Design of a large dynamic range readout unit for the PSD detector of DAMPE

A large dynamic range is required by the Plastic Scintillator Detector (PSD) of DArk Matter Paricle Explorer (DAMPE), and a double-dynode readout has been developed. To verify this design, a prototype detector module has been constructed and tested with cosmic rays and heavy ion beams. The results match with the estimation and the readout unit could easily cover the required dynamic range

Laser tuning parameters and concentration retrieval technique for wavelength modulation spectroscopy based on the variable-radius search artificial bee colony algorithm

A novel wavelength modulation spectroscopy (WMS) laser tuning parameters and concentration retrieval technique based on the variable-radius-search artificial bee colony(VRS-ABC) algorithm is proposed. The technique imitates the foraging behavior of bees to achieve the retrieval of gas concentration and laser tuning parameters in a calibration-free WMS system. To address the problem that the basic artificial bee colony(ABC) algorithm tends to converge prematurely, we improve the search method of the scout bee. In contrast to prior concentration retrieval methods that utilized the Levenberg-Marquardt algorithm, the current technique exhibits a reduced dependence on the pre-characterization of laser parameters, leading to heightened precision and reliability in concentration retrieval. We validated the simulation with the VRS-ABC-based technique and the LM-based technique for the target gas C2H2. The simulation results show that the VRS-ABC-based technique performs better in terms of convergence speed and fitting accuracy, especially in the multi-parameter model without exact characterization

Concentration retrieval in a calibration-free wavelength modulation spectroscopy system using particle swarm optimization algorithm

This paper develops a spectral fitting technology based on the particle swarm optimization (PSO) algorithm, which is applied to a calibration-free wavelength modulation spectroscopy system to achieve concentration retrieval. As compared with other spectral fitting technology based on the Levenberg-Marquardt (LM) algorithm, this technology is relatively weakly dependent on the pre-characterization of the laser parameters. The gas concentration is calculated by fitting the simulated spectra to the measured spectra using the PSO algorithm. We validated the simulation with the LM algorithm and PSO algorithm for the target gas C2H2. The results showed that the convergence speed of the spectral fitting technique based on the PSO algorithm was about 63 times faster than the LM algorithm when the fitting accuracy remained the same. Within 5 seconds, the PSO algorithm can produce findings that are generally consistent with the values anticipated.Comment: arXiv admin note: text overlap with arXiv:2210.1654

Simulation of the Signal Propagation for Thin-gap RPC in the ATLAS Phase-II Upgrade

Thin-gap Resistive Plate Chambers (RPCs) with a 1 mm gap size are introduced in the Phase-II ATLAS upgrade. Smaller avalanche charge due to the reduced gap size raises concerns for signal integrity. This work focuses on the RPC signal propagation process in lossless conditions, and an analytical study is implemented for the ATLAS RPC. Detector modeling is presented, and the simulation of the RPC signal is discussed in detail. Simulated characteristic impedance and crosstalk have been compared with the measured value to validate this model. This method is applied to different RPC design geometries, including the newly proposed $\eta-\eta$ readout scheme.Comment: 6 pages, 5 figures, submitted to NIM

Design and experiment of Panax notoginseng root orientation transplanting device based on YOLOv5s

Consistent root orientation is one of the important requirements of Panax notoginseng transplanting agronomy. In this paper, a Panax notoginseng orientation transplanting method based on machine vision technology and negative pressure adsorption principle was proposed. With the cut-main root of Panax notoginseng roots as the detection object, the YOLOv5s was used to establish a root feature detection model. A Panax notoginseng root orientation transplanting device was designed. The orientation control system identifies the root posture according to the detection results and controls the orientation actuator to adjust the root posture. The detection results show that the precision rate of the model was 94.2%, the recall rate was 92.0%, and the average detection precision was 94.9%. The Box-Behnken experiments were performed to investigate the effects of suction plate rotation speed, servo rotation speed and the angle between the camera and the orientation actuator(ACOA) on the orientation qualification rate and root drop rate. Response surface method and objective optimisation algorithm were used to analyse the experimental results. The optimal working parameters were suction plate rotation speed of 5.73 r/min, servo rotation speed of 0.86 r/s and ACOA of 35°. Under this condition, the orientation qualification rate and root drop rate of the actual experiment were 89.87% and 6.57%, respectively, which met the requirements of orientation transplanting for Panax notoginseng roots. The research method of this paper is helpful to solve the problem of orientation transplanting of other root crops

EVNet: An Explainable Deep Network for Dimension Reduction

Dimension reduction (DR) is commonly utilized to capture the intrinsic structure and transform high-dimensional data into low-dimensional space while retaining meaningful properties of the original data. It is used in various applications, such as image recognition, single-cell sequencing analysis, and biomarker discovery. However, contemporary parametric-free and parametric DR techniques suffer from several significant shortcomings, such as the inability to preserve global and local features and the pool generalization performance. On the other hand, regarding explainability, it is crucial to comprehend the embedding process, especially the contribution of each part to the embedding process, while understanding how each feature affects the embedding results that identify critical components and help diagnose the embedding process. To address these problems, we have developed a deep neural network method called EVNet, which provides not only excellent performance in structural maintainability but also explainability to the DR therein. EVNet starts with data augmentation and a manifold-based loss function to improve embedding performance. The explanation is based on saliency maps and aims to examine the trained EVNet parameters and contributions of components during the embedding process. The proposed techniques are integrated with a visual interface to help the user to adjust EVNet to achieve better DR performance and explainability. The interactive visual interface makes it easier to illustrate the data features, compare different DR techniques, and investigate DR. An in-depth experimental comparison shows that EVNet consistently outperforms the state-of-the-art methods in both performance measures and explainability.Comment: 18 pages, 15 figures, accepted by TVC