On the Outage Probability of Localization in Randomly Deployed Wireless Networks

This paper analyzes the localization outage probability (LOP), the probability that the position error exceeds a given threshold, in randomly deployed wireless networks. Two typical cases are considered: a mobile agent uses all the neighboring anchors or select the best pair of anchors for self-localization. We derive the exact LOP for the former case and tight bounds for the LOP for the latter case. The comparison between the two cases reveals the advantage of anchor selection in terms of LOP versus complexity tradeoff, providing insights into the design of efficient localization systems

Differential Privacy of Aggregated DC Optimal Power Flow Data

We consider the problem of privately releasing aggregated network statistics obtained from solving a DC optimal power flow (OPF) problem. It is shown that the mechanism that determines the noise distribution parameters are linked to the topology of the power system and the monotonicity of the network. We derive a measure of "almost" monotonicity and show how it can be used in conjunction with a linear program in order to release aggregated OPF data using the differential privacy framework.Comment: Accepted by 2019 American Control Conference (ACC

The Impact of Vocabulary Assessment and Personalized Feedback on Students’ Vocabulary Mastery

This review explores the synergy between vocabulary assessment and personalized feedback in supporting students’ vocabulary mastery and enhancing their learning experiences. Vocabulary plays a crucial role in academic success, serving as the cornerstone of comprehension and communication. Therefore, accurate vocabulary assessment and effective feedback mechanisms are imperative. The paper outlines the significance of individualized learning, emphasizing the need to recognize students’ unique learning styles and tailor feedback accordingly, and discusses the transformative role of technology in facilitating innovative assessment and feedback approaches. However, the implementation of these approaches encounters various challenges, including technical barriers, logistical hurdles, and resistance from educators and students. The current body of research, while insightful, also presents limitations such as restricted scope, scale, and unaddressed gaps in knowledge. Despite these challenges, the integration of vocabulary assessment and personalized feedback offers promising prospects for enhancing students’ learning outcomes and motivation. Future research needs to focus on overcoming existing challenges and expanding the understanding of this integrative educational approach to benefit diverse student populations

マダイ稚魚の成長、消化率、消化管形態、サイトカイン遺伝子の発現に対する低・無魚粉飼料へのタウリンの添加効果に関する研究

東京海洋大学博士学位論文 2019年度(2020年3月) 応用生物科学 課程博士 甲第536号指導教員:佐藤秀一東京海洋大学201

The Impact of Vocabulary Learning Methods on Students’ Vocabulary Application Skills

This research paper investigates the impact of vocabulary learning methods on students’ vocabulary application skills in English language acquisition. I examine traditional methods like rote memorization and flashcards, as well as modern approaches such as contextual learning, technology-assisted methods, and multimodal strategies. Through a mixed-methods research design, including surveys, interviews, and classroom observations, I uncover valuable insights into how these methods influence vocabulary application. My findings reveal that traditional methods, while effective for vocabulary retention, often fall short in facilitating practical vocabulary usage. Contextual learning emerges as a potent strategy, promoting active vocabulary application by immersing learners in real-life language contexts. Technology-assisted methods enhance pronunciation and offer immersive experiences, contributing to improved vocabulary application. Multimodal approaches that integrate various methods provide a holistic solution, fostering both recognition and active use of vocabulary. The implications for language teaching emphasize the need for a balanced approach that combines traditional and modern methods. Incorporating technology and real-life contexts into language education enhances students’ ability to apply vocabulary effectively, bridging the gap between knowledge and application

Experimental investigation and thermal modelling of slot cooling improvement for electrical machines

Intensive transport electrification is key to meet the future increasingly stringent emission targets, with legislations spanning all forms of transport including automotive, aerospace, marine and rail. The electrical machine is at the heart of all the electrified transport architectures, and hence improving its performance metrics, being it power density (kW/kg, kW/L), efficiency or cost performance ($/kW) is critical to increase the proliferation of cleaner, greener technologies. Thermal improvements are quite important in improving the performance metrics of electrical machines used for transport, and this research focuses on the aforesaid aspects while keeping a multi-domain perspective. Taking as a case study an existing Interior Permanent Magnet Synchronous Machine used for an EV traction application, firstly thermal models are built and experimentally validated. The thermal models are then used to conduct sensitivity analysis on the constituent elements, from which it is determined that the slot number and the effective slot thermal conductivity are important aspects which merit looking into more detail within this research. By conducting multi-domain studies, including electromagnetic and thermal aspects, the optimal slot number is investigated and experimentally validated, with guidelines provided on the selection of this parameter for temperature reduction for different stator sizes. Subsequently a novel, low-cost, effective way to improve the thermal performance of concentrated-wound electrical machines is proposed by extending a part of the back-iron extension into the slot, with the invention named ‘Back Iron Extension’ (BIE). Comprehensive modeling and experimental validation of BIE is conducted, with a 26.7% peak temperature reduction demonstrated, and general guidelines on its sizing are also provided. The simplicity of the BIE, which requires no additional costly materials and which can be implemented within the lamination punching process make it a strong candidate to be used with the next generation of high power density, high cost-performance electrical machines

3D Reconstruction of Optical Building Images Based on Improved 3D-R2N2 Algorithm

Three-dimensional reconstruction technology is a key element in the construction of urban geospatial models. Addressing the current shortcomings in reconstruction accuracy, registration results convergence, reconstruction effectiveness, and convergence time of 3D reconstruction algorithms, we propose an optical building object 3D reconstruction method based on an improved 3D-R2N2 algorithm. The method inputs preprocessed optical remote sensing images into a Convolutional Neural Network (CNN) with dense connections for encoding, converting them into a low-dimensional feature matrix and adding a residual connection between every two convolutional layers to enhance network depth. Subsequently, 3D Long Short-Term Memory (3D-LSTM) units are used for transitional connections and cyclic learning. Each unit selectively adjusts or maintains its state, accepting feature vectors computed by the encoder. These data are further passed into a Deep Convolutional Neural Network (DCNN), where each 3D-LSTM hidden unit partially reconstructs output voxels. The DCNN convolutional layer employs an equally sized 3 3 3 convolutional kernel to process these feature data and decode them, thereby accomplishing the 3D reconstruction of buildings. Simultaneously, a pyramid pooling layer is introduced between the feature extraction module and the fully connected layer to enhance the performance of the algorithm. Experimental results indicate that, compared to the 3D-R2N2 algorithm, the SFM-enhanced AKAZE algorithm, the AISI-BIM algorithm, and the improved PMVS algorithm, the proposed algorithm improves the reconstruction effect by 5.3%, 7.8%, 7.4%, and 1.0% respectively. Furthermore, compared to other algorithms, the proposed algorithm exhibits higher efficiency in terms of registration result convergence and reconstruction time, with faster computational speed. This research contributes to the enhancement of building 3D reconstruction technology, laying a foundation for future research in deep learning applications in the architectural field