Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-M&N schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust

Magnetic shield of PMT used in DAMPE electromagnetic calorimeter

The magnetic characteristics of photomultiplier tube R5610A-01 are studied in this paper. The experimental data shows that the gain of R5610A-01 loses about 53% when the magnetic field is 3G along its +X axis. A cylinder of one-layer permalloy strip is able to reduce the effect of 3G magnetic field on the PMT's gain to less than 1%.Comment: 4 pages, 6 figures, accepted by Chinese Physics

LaunchpadGPT: Language Model as Music Visualization Designer on Launchpad

Launchpad is a musical instrument that allows users to create and perform music by pressing illuminated buttons. To assist and inspire the design of the Launchpad light effect, and provide a more accessible approach for beginners to create music visualization with this instrument, we proposed the LaunchpadGPT model to generate music visualization designs on Launchpad automatically. Based on the language model with excellent generation ability, our proposed LaunchpadGPT takes an audio piece of music as input and outputs the lighting effects of Launchpad-playing in the form of a video (Launchpad-playing video). We collect Launchpad-playing videos and process them to obtain music and corresponding video frame of Launchpad-playing as prompt-completion pairs, to train the language model. The experiment result shows the proposed method can create better music visualization than random generation methods and hold the potential for a broader range of music visualization applications. Our code is available at https://github.com/yunlong10/LaunchpadGPT/.Comment: Accepted by International Computer Music Conference (ICMC) 202

Five-Section Trajectory Design of Thick Glutenite Reservoir in Shengli Oilfield

Many blocks of Shengli Oilfield are located in urban areas, and the site selection of well sites is limited. In order to meet the needs of reservoir development and deployment, five-section trajectory is increasingly used. Difficulty in site selection results in directional well development, and reservoir deployment requires vertical well development. In order to resolve the two contradictions, five-section trajectory is used in the well design, and vertical drilling after hitting the target. The problems with this type of trajectory are high torque drag and easier fatigue of the drilling pipe. When the displacement is small, the effect is small. When the displacement is large, it will cause engineering complexity such as difficulty drilling weight transfer and fatigue of drilling pipe. Aiming at the shortcomings of the five-section trajectory, with the help of existing drill string force analysis software, the parameters of the five-section trajectory were analyzed, and reasonable values were recommended to provide an optimization idea for the five-section trajectory

A study of energy correction for the electron beam data in the BGO ECAL of the DAMPE

The DArk Matter Particle Explorer (DAMPE) is an orbital experiment aiming at searching for dark matter indirectly by measuring the spectra of photons, electrons and positrons originating from deep space. The BGO electromagnetic calorimeter is one of the key sub-detectors of the DAMPE, which is designed for high energy measurement with a large dynamic range from 5 GeV to 10 TeV. In this paper, some methods for energy correction are discussed and tried, in order to reconstruct the primary energy of the incident electrons. Different methods are chosen for the appropriate energy ranges. The results of Geant4 simulation and beam test data (at CERN) are presented

PRE+: dual of proxy re-encryption for secure cloud data sharing service

With the rapid development of very large, diverse, complex, and distributed datasets generated from internet transactions, emails, videos, business information systems, manufacturing industry, sensors and internet of things etc., cloud and big data computation have emerged as a cornerstone of modern applications. Indeed, on the one hand, cloud and big data applications are becoming a main driver for economic growth. On the other hand, cloud and big data techniques may threaten people and enterprises’ privacy and security due to ever increasing exposure of their data to massive access. In this paper, aiming at providing secure cloud data sharing services in cloud storage, we propose a scalable and controllable cloud data sharing framework for cloud users (called: Scanf). To this end, we introduce a new cryptographic primitive, namely, PRE+, which can be seen as the dual of traditional proxy re-encryption (PRE) primitive. All the traditional PRE schemes until now require the delegator (or the delegator and the delegatee cooperatively) to generate the re-encryption keys. We observe that this is not the only way to generate the re-encryption keys, the encrypter also has the ability to generate re-encryption keys. Based on this observation, we construct a new PRE+ scheme, which is almost the same as the traditional PRE scheme except the re-encryption keys generated by the encrypter. Compared with PRE, our PRE+ scheme can easily achieve the non-transferable property and message-level based fine-grained delegation. Thus our Scanf framework based on PRE+ can also achieve these two properties, which is very important for users of cloud storage sharing service. We also roughly evaluate our PRE+ scheme’s performance and the results show that our scheme is efficient and practica for cloud data storage applications.Peer ReviewedPostprint (author's final draft

A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification

Cross-domain text classification aims to adapt models to a target domain that lacks labeled data. It leverages or reuses rich labeled data from the different but related source domain(s) and unlabeled data from the target domain. To this end, previous work focuses on either extracting domain-invariant features or task-agnostic features, ignoring domain-aware features that may be present in the target domain and could be useful for the downstream task. In this paper, we propose a two-stage framework for cross-domain text classification. In the first stage, we finetune the model with mask language modeling (MLM) and labeled data from the source domain. In the second stage, we further fine-tune the model with self-supervised distillation (SSD) and unlabeled data from the target domain. We evaluate its performance on a public cross-domain text classification benchmark and the experiment results show that our method achieves new state-of-the-art results for both single-source domain adaptations (94.17% $\uparrow$1.03%) and multi-source domain adaptations (95.09% $\uparrow$1.34%)