In this paper, we solve the inter-agent collision avoidance problem in an arbitrary n-dimensional Euclidean space using reciprocal safety velocity cones (RSVCs). We propose a decentralized feedback control strategy th...
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In this paper, we solve the inter-agent collision avoidance problem in an arbitrary n-dimensional Euclidean space using reciprocal safety velocity cones (RSVCs). We propose a decentralized feedback control strategy that guarantees simultaneously asymptotic stabilization to a reference and collision avoidance. Our algorithm is purely decentralized in the sense that each agent uses only local information about its neighbouring agents. Moreover, the proposed solution can be implemented using only inter-agent bearing measurements. Therefore, the algorithm is a sensor-based control strategy which is practically implementable using a wide range of sensors such as vision systems and range scanners. Simulation results in a two dimensional environment cluttered with agents shows that the number of possible deadlocks is marginal and decrease with the decrease in the clutteredness of the workspace.
In recent years, software engineers have explored ways to assist quantum software programmers. Our goal in this paper is to continue this exploration and see if quantum software programmers deal with some problems pla...
In recent years, software engineers have explored ways to assist quantum software programmers. Our goal in this paper is to continue this exploration and see if quantum software programmers deal with some problems plaguing classical programs. Specifically, we examine whether intermittently failing tests, i.e., flaky tests, affect quantum software development. To explore flakiness, we conduct a preliminary analysis of 14 quantum software repositories. Then, we identify flaky tests and categorize their causes and methods of fixing them. We find flaky tests in 12 out of 14 quantum software repositories. In these 12 repositories, the lower boundary of the percentage of issues related to flaky tests ranges between 0.26% and 1.85% per repository. We identify 46 distinct flaky test reports with 8 groups of causes and 7 common solutions. Further, we notice that quantum programmers are not using some of the recent flaky test countermeasures developed by software engineers. This work may interest practitioners, as it provides useful insight into the resolution of flaky tests in quantum programs. Researchers may also find the paper helpful as it offers quantitative data on flaky tests in quantum software and points to new research opportunities.
Drowsy drivers can cause casualties, grave injuries, and fimancial waste; thus, it is essential to have a mechanism that detects drowsiness among drivers. This paper presents a compressed non-intrusive drowsiness moni...
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Drowsy drivers can cause casualties, grave injuries, and fimancial waste; thus, it is essential to have a mechanism that detects drowsiness among drivers. This paper presents a compressed non-intrusive drowsiness monitoring system in three phases. In phase 1, we target the detection of human drowsiness using a set of state-of-the-art machine learning architectures and a custom architecture by using the state of eye and mouth regions from a combination of the MRL Eye and YawDD datasets. Out of VGG-16 (97% test accuracy), Xception (98%), MobileNet (97%), InceptionV3 (98%), and custom architecture (98%), the custom architecture is selected to get carried forward to the subsequent phases because it gives comparable results to deeper models with 100 times fewer parameters. The satisfactory performance of this model was also verified on a selective subset of the NTHU-DDD video dataset. In phase 2, the custom architecture is employed to extract appearance-related features from a set of frames used by an LSTM network to learn motion-related characteristics. The CNN+LSTM model gives 98% test accuracy on the mouth test dataset. In phase 3, to target an embedded board implementation, we used the knowledge distillation technique where the thinned-out version of custom architecture (student network) to get trained under the guidance of Xception (teacher network). Under this experimental phase, the accuracy of the student network got increased from 92% to 96.4% on eye detection and from 97% to 98% on mouth detection.
This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision ...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation.
We compute precise asymptotic expressions for the learning curves of least squares random feature (RF) models with either a separable strongly convex regularization or the 1 regularization. We propose a novel multi-le...
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Sentiment Classification is a continuing area of research in text mining. Sentiment Analysis the automatic representation of the ideas, emotions and subjectivity of text, whose purpose is to define the polarity of the...
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In this paper, we propose a secure-by-construction scheme for synthesizing controllers to enforce safety and security properties simultaneously over control systems. As a key insight, we establish a bridge between the...
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In this paper, we propose a secure-by-construction scheme for synthesizing controllers to enforce safety and security properties simultaneously over control systems. As a key insight, we establish a bridge between the desired safety and security properties by leveraging notions of (augmented) control barrier functions. Based on these functions, we show that one can synthesize secure-by-construction controllers for control systems with continuous state and input sets. Additionally, we provide sum-of-square (SOS) conditions under which the desired (augmented) control barrier functions can be constructed. Finally, we demonstrate the applicability of our results on a case study.
The smart city comprises various interlinked elements which communicate data and offers urban life to *** Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agriculture,logistics,...
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The smart city comprises various interlinked elements which communicate data and offers urban life to *** Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agriculture,logistics,and *** improving the drone flying safety and quality of services,a significant solution is for designing the Internet of Drones(IoD)where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones,where the drones were utilized for collecting the data,and communicate with *** addition,the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering(TSA-C)technique to choose cluster heads(CHs)and organize clusters in IoV ***,the SIRSS-CIoD technique involves the design of a biogeography-based optimization(BBO)technique to an optimum route selection(RS)*** design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study.A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.
Cataracts are the leading cause of visual impairment and blindness globally attracting abroad attention from society. Over the years researchers have developed many state-of-the-art convolutional neural networks (CNNs...
Cataracts are the leading cause of visual impairment and blindness globally attracting abroad attention from society. Over the years researchers have developed many state-of-the-art convolutional neural networks (CNNs) to recognize cataract severity levels based on different ophthalmic images. However most current works focus on improving cataract recognition performance by designing complex CNNs often ignoring resource-constrained medical device limitations. To this problem this paper proposes a novel dual-mixed channel-independent convolution (DMIConv) method which takes advantage of the multiscale convolution kernels by combining a depthwise convolution with a depthwise dilated convolution sequentially. Moreover we build a lightweight dual-mixed channel-independent network (DMINet) to recognize cataracts. To verify the effectiveness and efficiency of DMINet we conduct extensive experiments on a clinical anterior segment optical coherence tomography (AS-OCT) dataset of nuclear cataract (NC) and a publicly available OCT dataset. The results show that our proposed DMINet keeps a better tradeoff between the model complexity and the classification performance than efficient CNNs e.g DMINet outperforms MixNet by 3.34% of accuracy by using 4.58 % fewer parameters
Malicious webpage is developed or manipulated to be used as attack tool where it is considered as one of the main reasons of Internet criminal activities. Thus, it is essential to detect such webpages and prevent end ...
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