholders with dif- ferent expertise are contributing to vulnerability assessment, which can be further combined with attack-scenario analysis to provide more prac- tical analysis. In doing so, we show through a case study evaluation how our proposed framework leverages crucial relationships between vulner- abilities, threats and attacks, in order to narrow further the risk-window induced by discoverable vulnerabilities.
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5.
  • Li, Bing, et al. (författare)
  • Capacity of Advance Right-Turn Motorized Vehicles at Signalized Intersections for Mixed Traffic Conditions
  • 2019
  • Ingår i: Mathematical problems in engineering (Print). - : HINDAWI LTD. - 1024-123X .- 1563-5147.
  • Tidskriftsartikel (refereegranskat)abstract
    • Right-turn motorized vehicles turn right using channelized islands, which are used to improve the capacity of intersections. For ease of description, these kinds of right-turn motorized vehicles are called advance right-turn motorized vehicles (ARTMVs) in this paper. The authors analyzed four aspects of traffic conflict involving ARTMVs with other forms of traffic flow. A capacity model of ARTMVs is presented here using shockwave theory and gap acceptance theory. The proposed capacity model was validated by comparison to the results of the observations based on data collected at a single intersection with channelized islands in Kunming, the Highway Capacity Manual (HCM) model and the VISSIM simulation model. To facilitate engineering applications, the relationship describing the capacity of the ARTMVs with reference to the distance between the conflict zone and the stop line and the relationship describing the capacity of the ARTMVs with reference to the effective red time of the nonmotorized vehicles moving in the same direction were analyzed. The authors compared these results to the capacity of no advance right-turn motorized vehicles (NARTMVs). The results show that the capacity of the ARTMVs is more sensitive to the changes in the arrival rate of nonmotorized vehicles when the arrival rate of the nonmotorized vehicles is 500(veh/h)similar to 2000(veh/h) than when the arrival rate is some other value. In addition, the capacity of NARTMVs is greater than the capacity of ARTMVs when the nonmotorized vehicles have a higher arrival rate.
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6.
  • Lin, Sen, et al. (författare)
  • Non-destructive monitoring of forming quality of self-piercing riveting via a lightweight deep learning
  • 2023
  • Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Self-piercing riveting (SPR) has been widely used in automobile body jointing. However, the riveting process is prone to various forming quality failures, such as empty riveting, repeated riveting, substrate cracking, and other riveting defects. This paper combines deep learning algorithms to achieve non-contact monitoring of SPR forming quality. And a lightweight convolutional neural network with higher accuracy and less computational effort is designed. The ablation and comparative experiments results show that the lightweight convolutional neural network proposed in this paper achieves improved accuracy and reduced computational complexity. Compared with the original algorithm, the algorithm's accuracy in this paper is increased by 4.5[Formula: see text], and the recall is increased by 1.4[Formula: see text]. In addition, the amount of redundant parameters is reduced by 86.5[Formula: see text], and the amount of computation is reduced by 47.33[Formula: see text]. This method can effectively overcome the limitations of low efficiency, high work intensity, and easy leakage of manual visual inspection methods and provide a more efficient solution for monitoring the quality of SPR forming quality. © 2023. The Author(s).
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7.
  • Ma, Liyao, et al. (författare)
  • Apple grading method based on neural network with ordered partitions and evidential ensemble learning
  • 2022
  • Ingår i: CAAI Transactions on Intelligence Technology. - : John Wiley & Sons. - 2468-6557 .- 2468-2322. ; 7:4, s. 561-569
  • Tidskriftsartikel (refereegranskat)abstract
    • In order to improve the performance of the automatic apple grading and sorting system, in this paper, an ensemble model of ordinal classification based on neural network with ordered partitions and Dempster–Shafer theory is proposed. As a non-destructive grading method, apples are graded into three grades based on the Soluble Solids Content value, with features extracted from the preprocessed near-infrared spectrum of apple serving as model inputs. Considering the uncertainty in grading labels, mass generation approach and evidential encoding scheme for ordinal label are proposed, with uncertainty handled within the framework of Dempster–Shafer theory. Constructing neural network with ordered partitions as the base learner, the learning procedure of the Bagging-based ensemble model is detailed. Experiments on Yantai Red Fuji apples demonstrate the satisfactory grading performances of proposed evidential ensemble model for ordinal classification. © 2022 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology.
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8.
  • Nergård, Henrik, et al. (författare)
  • Current status and upcoming needs in SME’s in Northern regions of Finland, Norway and Sweden : Technologies, personnel, market and ICT in the business process
  • 2012
  • Rapport (övrigt vetenskapligt/konstnärligt)abstract
    • The results written in this document comes from a questionnaire that was sent out within the Interreg IV A Nord project Digital integrated Manufacturing. Partners in the project are CENTRIA Research and Development (Lead partner), Finland, Luleå University of Technology, Sweden and Narvik University College, Norway. The project is financed and supported by the European Commission via Europeiska regionala utvecklingsfonden, Länsstyrelsen Norrbotten, Luleå University of Technology, Lapin Liito, CENTRIA Research and Development, Innovasjon Norge, Troms fylkeskommune, Narvik University College and Nordland Fylkeskommune.The projects purpose is to increase the competence and skills of employees in manufacturing Small and Medium Sized Enterprises (SME’s) so that they can improve their global competitiveness within their area of expertise. The project aims to approach this by demonstrating and using new technologies and methods throughout the entire business chain.The Projects primary target groups are the employees within the SME’s in the Interreg IV A Nord area which includes the following region in Norway, Finland and Sweden; Lapplands landskap, Mellersta Österbottens landskap, Norra Österbottens landskap, Norrbottens län, Västerbottens län (Skellefteå, Norsjö, Malå and Sorsele kommuner), Finnmark fylkeskommune, Troms fylkeskommune and Nordland fylkeskommuneThe project contained 4 work packages and this report contains the results from Work Package 1: Current status and upcoming DIM-needs amongst SME’s. One task within this work package was to conduct a questionnaire. The purpose with the questionnaire was to get fundamental knowledge and information from manufacturing companies within the Interreg IV A Nord region regarding the following topics:• General company information and current status• Products and Design and production process• Information and Communication Technologies• Business partner relationships• CompetitionSome conclusions from the questionnaire indicate that companies that answered the questionnaire want to maintain their business and make it grow. Regarding DIM technologies some companies have implemented certain methods, tools, machines to a larger extent than others. The companies state that they are more interested in employing personnel with technical skills (both professional and academic degrees) than personnel with economy skills. Robotics was seen as one area of improvement.
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9.
  • Sun, Bin, et al. (författare)
  • An Overview of Parameter and Data Strategies for K-Nearest Neighbours Based Short-Term Traffic Prediction
  • 2017
  • Ingår i: ACM International Conference Proceeding Series Volume Part F133326. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450353762 ; , s. 68-74
  • Konferensbidrag (refereegranskat)abstract
    • Modern intelligent transportation systems (ITS) requires reliable and accurate short-term traffic prediction. One widely used method to predict traffic is k-nearest neighbours (kNN). Though many studies have tried to improve kNN with parameter strategies and data strategies, there is no comprehensive analysis of those strategies. This paper aims to analyse kNN strategies and guide future work to select the right strategy to improve prediction accuracy. Firstly, we examine the relations among three kNN parameters, which are: number of nearest neighbours (k), search step length (d) and window size (v). We also analysed predict step ahead (m) which is not a parameter but a user requirement and configuration. The analyses indicate that the relations among parameters are compound especially when traffic flow states are considered. The results show that strategy of using v leads to outstanding accuracy improvement. Later, we compare different data strategies such as flow-aware and time-aware ones together with ensemble strategies. The experiments show that the flowaware strategy performs better than the time-aware one. Thus, we suggest considering all parameter strategies simultaneously as ensemble strategies especially by including v in flow-aware strategies.
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10.
  • Sun, Bin, et al. (författare)
  • Anomaly-Aware Traffic Prediction Based on Automated Conditional Information Fusion
  • 2018
  • Ingår i: Proceedings of 21st International Conference on Information Fusion. - : IEEE conference proceedings. - 9780996452762
  • Konferensbidrag (refereegranskat)abstract
    • Reliable and accurate short-term traffic prediction plays a key role in modern intelligent transportation systems (ITS) for achieving efficient traffic management and accident detection. Previous work has investigated this topic but lacks study on automated anomaly detection and conditional information fusion for ensemble methods. This works aims to improve prediction accuracy by fusing information considering different traffic conditions in ensemble methods. In addition to conditional information fusion, a day-week decomposition (DWD) method is introduced for preprocessing before anomaly detection. A k-nearest neighbours (kNN) based ensemble method is used as an example. Real-world data are used to test the proposed method with stratified ten-fold cross validation. The results show that the proposed method with incident labels improves predictions up to 15.3% and the DWD enhanced anomaly-detection improves predictions up to 8.96%. Conditional information fusion improves ensemble prediction methods, especially for incident traffic. The proposed method works well with enhanced detections and the procedure is fully automated. The accurate predictions lead to more robust traffic control and routing systems.
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