Source Location of Forced Oscillations Using Synchrophasor and SCADA Data

Recent advances in synchrophasor based oscillation monitoring algorithms have allowed engineers to detect oscillation issues that may have previously gone undetected. Although such an oscillation can be flagged and its oscillation shape can indicate the general vicinity of its source, low number of synchrophasors means that a specific generator or load that is the root cause of an oscillation cannot easily be pinpointed. Fortunately, SCADA serves as a much more readily available telemetered source of data if only at a relatively low sampling rate of 1 sample every 1 to 10 seconds. This paper shows that it is possible to combine synchrophasor and SCADA data for effective source location of forced oscillations. For multiple recent oscillation events, the proposed automatic methods were successful in correct identification of the oscillation source which was confirmed in each case by discussion with respective generation plant owners

Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic

Learning high-quality Q-value functions plays a key role in the success of many modern off-policy deep reinforcement learning (RL) algorithms. Previous works focus on addressing the value overestimation issue, an outcome of adopting function approximators and off-policy learning. Deviating from the common viewpoint, we observe that Q-values are indeed underestimated in the latter stage of the RL training process, primarily related to the use of inferior actions from the current policy in Bellman updates as compared to the more optimal action samples in the replay buffer. We hypothesize that this long-neglected phenomenon potentially hinders policy learning and reduces sample efficiency. Our insight to address this issue is to incorporate sufficient exploitation of past successes while maintaining exploration optimism. We propose the Blended Exploitation and Exploration (BEE) operator, a simple yet effective approach that updates Q-value using both historical best-performing actions and the current policy. The instantiations of our method in both model-free and model-based settings outperform state-of-the-art methods in various continuous control tasks and achieve strong performance in failure-prone scenarios and real-world robot tasks

Deep N-ary Error Correcting Output Codes

Ensemble learning consistently improves the performance of multi-class classification through aggregating a series of base classifiers. To this end, data-independent ensemble methods like Error Correcting Output Codes (ECOC) attract increasing attention due to its easiness of implementation and parallelization. Specifically, traditional ECOCs and its general extension N-ary ECOC decompose the original multi-class classification problem into a series of independent simpler classification subproblems. Unfortunately, integrating ECOCs, especially N-ary ECOC with deep neural networks, termed as deep N-ary ECOC, is not straightforward and yet fully exploited in the literature, due to the high expense of training base learners. To facilitate the training of N-ary ECOC with deep learning base learners, we further propose three different variants of parameter sharing architectures for deep N-ary ECOC. To verify the generalization ability of deep N-ary ECOC, we conduct experiments by varying the backbone with different deep neural network architectures for both image and text classification tasks. Furthermore, extensive ablation studies on deep N-ary ECOC show its superior performance over other deep data-independent ensemble methods.Comment: EAI MOBIMEDIA 202

RoboCoDraw: Robotic Avatar Drawing with GAN-based Style Transfer and Time-efficient Path Optimization

Robotic drawing has become increasingly popular as an entertainment and interactive tool. In this paper we present RoboCoDraw, a real-time collaborative robot-based drawing system that draws stylized human face sketches interactively in front of human users, by using the Generative Adversarial Network (GAN)-based style transfer and a Random-Key Genetic Algorithm (RKGA)-based path optimization. The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm. A core component in this system is the Avatar-GAN proposed by us, which generates a cartoon avatar face image from a real human face. AvatarGAN is trained with unpaired face and avatar images only and can generate avatar images of much better likeness with human face images in comparison with the vanilla CycleGAN. After the avatar image is generated, it is fed to a line extraction algorithm and converted to sketches. An RKGA-based path optimization algorithm is applied to find a time-efficient robotic drawing path to be executed by the robotic arm. We demonstrate the capability of RoboCoDraw on various face images using a lightweight, safe collaborative robot UR5.Comment: Accepted by AAAI202

Розробка модуля Ethernet контролю для дистанційного керування електроживильною установкою

Sound processing in the inner ear involves separation of the constituent frequencies along the length of the cochlea. Frequencies relevant to human speech (100 to 500 Hz) are processed in the apex region. Among mammals, the guinea pig cochlear apex processes similar frequencies and is thus relevant for the study of speech processing in the cochlea. However, the requirement for extensive surgery has challenged the optical accessibility of this area to investigate cochlear processing of signals without significant intrusion. A simple method is developed to provide optical access to the guinea pig cochlear apex in two directions with minimal surgery. Furthermore, all prior vibration measurements in the guinea pig apex involved opening an observation hole in the otic capsule, which has been questioned on the basis of the resulting changes to cochlear hydrodynamics. Here, this limitation is overcome by measuring the vibrations through the unopened otic capsule using phase-sensitive Fourier domain optical coherence tomography. The optically and surgically advanced method described here lays the foundation to perform minimally invasive investigation of speech-related signal processing in the cochlea. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.Funding Agencies|NIH NIDCD [R01DC000141]; NIH [R01DC004554, R01DC010201, R01DC011796]; Swedish Research Council [K2014-63X-14061-14-5]; Torsten Soderberg Foundation</p

Auggie: Encouraging Effortful Communication through Handcrafted Digital Experiences

Digital communication is often brisk and automated. From auto-completed messages to "likes," research has shown that such lightweight interactions can affect perceptions of authenticity and closeness. On the other hand, effort in relationships can forge emotional bonds by conveying a sense of caring and is essential in building and maintaining relationships. To explore effortful communication, we designed and evaluated Auggie, an iOS app that encourages partners to create digitally handcrafted Augmented Reality (AR) experiences for each other. Auggie is centered around crafting a 3D character with photos, animated movements, drawings, and audio for someone else. We conducted a two-week-long field study with 30 participants (15 pairs), who used Auggie with their partners remotely. Our qualitative findings show that Auggie participants engaged in meaningful effort through the handcrafting process, and felt closer to their partners, although the tool may not be appropriate in all situations. We discuss design implications and future directions for systems that encourage effortful communication.Comment: To appear at the 25th ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW '22). 25 page

HBV infection-induced liver cirrhosis development in dual-humanized mice with human bone mesenchymal stem cell transplantation

疾病动物模型是现代医学发展的基石，尤其是重大、突发传染病暴发时，适宜的疾病动物模型可为及时发现病原体、制定防控策略提供强大保障，原创的疾病动物模型已成为衡量一个国家生物医药科研水平的标志。我校夏宁邵教授团队和浙江大学附属第一医院李君教授团队历经5年的协同攻关，终于建立了国际上首个高度模拟人类乙肝病毒（HBV）自然感染诱发的慢乙肝肝硬化小鼠模型。厦门大学公共卫生学院袁伦志博士生、浙江大学医学院附属第一医院江静博士和厦门大学公共卫生学院刘旋博士生为该论文共同第一作者。厦门大学夏宁邵教授、浙江大学附属第一医院李君教授和厦门大学程通副教授为该论文共同通讯作者。【Abstract】Objective： Developing a small animal model that accurately delineates the natural history of hepatitis B virus (HBV) infection and immunopathophysiology is necessary to clarify the mechanisms of host-virus interactions and to identify intervention strategies for HBV-related liver diseases. This study aimed to develop an HBV-induced chronic hepatitis and cirrhosis mouse model through transplantation of human bone marrow mesenchymal stem cells (hBMSCs). Design： Transplantation of hBMSCs into Fah -/- Rag2 -/- IL-2Rγc -/- SCID (FRGS) mice with fulminant hepatic failure (FHF) induced by hamster-anti-mouse CD95 antibody JO2 generated a liver and immune cell dual-humanized (hBMSC-FRGS) mouse. The generated hBMSC-FRGS mice were subjected to assessments of sustained viremia, specific immune and inflammatory responses and liver pathophysiological injury to characterize the progression of chronic hepatitis and cirrhosis after HBV infection. Results： The implantation of hBMSCs rescued FHF mice, as demonstrated by robust proliferation and transdifferentiation of functional human hepatocytes and multiple immune cell lineages, including B cells, T cells, NK cells, dendritic cells (DCs) and immune cell lineages, including B cells, T cells, NK cells, dendritic cells (DCs) and viremia and specific immune and inflammatory responses and showed progression to chronic hepatitis and liver cirrhosis at a frequency of 55% after 54 weeks. Conclusion： This new humanized mouse model recapitulates the liver cirrhosis induced by human HBV infection, thus providing research opportunities for understanding viral immune pathophysiology and testing antiviral therapies in vivo.this work was supported by the national Science and technology Major Project (grant nos. 2017ZX10304402, 2017ZX10203201 and 2018ZX09711003-005-003), the national natural Science Foundation of china(grant nos. 81672023, 81571818 and 81771996), the Scientific research Foundation of the State Key laboratory of Molecular Vaccinology and Molecular Diagnostics (grant no 2016ZY005), Zhejiang Province and State's Key Project of the research and Development Plan of china (grant nos 2017c01026 and 2016YFc1101304/3).该研究获得了传染病防治国家科技重大专项、新药创制国家科技重大专项和国家自然科学基金的资助