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Duong, T.T.,Tuan, T.Q.,Dung, D.V.A.,Van Quy, N.,Vu, D.L.,Nam, M.H.,Chien, N.D.,Yoon, S.G.,Le, A.T. Elsevier 2014 CURRENT APPLIED PHYSICS Vol.14 No.12
Polyaniline nanowires (PANI NWs) were deposited onto fluorine-doped tin oxide (FTO) glass substrate using the cyclic voltammetric method with aniline monomer precursor in HCl aqueous solution. The secondary oxidation peak plays an important role in polymerization of aniline monomer and the optimization of catalytic activity of PANI-based counter electrodes was achieved by controlling the number of cycles. The photovoltaic performance of the dye-sensitized solar cells (DSSCs) with PANI NWs counter electrodes (CEs) was optimized at 4th cycles, and then following parameters were obtained: J<SUB>sc</SUB> = 17.2 mA cm<SUP>-2</SUP>, V<SUB>oc</SUB> = 0.71 V, FF = 59.3%, and efficiency (η) = 7.24%. While, J<SUB>sc</SUB> = 14.7 mA cm<SUP>-2</SUP>, V<SUB>oc</SUB> = 0.77 V, FF = 70.6%, and efficiency (η) = 7.98% in cells with Pt CEs. The PANI NWs were attractive as an alternative CEs for the low-cost DSSCs instead of Pt.
Thuy-Van T. Duong,Le Huu Binh 한국전자통신연구원 2022 ETRI Journal Vol.44 No.5
In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other wellknown routing algorithms.
A Fusion of Data Mining Techniques for Predicting Movement of Mobile Users
Thuy Van T. Duong,Dinh Que Tran 한국통신학회 2015 Journal of communications and networks Vol.17 No.6
Predicting locations of users with portable devices such as IP phones, smart-phones, iPads and iPods in public wireless local area networks (WLANs) plays a crucial role in location management and network resource allocation. Many techniques in machine learning and data mining, such as sequential pattern mining and clustering, have been widely used. However, these approaches have two deficiencies. First, because they are based on profiles of individual mobility behaviors, a sequential pattern technique may fail to predict new users or users with movement on novel paths. Second, using similar mobility behaviors in a cluster for predicting the movement of users may cause significant degradation in accuracy owing to indistinguishable regular movement and random movement. In this paper, we propose a novel fusion technique that utilizes mobility rules discovered from multiple similar users by combining clustering and sequential pattern mining. The proposed technique with two algorithms, named the clusteringbased- sequential-pattern-mining (CSPM) and sequential-patternmining- based-clustering (SPMC), can deal with the lack of information in a personal profile and avoid some noise due to random movements by users. Experimental results show that our approach outperforms existing approaches in terms of efficiency and prediction accuracy.
Canh, T.T.,Verstegen, M.W.A.,Mui, N.B.,Aarnink, A.J.A.,Schrama, J.W.,Van't Klooster, C.E.,Duong, N.K. Asian Australasian Association of Animal Productio 1999 Animal Bioscience Vol.12 No.4
An experiment was conducted to investigate the effect of diet for growing-finishing pigs with high level of non-starch polysaccharides (NSP) from by-products on nitrogen excretion and nitrogen losses from slurry during storage. Sixteen commercial crossbred barrows of about 68 kg BW were randomly allotted to one of four diets. The control diet was formulated using tapioca and rice as basal energy sources. In the other diets, tapioca was replaced by either coconut expellar, rice bran or beer by-product. The diets differed mainly in the amount and compostition of NSP. After a 12-day adaptation period, urine and faeces were collected separately in metabolism cages for 9 days. Urine and faeces from the first four days were used to analyse the nitrogen partitioning. Urine and faeces from the last 5 days were mixed as slurry. The slurry was sampled at the end of the collection period and again after 30 days storage, to analyse for nitrogen to calculate the losses. Increasing dietary NSP reduced urinary nitrogen and nitrogen losses from the slurry during storage. The pigs fed the diet based on beer by-product excreted the most nitrogen via faeces and the least nitrogen via urine. Nitrogen losses from slurry of pigs fed the beer by-product were from 34 to 65% lower than from the other three diets. It is concluded that including NSP-rich by-products in the diet of growing-finishing pigs reduces urinary nitrogen excretion and nitrogen losses from slurry during storage.
Thuy-Van T. Duong,Le Huu Binh,Vuong M. Ngo 한국통신학회 2022 ICT Express Vol.8 No.1
Wireless Mesh Networks is increasingly being applied widely with explosive traffic demand. This leads to a great challenge for traditional routing protocols in ensuring Quality of Service. We propose a QoS-guaranteed intelligent routing algorithm in this paper for WMN with heavy traffic load using reinforcement learning to improve its performance. We build a reward function for the Q-Learning algorithm to choose a route so that the packet delivery ratio is the highest. Concurrently, the learning rate coefficient is flexibly changed to determine constraints of the end-to-end delay. Our performance evaluations show that the proposed algorithm has significantly improved performance compared with other well-known routing algorithms.