得獎論文
IEEE最佳論文獎
- 期刊名稱：
IEEE ICEIB 2023 - International Conference on Electronic Communications, Internet of Things and Big Data
- 作者姓名：
高曉雯
- 論文摘要：
Handover (HO) is the key function to maintaining users’ connections while moving within the coverage of cellular communication networks. During the HO process, it is possible to decrease the data throughput and cause interruptions of time-critical services. In addition, the HO signal processes between the mobile phone and the mobile network increase energy consumption for both of them. These problems are even more complex in the 5G era because of the co-existence of macro- and micro-cell (ultra-dense small cells), and different deployment architectures, such as non-standalone (NSA) and standalone (SA). To investigate HO behaviors in real 5G networks, we collected a rich mobile signal dataset consisting of 2 sets of repeated driving trips under a 5G commercial NSA network in Taoyuan, Taiwan. Based on the dataset, we analyze and compare the HO sequential frequent patterns and the probability of the occurrence of ping-pong HO for the 2 driving routes. The results show the HO frequent patterns with high support can be found, and several ping-pong events occur repeatedly at the same position. Several observations are described to discuss the value of utilizing the dataset and design AI-assisted HO algorithms for decreasing ping-pong effects and unnecessary HO events.
SCI接受發表論文(2022-2024)
- 期刊名稱：
Computer Science and Information Systems
- 作者姓名：
儲韶廷(智聯所)
- 論文摘要：
In this paper, a robust compensation scheme using adaptive fuzzy Hermite neural networks (RCAFHNN), for use in synchronous reluctance motors (SRMs), is proposed. SRMs have a simple underlying mathematical model and mechanical structure, but are affected by problems related to parameter ariations, external interference, and nonlinear dynamics. In many fields, precise control of motors is required. Although the use of neural network and fuzzy are widespread, such controllers are affected by unbound nonlinear system model. In this study, RCAFHNN, based on an adaptive neural fuzzy interface system (ANFIS), was used to bound motor system model controller algorithm. RCAFHNN can be characterized in three parts. First, RCAFHNN offers fuzzy expert knowledge, a neural network for online estimation, and recursive weight estimation. Second, the replacement of the Gaussian function by the Hermite polynomial in RCAFHNN enables reduced membership function training times. Third, the system convergence and robustness compensation of RCAFHNN were confirmed using Lyapunov stability.
- 期刊名稱：
Optics Letters
- 作者姓名：
張博程(前瞻所)
- 論文摘要：
We present a scheme to precisely resolve the unperturbed line shape of an optical rubidium clock transition in a high vacuum, by which we avoided the systematic errors of “collision shift” and “modulation shift.” The spectral resolution resolved by this scheme is significantly improved such that we can use “Zeeman broadening” to inspect the stray magnetic field, through which we were able to compensate the magnetic field inside the Rb cells to be below 10−3 Gauss. We thus update the absolute frequency of the clock transition and propose a standard operation procedure (SOP) for the clock self-calibration.
- 期刊名稱：
Springer - The Visual Computer Journal
- 作者姓名：
江庭輝
- 論文摘要：
This work considers the video frame inpainting problem, where several former and latter frames are given, and the goal is to predict the middle frames. The state-of-the-art solution has applied bidirectional long short-term memory (LSTM) networks, which has a spatial-temporal mismatch problem. In this paper, we propose a trapezoid-structured LSTM architecture called T-LSTM-sbm for video frame inpainting with three designs: (i) segregated spatial-temporal gates, (ii) bridge joints, and (iii) multi-kernel LSTM. To prevent the spatial-temporal mismatch problem, while features are being passed through multi-layered LSTM nodes, the trapezoid structure reduces its number of LSTM nodes by two after each layer. This makes the model converge to the inpainted results more effectively. The separated temporal and spatial gates design can learn better spatial and temporal features by using individual gates. To relieve the information loss problem during the convergence of the trapezoidal layers, we use bridge joints among layers to better preserve useful information. The multiple kernels in LSTM are to enable extracting multi-scale information flows. T-LSTM-sbm is proved to outperform the state-of-the-art solutions in peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) on three common datasets, KTH Action, HMDB-51, and UCF-101.
- 期刊名稱：
IEEE Wireless Communications
- 作者姓名：
高曉雯
- 論文摘要：
The explosion of mobile applications and phenomenal adoption of mobile connectivity by end-users has generated an increasing amount of mobile data traffic. Application posing stringent network requirements of high bandwidth and low latency (e.g., immersive videos) and the substantial amount of data traffic has put tremendous pressure on existing network infrastructures. Cognizant of the need of increasing network capacity, the simultaneous use of heterogeneous network technologies (HetNet) has been proposed to address this imperative problem. 5G is expected to further drive the concept of HetNet by allowing the use of huge available bandwidth at milli-meter wave frequencies. While HetNet concentrates on improving network capacity from the data transmission perspective, it overlooks the importance of enhancing the support of user’s Quality of Experience (QoE) for the evolving new services. There is a wide range of factors influencing user’s QoE, such as network performance including delay, jitter and throughput, contextual influence such as personalized content delivery, mobility aware content caching and dissemination, and human impact such as human roles and demographic attributes. This paper initially presents a QoE-centric analysis, and evaluation of multimedia services over 5G networks. Then, to address the shortcomings of existing mobile networks, we propose a framework to enhance the support of QoE, to enable smooth delivery of personalized immersive video environment and personalized interaction with an immersive video, anywhere, anytime and on any device. Finally, we propose new solutions to achieve practically feasible spectrum allocation and personalized content caching and dissemination, to provide uninterrupted multimedia services to end-users.
- 期刊名稱：
Engineering Applications of Artificial Intelligence
- 作者姓名：
金聚鈺
- 論文摘要：
Content-based music retrieval has been studied for many years. However, it is not easy to achieve effective and efficient retrieval because two issues such as search strategy and music feature are not considered simultaneously. Therefore, in this paper, we propose an innovative music search method using automated navigations and semantic features to cope with these issues. For automated navigations, it is a novel autonomous-feedback technique that moves the search towards the user interest space effectively and efficiently. For semantic features, the low-level audio features are transformed into high-level semantic features to effectively associate with user concepts. To reveal the performance of the proposed method, we conducted a set of comprehensive evaluations on two real music datasets. In the comparative experiments, semantic features are shown to be more effective than audio features. Additionally, the proposed method is superior to state-of-the-art methods in terms of precision, which indicates the average improvements of 151.67% and 148.02% on two datasets, respectively. Moreover, the subjective evaluation shows that the proposed method can earn the users’ satisfactions in the materialized system. In summary, the proposed automated navigations and semantic features are useful for dealing with issues of search strategy and music feature in content-based music retrieval.
- 期刊名稱：
IEEE Transactions on Signal Processing
- 作者姓名：
張國韋
- 論文摘要：
How to perfectly recover a binary signal from its discrete Fourier transform (DFT) coefficients is studied. The theoretic lower bound and a practical recovery strategy are derived and developed. The concept of ambiguity pair is introduced. This pair of signals has almost the same DFT coefficients except for some positions. It can prove that when the signal length is N, then at least τ (N) DFT coefficients must be sampled, where τ (N) is the number of factors of the signal length N. A recovery algorithm is proposed and implemented. It can achieve the lowed bound for length N = 2m,m ≤ 6. To overcome the length limitation problem, a more practical recovery method is also proposed and implemented for N = 2m,m 大於 6. We can sample 11% of the total DFT coefficients to perfectly recover the binary signal. We also extend the concept of ambiguity pair to other discrete transforms (DCT andWHT) and two-dimensional DFT cases.