The Origin of the Gross Alpha and Beta Radiation Values of the Waters of Çanakkale Strait (Çanakkale/Turkey)

This study is an evaluation of radioactivity the waters of the Çanakkale Strait. The gross alpha- and gross beta-radioactivity counts (Berthold, LB770-PC 10-Channel Low-Level Planchet Counter) were calculated for seawater samples taken from eight different regions of the Çanakkale Strait (Şevketiye, Seddülbahir, Lapseki, Kumkale, Burhanlı, Dereliman, Eceabat, and Gelibolu). In the samples, the gross alpha-radiation ranged between 0.064 and 0.046Bq/L and the gross beta-radiation ranged between 14.325 and 10.532 Bq/L. The highest gross alpharadiation concentration was measured at Gelibolu (0.064 Bq/L) while the lowest (0.046 Bq/L) was measured at Şevketiye. The highest value for gross beta-radiation concentration (14.325 Bq/L) was measured in Seddülbahir, and the lowest value (10.532 Bq/L) was measured in Dereliman. The gross alpha-radiation concentrations measured by the Turkish Atomic Energy Authority in Çanakkale’s drinking and utility water ranged between 0.05 and 0.400 Bq/L, and the highest values (0.300 to 0.400Bq/L) were found in the Ezine county. Gross beta-radiation concentrations ranged from 0.05 to 0.500 Bq/L, and the highest values (from 0.400 to 0.500 Bq/L) were recorded in Lapseki province. The gross beta-radiation concentrations in both the sample results and TAEK data were determined to be high in Lapseki and its vicinity. Comparing the mean gross beta- and alpha-radiation concentration values of the Çanakkale Strait with the Bosphorus, the Sea of Marmara and the Black Sea, beta-radiation values in the study area were very high. Gross alpha-radiation results were low in the study area compared to other regions. Evaluating the results against the legal limit threshold, the results were above the legal limit for gross beta-radiation. This result indicates that the water is affected by the rocks through which it passe

Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach

Microgrids (MG) are anticipated to be important players in the future smart grid. For proper operation of MGs an Energy Management System (EMS) is essential. The EMS of an MG could be rather complicated when renewable energy resources (RER), energy storage system (ESS) and demand side management (DSM) need to be orchestrated. Furthermore, these systems may belong to different entities and competition may exist between them. Nash equilibrium is most commonly used for coordination of such entities however the convergence and existence of Nash equilibrium can not always be guaranteed. To this end, we use the correlated equilibrium to coordinate agents, whose convergence can be guaranteed. In this paper, we build an energy trading model based on mid-market rate, and propose a correlated Q-learning (CEQ) algorithm to maximize the revenue of each agent. Our results show that CEQ is able to balance the revenue of agents without harming total benefit. In addition, compared with Q-learning without correlation, CEQ could save 19.3% cost for the DSM agent and 44.2% more benefits for the ESS agent.Comment: Accepted by 2020 IEEE International Conference on SmartGridComm, 978-1-7281-6127-3/20/$31.00 copyright 2020 IEE

Correlated Deep Q-learning based Microgrid Energy Management

Microgrid (MG) energy management is an important part of MG operation. Various entities are generally involved in the energy management of an MG, e.g., energy storage system (ESS), renewable energy resources (RER) and the load of users, and it is crucial to coordinate these entities. Considering the significant potential of machine learning techniques, this paper proposes a correlated deep Q-learning (CDQN) based technique for the MG energy management. Each electrical entity is modeled as an agent which has a neural network to predict its own Q-values, after which the correlated Q-equilibrium is used to coordinate the operation among agents. In this paper, the Long Short Term Memory networks (LSTM) based deep Q-learning algorithm is introduced and the correlated equilibrium is proposed to coordinate agents. The simulation result shows 40.9% and 9.62% higher profit for ESS agent and photovoltaic (PV) agent, respectively.Comment: Accepted by 2020 IEEE 25th International Workshop on CAMAD, 978-1-7281-6339-0/20/$31.00 \copyright 2020 IEE

Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing

Radio access network (RAN) slicing is an important pillar in cross-domain network slicing which covers RAN, edge, transport and core slicing. The evolving network architecture requires the orchestration of multiple network resources such as radio and cache resources. In recent years, machine learning (ML) techniques have been widely applied for network management. However, most existing works do not take advantage of the knowledge transfer capability in ML. In this paper, we propose a deep transfer reinforcement learning (DTRL) scheme for joint radio and cache resource allocation to serve 5G RAN slicing. We first define a hierarchical architecture for the joint resource allocation. Then we propose two DTRL algorithms: Q-value-based deep transfer reinforcement learning (QDTRL) and action selection-based deep transfer reinforcement learning (ADTRL). In the proposed schemes, learner agents utilize expert agents' knowledge to improve their performance on target tasks. The proposed algorithms are compared with both the model-free exploration bonus deep Q-learning (EB-DQN) and the model-based priority proportional fairness and time-to-live (PPF-TTL) algorithms. Compared with EB-DQN, our proposed DTRL based method presents 21.4% lower delay for Ultra Reliable Low Latency Communications (URLLC) slice and 22.4% higher throughput for enhanced Mobile Broad Band (eMBB) slice, while achieving significantly faster convergence than EB-DQN. Moreover, 40.8% lower URLLC delay and 59.8% higher eMBB throughput are observed with respect to PPF-TTL.Comment: Under review of IEEE Transactions on Cognitive Communications and Networkin

Gamma Dose Values of Stratigraphic Units of Behramkale (Çanakkale) - Zeytinli (Edremit-Balikesir) Section of Kaz Mountains

In this study, gamma dose values were measured at 25 locations around a distance of 60 km parallel to the Aegean Sea in Güre, Küçükkuyu and Kazdağı regions. These measurements were made by keeping the Eberline Smart Portable (ESP) scintillator detector constant at a height of 1 meter above ground level. A SPA-6 plastic scintillation sensor is connected to the instrument tip to perform the measurement. These devices are direct measurement of external radiation. As the measurements were carried out in the open area, 0.2 occupancy factor was used in the calculations. In the studies, the highest effective dose value was calculated as 0.3 mSv at the location 2 (Asos Kadırga Bay). The lowest effective dose value is 0.054 mSV at the 15th location (Avcılar Village Mountain slope) and the average annual gamma dose is 0.14 mSv. For the study area, when the lifetime risk of cancer was calculated using gamma effective dose values, it was determined at the highest 2. locations (0.0012) and the lowest at the 15th locations (0,21x10-3). The average lifetime cancer risk value (2,39x10-4) of Turkey, were compared with values calculated in this study. In this comparison, the gamma dose values of locations 9 and 15 were lower and the values of other locations were higher. © 2020, Turkish Chemical Society. All rights reserved

Joint Sensing and Communications for Deep Reinforcement Learning-based Beam Management in 6G

User location is a piece of critical information for network management and control. However, location uncertainty is unavoidable in certain settings leading to localization errors. In this paper, we consider the user location uncertainty in the mmWave networks, and investigate joint vision-aided sensing and communications using deep reinforcement learning-based beam management for future 6G networks. In particular, we first extract pixel characteristic-based features from satellite images to improve localization accuracy. Then we propose a UK-medoids based method for user clustering with location uncertainty, and the clustering results are consequently used for the beam management. Finally, we apply the DRL algorithm for intra-beam radio resource allocation. The simulations first show that our proposed vision-aided method can substantially reduce the localization error. The proposed UK-medoids and DRL based scheme (UKM-DRL) is compared with two other schemes: K-means based clustering and DRL based resource allocation (K-DRL) and UK-means based clustering and DRL based resource allocation (UK-DRL). The proposed method has 17.2% higher throughput and 7.7% lower delay than UK-DRL, and more than doubled throughput and 55.8% lower delay than K-DRL