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基於深度學習演算法之無人機輔助下世代異質網路資源管理

為了解決Black Out Azari的問題，作者YIRGA YAYEH MUNAYE 這樣論述：

The emerging of the 5G mobile network is envisioned to provide an efficient platform to interconnect machines, objects, and devices with interconnecting people. The 5G technology can enable new user experiences such as resource allocation and provide new service areas such as connecting massive Int

ernet of Things (IoT) equipped with peak data rates, low latency, and massive capacity. The management of resources in a wireless network for the increasingly heterogeneous, complicated, and dynamic IoT communication network is causing several issues. The IoT users, on the other hand, are demanding

the use of high network capacities without the limitations of time and location, as well as the reduced availability and use of terrestrial base stations (BS). As a result, unmanned aerial vehicles (UAVs) are being discussed as a possible alternate and versatile BS for transmitting wireless data tha

t serves as a communication transmitter (Tx) from air-to-ground (A2G) to IoT users. The use of UAVs as a communication platform has a lot of functional implications for future wireless networks, particularly for resource management (RM) to support wireless networks. In this dissertation, UAVs are im

plemented and used as an A2G communication link to gather communication data from IoT ground users who are connected to them. Furthermore, in a wireless system, RM-based on UAV-assisted connectivity, such as user throughput estimation, equal resource allocation, interference management, and power us

age, is dependent on the data traffic demand and the ability required to accommodate that capacity.In brief, the following are the motivations for this dissertation's typical development of deep learning (DL) over conventional machine learning (ML) approaches. Deep learning algorithms are being ca

pable of handling large, heterogeneous datasets, are capable of managing the complex and dynamic amount of data, which is crucial for improving the accuracy of model training and testing values. In addition, DL is capable of extracting high-leveled features from the input data automatically and hier

archically. Therefore, it simplifies the automatic extraction of features that are not implemented in conventional ML techniques, which can be helpful for IoT networks based on UAV-assisted environments. Since non-trivial spatial/temporal patterns may be exhibited by the source of data produced from

heterogeneous sources. The IoT communication architecture often produces unlabeled or semi-labeled data in the environment. Useful patterns can be derived from unlabeled data through deep learning approaches. However, if the data feature is labeled and accessible, traditional machine learning app

roaches operate successfully. DL is vital for resource management to reduce the sophistication of time complexity and can accomplish collaborative tasks without retraining the model. The developed design with a multi-agent-based resource management approach focused on the allocation of resources w

ith multi-UAVs in Heterogeneous networks (HetNets) and IoT networks based on the application of deep reinforcement learning (DRL) approach.The key objective of this dissertation is to improve a resource management scheme for future HetNets and IoT networks using the multi-layer perceptron (MLP), lon

g short-term memory (LSTM), gated recurrent unit (GRU), and DRL methods with the use or assistance of UAVs. In particular, (i) evaluating and optimizing the resource management scheme based on DL and DRL approaches; (ii) analyzing and evaluating user throughput; (iii) optimizing the maximization of

throughput with the UAV positioning technique. Then, for the clustering of urban, suburban, and rural properties of the actual data collection area, a clustering algorithm (i.e. K-means) was used. The clustering task is implemented using signal distribution and fluctuation considerations. The resour

ces are considered as bandwidth, frequency band, time slot, user throughput values, the positions of UAVs and IoT users, the heights and altitude from users to UAVs, signal-interference-to-noise-ratio (SINR), the groups of line-of-sight (LOS), non-line-of-sight (NLoS) access links, and power transmi

ssion and consumption issues are used for RM scheme. Then, to analyze, evaluate system performance and the main proposed method development with long short-term memory, the gated recurrent unit, and DRL was used. In short, the DL approaches (i.e., MLP, LSTM, GRU) and DRL are applied as a proposed me

thod to train and predict the improvement system layout construction to investigate the RM scheme. The suggested scheme is then integrated with a round-robin (RR) technique for scheduling user service requests in resource queue management. Finally, the TensorFlow (Python) programming tool is used to

assess the overall capability of the suggested method.The dissertation's main contribution is to examine and analyze the resource management challenge to better allocate resources. Furthermore, the proposed method allows for the best possible A2G link access user throughput efficiency by utilizing

the least amount of transmission power and SINR. We have made use of actual data collection functionality. The justification for taking into account various terrestrial mobile users and UAVs with diverse environmental characteristics such as suburban, urban, and rural areas, is that the upcoming 5G

HetNets would have a heterogeneous IoT user. Therefore, the key contributions can be summarized as follows:(1) The deep learning methods (i.e., MLP and LSTM) are being used to system model and forecast the locations of UAVs to maximize user throughput. The LSTM–GRU methodology is then used for the

analysis and evaluation of throughput with different environmental features. This allows researchers to compare and contrast the throughput adaptability of deep learning methods to conventional approaches.(2) Build a system model that connects IoT users, UAV-BS, and A2G channel access links. Then,

Formulate the resource management issue using this framework, taking into account several limitations such as the number of users, channel gains, interference problems, and power consumption rates. Since these variables are changeable, it is critical to describe the heterogeneous and complex existe

nce of the environment at each time slot.(3) To solve the optimization of joint resource management, a multi-agent DRL is applied to the development of the main system model and for high-dimensional datasets. First, on the optimization of a UAV-assisted wireless IoT network-based resource managemen

t scheme. Second, it is proposed to optimize the resources in three stages; (i.e. throughput estimation, minimizing the SINR, and power usage). Third, to manage the service request queue for IoT users, the round-robin scheduling algorithm was used. This makes our system more computationally efficien

t and stable. Finally, the proposed method was compared and test with previous related works. For light traffic, and heavy traffic, DRL is used to handle traffic-introduced huge variables. With the aid of the considered approach, this work addresses two main problems of fair resource allocation:1) a

llocating network slices to the users dynamically and 2) balancing resource blocks or quality-of-service (QoS).(4) Finally, this dissertation introduces an easy and computationally efficient method to maintain several mobile users in a dynamic environment within the transmission scope of the UAVs.

To diminish the computational complexity, orthogonal frequency is considered. Ultimately, with UAV altitude and user distance dissemination, analyze the resources such as user throughput. The evaluation of the system is carried out through various testing scenarios that helped us to create a tough m

ethod that can be easily adapted to potential dynamic network applications.According to the evaluation metrics, our proposed approach demonstrates a promising outcome based on the experimental results. Because of its low computational complexity, the proposed solution converges quickly on classifica

tion and regression evaluation tasks, making it suitable for heterogeneous IoT networks. The system's reliability is related to diverse situations and the outcomes illustrate that the proposed hybrid MLP-LSTM, LSTM–GRU, and DRL models have stable and encouraging value for future 5G HetNets resource

management. Finally, the proposed approach was evaluated and compared using root means square error (RMSE), mean average percentage error (MAPE), mean square error (MSE) evaluation metrics with previous related works.

台灣格特氏隱球菌分子流行病學調查、藥劑感受性及致病性之研究

為了解決Black Out Azari的問題，作者林國璽 這樣論述：

隱球菌病是一種潛在的致命性疾病，主要由新生隱球菌(Cryptococcus neoformans species complex)或格特氏隱球菌(C. gattii SC)引起。這個疾病通常會影響免疫力低下的患者。隱球菌病在北美洲爆發後，人們開始對於C. gattii SC所引起的感染提高關注。然而，因為由C. gattii SC 引起的人類隱球菌病相當罕見，此一特性限制了對這種真菌病原體的研究。因此，研究環境菌株是瞭解C. gattii SC 的另一種方式。此研究利用一個新設計的選擇性培養基的兩階段培養，在不受到共存絲狀真菌干擾情況下從台灣的環境中分離出了C. gattii SC 菌株。C

. gattii SC 的菌株可以在台灣的樹木棲地中被分離出來。此次分離菌株共鑑定出7個序列型(sequence type)，4個存在VGI 群、3個存在VGII 群。根據分子流行病學和地理上分佈，臨床菌株與環境菌株為密切相關聯。親緣關係分析指出，台灣的C. gattii SC 菌株可能與南美和南亞的菌株同源。抗真菌藥物感受性試驗顯示，不同序列型的菌株之間的抗真菌藥物感受性存在顯著差異。致病性和毒力試驗則顯示，所有環境菌株均具有致病性，且各序列型菌株之間的毒力具有差異顯著。而在此研究中首次鑑定出的ST 630 菌株在VGI 群的各序列型中有著最低的抗真菌藥物感受性及最高的毒力。本研究中新設計出

的兩階段培養方法提供了一個有效的方法來研究C. gattii SC 的環境菌株。而本研究的結果也提供了關於台灣C. gattii SC 的分子流行病學、抗真菌藥物感受性及毒力的重要資訊。