Kane Method Based Dynamics Modeling and Control Study for Space Manipulator Capturing a Space Target

Dynamics modeling and control problem of a two-link manipulator mounted on a spacecraft (so-called carrier) freely flying around a space target on earth’s circular orbit is studied in the paper. The influence of the carrier’s relative movement on its manipulator is considered in dynamics modeling; nevertheless, that of the manipulator on its carrier is neglected with the assumption that the mass and inertia moment of the manipulator is far less than that of the carrier. Meanwhile, we suppose that the attitude control system of the carrier guarantees its side on which the manipulator is mounted points accurately always the space target during approaching operation. The ideal constraint forces can be out of consideration in dynamics modeling as Kane method is used. The path functions of the manipulator’s end-effector approaching the space target as well as the manipulator’s joints control torque functions are programmed to meet the soft touch requirement that the end-effector’s relative velocity to the space target is zero at touch moment. Numerical simulation validation is conducted finally

Dynamic Acoustic Compensation and Adaptive Focal Training for Personalized Speech Enhancement

Recently, more and more personalized speech enhancement systems (PSE) with excellent performance have been proposed. However, two critical issues still limit the performance and generalization ability of the model: 1) Acoustic environment mismatch between the test noisy speech and target speaker enrollment speech; 2) Hard sample mining and learning. In this paper, dynamic acoustic compensation (DAC) is proposed to alleviate the environment mismatch, by intercepting the noise or environmental acoustic segments from noisy speech and mixing it with the clean enrollment speech. To well exploit the hard samples in training data, we propose an adaptive focal training (AFT) strategy by assigning adaptive loss weights to hard and non-hard samples during training. A time-frequency multi-loss training is further introduced to improve and generalize our previous work sDPCCN for PSE. The effectiveness of proposed methods are examined on the DNS4 Challenge dataset. Results show that, the DAC brings large improvements in terms of multiple evaluation metrics, and AFT reduces the hard sample rate significantly and produces obvious MOS score improvement

STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19

Human mobility estimation is crucial during the COVID-19 pandemic due to its significant guidance for policymakers to make non-pharmaceutical interventions. While deep learning approaches outperform conventional estimation techniques on tasks with abundant training data, the continuously evolving pandemic poses a significant challenge to solving this problem due to data nonstationarity, limited observations, and complex social contexts. Prior works on mobility estimation either focus on a single city or lack the ability to model the spatio-temporal dependencies across cities and time periods. To address these issues, we make the first attempt to tackle the cross-city human mobility estimation problem through a deep meta-generative framework. We propose a Spatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model that estimates dynamic human mobility responses under a set of social and policy conditions related to COVID-19. Facilitated by a novel spatio-temporal task-based graph (STTG) embedding, STORM-GAN is capable of learning shared knowledge from a spatio-temporal distribution of estimation tasks and quickly adapting to new cities and time periods with limited training samples. The STTG embedding component is designed to capture the similarities among cities to mitigate cross-task heterogeneity. Experimental results on real-world data show that the proposed approach can greatly improve estimation performance and out-perform baselines.Comment: Accepted at the 22nd IEEE International Conference on Data Mining (ICDM 2022) Full Pape

Bis[2-(benzyl­idene­amino)­phen­yl] disulfide

In the title mol­ecule, C26H20N2S2, the two benzene rings connected by a disulfide chain form a dihedral angle of 84.9 (1)°, and the two benzene rings in the two benzyl­idene­amino­phenyl fragments form dihedral angles of 34.4 (1) and 32.8 (1)°. The crystal structure exhibits weak inter­molecular C—H⋯S hydrogen bonds, which link the mol­ecules into chains along [101]

Cross-view Semantic Alignment for Livestreaming Product Recognition

Live commerce is the act of selling products online through live streaming. The customer's diverse demands for online products introduce more challenges to Livestreaming Product Recognition. Previous works have primarily focused on fashion clothing data or utilize single-modal input, which does not reflect the real-world scenario where multimodal data from various categories are present. In this paper, we present LPR4M, a large-scale multimodal dataset that covers 34 categories, comprises 3 modalities (image, video, and text), and is 50x larger than the largest publicly available dataset. LPR4M contains diverse videos and noise modality pairs while exhibiting a long-tailed distribution, resembling real-world problems. Moreover, a cRoss-vIew semantiC alignmEnt (RICE) model is proposed to learn discriminative instance features from the image and video views of the products. This is achieved through instance-level contrastive learning and cross-view patch-level feature propagation. A novel Patch Feature Reconstruction loss is proposed to penalize the semantic misalignment between cross-view patches. Extensive experiments demonstrate the effectiveness of RICE and provide insights into the importance of dataset diversity and expressivity. The dataset and code are available at https://github.com/adxcreative/RICEComment: Accepted to ICCV202

Perancangan Planogram Berdasarkan Merchandise Hierarchy Dan Category Management Di Ritel X

Penelitian dilakukan di Ritel X yang menjual produk-produk tekstil garmen seperti kemeja batik, blus batik, daster, baju anak laki-laki, baju anak perempuan dan sarung serta produk tekstil rumah tangga seperti sprei, selimut dan bed cover. Berdasarkan observasi yang dilakukan, produk-produk di Ritel X tidak tertata dengan baik dan belum melakukan perancangan tampilan produk yang mengakibatkan sulitnya menemukan produk-produk yang diinginkan konsumen. Peneliti akan membuat rancangan planogram berdasarkan category management dan merchandise hierarchy dari produk-produk yang ada di Ritel X. Pembuatan kategori produk dalam category management digunakan untuk mengetahui keseluruhan stock keeping unit dari produk-produk di Ritel X yang harus dirancang dalam planogram. Penetapan merchandise hierarchy dilakukan dengan cara melakukan wawancara secara langsung dengan para konsumen di Ritel X. Selain category management dan merchandise hierarchy dalam merancang planogram juga mempertimbangkan margin profit produk-produk. Hasil perancangan planogram diharapkan akan memberi dasar dalam menata tampilan produk di Ritel X agar lebih menarik minat kosumen dan meningkatkan profit penjualan