Fixed-Wing UAVs flocking in continuous spaces: A deep reinforcement learning approach
پهپادهای ثابت بال در فضاهای مداوم هجوم می آورند: یک رویکرد یادگیری تقویتی عمیق-2020
Fixed-Wing UAVs (Unmanned Aerial Vehicles) flocking is still a challenging problem due to the kinematics complexity and environmental dynamics. In this paper, we solve the leader–followers flocking problem using a novel deep reinforcement learning algorithm that can generate roll angle and velocity commands by training an end-to-end controller in continuous state and action spaces. Specifically, we choose CACLA (Continuous Actor–Critic Learning Automation) as the base algorithm and we use the multi-layer perceptron to represent both the actor and the critic. Besides, we further improve the learning efficiency by using the experience replay technique that stores the training data in the experience memory and samples from the memory as needed. We have compared the performance of the proposed CACER (Continuous Actor–Critic with Experience Replay) algorithm with benchmark algorithms such as DDPG and double DQN in numerical simulation, and we have demonstrated the performance of the learned optimal policy in semi-physical simulation without any parameter tuning.
Keywords: Fixed-wing UAV | Flocking | Reinforcement learning | Actor–critic
Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data
جمع شدن ورودی ها و خروجی ها قبل از تجزیه و تحلیل پوششی داده ها تحت داده های بزرگ-2020
The main goal of this paper is to explore the possible solutions to a ‘big data’ problem related to the very large dimensions of input–output data. In particular, we focus on the cases of severe ‘curse of di- mensionality’ problem that require dimension-reduction prior to using Data Envelopment Analysis. To achieve this goal, we have presented some theoretical grounds and performed a new to the literature simulation study where we explored the price-based aggregation as a solution to address the problem of very large dimensions.
Keywords: Data Envelopment Analysis | Productivity | Efficiency | Big data
Digital entrepreneurship and field conditions for institutional change– Investigating the enabling role of cities
کارآفرینی دیجیتال و شرایط میدانی برای تغییر نهادی - بررسی نقش توانمند شهرها-2019
Digital entrepreneurship may result in institutional turbulence and new initiatives are frequently blocked by vested interest groups who posit superior financial and relational resources. In this paper, we explore the role of cities in facilitating digital entrepreneurship and overcoming institutional resistance to innovation. Drawing upon two historical case studies of digital entrepreneurship in the city of Stockholm along with an extensive material on the sharing economy in Sweden, our results suggest that cities offer an environment that is critical for digital entrepreneurship. The economic and technological diversity of a city may provide the field conditions required for institutional change to take place and to avoid regulatory capture..
Keywords: Digital entrepreneurship | Digital innovation | Cities | Agglomeration | Institutional entrepreneurship | Field conditions | Regulatory capture
Automatic spike detection in beam loss signals for LHC collimator alignment
تشخیص خودکار spike در سیگنال های از دست رفته پرتو برای تراز کولیاتور LHC-2019
collimation system is installed in the Large Hadron Collider to protect its super-conducting magnets and sensitive equipment from potentially dangerous beam halo particles. The collimator settings are determined following an alignment procedure, whereby collimator jaws are moved towards the beam until a suitable spike pattern, consisting of a sharp rise followed by a slow decay, is observed in nearby beam loss monitors. This indicates the collimator jaw is aligned to the beam. The current method for aligning collimators is semiautomated whereby an operator must continuously observe the loss signals to determine whether the jaw has touched the beam, or if some other perturbation in the beam caused the losses. The human element in this procedure can result in errors and is a major bottleneck in automating and speeding up the alignment. This paper proposes to automate the human task of spike detection by using machine learning. A data set was formed from previous alignment campaigns, from which fourteen manually engineered features were extracted and six machine learning models were trained, analysed in-depth and thoroughly tested. The suitability of using machine learning in LHC operation was confirmed during collimator alignments performed in 2018, which significantly benefited from the models trained through machine learning in this study.
Keywords: Large Hadron Collider | Collimation | Machine learning | Spike detection | Pattern recognition | Automatic alignment
A comprehensive view on quantity based aggregation for cadastral databases
دیدگاه جامع بر روی کمیت بر اساس تراکم برای پایگاه داده های cadastral-2017
Article history:Available online 1 March 2017Keywords: Security Database Access control Inference AggregationQuantity Based Aggregation (QBA) control is a subject that is closely related to inference control in databases. The goal is to enforce k out of n disclosure control. In this paper we work on QBA problems in the context of cadastral databases: how to prevent a user from knowing 1) the owners of all parcels in a region, and 2) all parcels belonging to the same owner. This work combines and extends our pre- vious work on the subject [1, 2, 3]. We overview the legislative context surrounding cadastral databases. We give important deﬁnitions related to the QBA concept. We present a complete model for QBA control in cadastral databases. We show how to implement the security policy eﬃciently, and we present our prototype of secure cadastral databases with some performance evaluations.© 2016 Elsevier Ltd. All rights reserved.
Keywords: Security | Database | Access control | Inference | Aggregation