arXiv subjects

Jianrong Zhou

Publications and source records attributed to Jianrong Zhou.

18 recordsLinked to original sources

arXiv2026

Development of Readout Electronics for a High-Speed Event-Driven Neutron Imaging Detector Based on Timepix4

As the Chinese Spallation Neutron Source enters Phase II, the increase in proton beam power will lead to a further boost in the intensity of pulsed neutron beams. To address the demand for higher event-rate readout electronics for energy-resolved neutron imaging detectors, we have developed a high-performance readout electronics system based on the Timepix4 chip. The prototype electronics system comprises a Timepix4 chip board and a high-performance digital board, which are interconnected through a custom FMC interface. The advantage of this system is its ability to achieve the full bandwidth readout of 160 Gbps for a single Timepix4 chip. The electronics system, based solely on a single ZYNQ-MPSOC chip, is capable of fully meeting the required performance specifications within a compact form factor of 8 cm x 30 cm. Furthermore, the system features a high-capacity external SODIMM memory interface (supporting up to 32 GB), which ensures stable data readout through a single 40 Gbps QSFP+ interface. As of the present moment, notable progress has been achieved, including the successful establishment of 16 data channels between Timepix4 and FPGA that operate error-free and stably at a speed of 5.12 Gbps, which is half of the maximum theoretical speed of 10.24 Gbps. The threshold standard deviation across all pixels is less than 50 e- after equalization. And the clear structural results obtained from X-ray experiments indicate that the functionality is essentially complete, allowing further testing.

physics.ins-det↗

arXiv2026

CTPX1: A Highly Integrated and High-Throughput Data-Driven Camera Based on Timepix4

The upgrade of the China Spallation Neutron Source (CSNS-II) will raise the proton beam power to 500 kW. Consequently, the existing Timepix3-based detector systems, limited to a count rate of 80 Mhits/s, will encounter severe saturation challenges. To address the demand of the Energy-Resolved Neutron Imaging instrument (ERNI) for next-generation higher count-rate electronics, this paper presents CTPX1, a high-performance data-driven camera system based on the Timepix4 ASIC. The system adopts a compact modular architecture, integrating readout electronics, a precision high-voltage bias unit, and a TEC temperature control subsystem. To fully exploit the readout potential of the Timepix4 ASIC's 16 high-speed serial links, this paper proposes a two-stage parallel processing architecture. This architecture achieves real-time data aggregation with a total bandwidth of up to 81.92 Gbps. Over a continuous 12-hour operation period, temperature fluctuations were kept within 0.1 °C while the high-voltage output noise remained below 1 mV. High-flux X-ray testing indicates that the system achieves a peak event readout rate of 1.17 Ghits/s, approaching the limit of the configured link speed. In-beam neutron verification at CSNS confirms that the camera successfully resolves fine spatial structures, achieving an imaging performance consistent with the 55 μm pixel pitch of the sensor. Furthermore, the clear observation of spectral features in the Time of flight (TOF) spectrum of a γ-Fe sample validates the system's good time resolution. This camera effectively addresses the data readout saturation challenges, validates the feasibility of Timepix4 technology for neutron imaging, and provides a viable solution for next-generation high-performance neutron imaging instruments.

physics.ins-det↗

arXiv2024

Solution-Hashing Search Based on Layout-Graph Transformation for Unequal Circle Packing

The problem of packing unequal circles into a circular container stands as a classic and challenging optimization problem in computational geometry. This study introduces a suite of innovative and efficient methods to tackle this problem. Firstly, we present a novel layout-graph transformation method that represents configurations as graphs, together with an inexact hash method facilitating fast comparison of configurations for isomorphism or similarity. Leveraging these advancements, we propose an Iterative Solution-Hashing Search algorithm adept at circumventing redundant exploration through efficient configuration recording. Additionally, we introduce several enhancements to refine the optimization and search processes, including an adaptive adjacency maintenance method, an efficient vacancy detection technique, and a Voronoi-based locating method. Through comprehensive computational experiments across various benchmark instances, our algorithm demonstrates superior performance over existing state-of-the-art methods, showcasing remarkable applicability and versatility. Notably, our algorithm surpasses the best-known results for 56 out of 179 benchmark instances while achieving parity with the remaining instances.

cs.CG↗

arXiv2023

An Efficient Solution Space Exploring and Descent Method for Packing Equal Spheres in a Sphere

The problem of packing equal spheres in a spherical container is a classic global optimization problem, which has attracted enormous studies in academia and found various applications in industry. This problem is computationally challenging, and many efforts focus on small-scale instances with the number of spherical items less than 200 in the literature. In this work, we propose an efficient local search heuristic algorithm named solution space exploring and descent for solving this problem, which can quantify the solution's quality to determine the number of exploring actions and quickly discover a high-quality solution. Besides, we propose an adaptive neighbor object maintenance method to speed up the convergence of the continuous optimization process and reduce the time consumption. Computational experiments on a large number of benchmark instances with $5 \leq n \leq 400$ spherical items show that our algorithm significantly outperforms the state-of-the-art algorithm. In particular, it improves the 274 best-known results and matches the 84 best-known results out of the 396 well-known benchmark instances.

cs.CG↗

arXiv2023

Geometric Batch Optimization for the Packing Equal Circles in a Circle Problem on Large Scale

The problem of packing equal circles in a circle is a classic and famous packing problem, which is well-studied in academia and has a variety of applications in industry. This problem is computationally challenging, and researchers mainly focus on small-scale instances with the number of circular items n less than 320 in the literature. In this work, we aim to solve this problem on large scale. Specifically, we propose a novel geometric batch optimization method that not only can significantly speed up the convergence process of continuous optimization but also reduce the memory requirement during the program's runtime. Then we propose a heuristic search method, called solution-space exploring and descent, that can discover a feasible solution efficiently on large scale. Besides, we propose an adaptive neighbor object maintenance method to maintain the neighbor structure applied in the continuous optimization process. In this way, we can find high-quality solutions on large scale instances within reasonable computational times. Extensive experiments on the benchmark instances sampled from n = 300 to 1,000 show that our proposed algorithm outperforms the state-of-the-art algorithms and performs excellently on large scale instances. In particular, our algorithm found 10 improved solutions out of the 21 well-studied moderate scale instances and 95 improved solutions out of the 101 sampled large scale instances. Furthermore, our geometric batch optimization, heuristic search, and adaptive maintenance methods are general and can be adapted to other packing and continuous optimization problems.

cs.CG↗

arXiv2022

Incorporating Multi-armed Bandit with Local Search for MaxSAT

Partial MaxSAT (PMS) and Weighted PMS (WPMS) are two practical generalizations of the MaxSAT problem. In this paper, we propose a local search algorithm for these problems, called BandHS, which applies two multi-armed bandits to guide the search directions when escaping local optima. One bandit is combined with all the soft clauses to help the algorithm select to satisfy appropriate soft clauses, and the other bandit with all the literals in hard clauses to help the algorithm select appropriate literals to satisfy the hard clauses. These two bandits can improve the algorithm's search ability in both feasible and infeasible solution spaces. We further propose an initialization method for (W)PMS that prioritizes both unit and binary clauses when producing the initial solutions. Extensive experiments demonstrate the excellent performance and generalization capability of our proposed methods, that greatly boost the state-of-the-art local search algorithm, SATLike3.0, and the state-of-the-art SAT-based incomplete solver, NuWLS-c.

cs.AI↗

arXiv2022

Farsighted Probabilistic Sampling: A General Strategy for Boosting Local Search MaxSAT Solvers

Local search has been demonstrated as an efficient approach for two practical generalizations of the MaxSAT problem, namely Partial MaxSAT (PMS) and Weighted PMS (WPMS). In this work, we observe that most local search (W)PMS solvers usually flip a single variable per iteration. Such a mechanism may lead to relatively low-quality local optimal solutions, and may limit the diversity of search directions to escape from local optima. To address this issue, we propose a general strategy, called farsighted probabilistic sampling (FPS), to replace the single flipping mechanism so as to boost the local search (W)PMS algorithms. FPS considers the benefit of continuously flipping a pair of variables in order to find higher-quality local optimal solutions. Moreover, FPS proposes an effective approach to escape from local optima by preferring the best to flip among the best sampled single variable and the best sampled variable pair. Extensive experiments demonstrate that our proposed FPS strategy significantly improves the state-of-the-art (W)PMS solvers, and FPS has an excellent generalization capability to various local search MaxSAT solvers.

cs.AI↗

arXiv2022

Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman Problems

TSP is a classical NP-hard combinatorial optimization problem with many practical variants. LKH is one of the state-of-the-art local search algorithms for the TSP. LKH-3 is a powerful extension of LKH that can solve many TSP variants. Both LKH and LKH-3 associate a candidate set to each city to improve the efficiency, and have two different methods, $α$-measure and POPMUSIC, to decide the candidate sets. In this work, we first propose a Variable Strategy Reinforced LKH (VSR-LKH) algorithm, which incorporates three reinforcement learning methods (Q-learning, Sarsa, Monte Carlo) with LKH, for the TSP. We further propose a new algorithm called VSR-LKH-3 that combines the variable strategy reinforcement learning method with LKH-3 for typical TSP variants, including the TSP with time windows (TSPTW) and Colored TSP (CTSP). The proposed algorithms replace the inflexible traversal operations in LKH and LKH-3 and let the algorithms learn to make a choice at each search step by reinforcement learning. Both LKH and LKH-3, with either $α$-measure or POPMUSIC, can be significantly improved by our methods. Extensive experiments on 236 widely-used TSP benchmarks with up to 85,900 cities demonstrate the excellent performance of VSR-LKH. VSR-LKH-3 also significantly outperforms the state-of-the-art heuristics for TSPTW and CTSP.

cs.AI↗

arXiv2022

BandMaxSAT: A Local Search MaxSAT Solver with Multi-armed Bandit

We address Partial MaxSAT (PMS) and Weighted PMS (WPMS), two practical generalizations of the MaxSAT problem, and propose a local search algorithm for these problems, called BandMaxSAT, that applies a multi-armed bandit model to guide the search direction. The bandit in our method is associated with all the soft clauses in the input (W)PMS instance. Each arm corresponds to a soft clause. The bandit model can help BandMaxSAT to select a good direction to escape from local optima by selecting a soft clause to be satisfied in the current step, that is, selecting an arm to be pulled. We further propose an initialization method for (W)PMS that prioritizes both unit and binary clauses when producing the initial solutions. Extensive experiments demonstrate that BandMaxSAT significantly outperforms the state-of-the-art (W)PMS local search algorithm SATLike3.0. Specifically, the number of instances in which BandMaxSAT obtains better results is about twice that obtained by SATLike3.0. Moreover, we combine BandMaxSAT with the complete solver TT-Open-WBO-Inc. The resulting solver BandMaxSAT-c also outperforms some of the best state-of-the-art complete (W)PMS solvers, including SATLike-c, Loandra and TT-Open-WBO-Inc.

cs.AI↗

arXiv2022

Effective Variable Depth Local Search for the Budgeted Maximum Coverage Problem

We address the Budgeted Maximum Coverage Problem (BMCP), which is a natural and more practical extension of the standard 0-1 knapsack problem and the set cover problem. Given m elements with nonnegative weights, n subsets of elements with nonnegative costs, and a total budget, BMCP aims to select some subsets such that the total cost of selected subsets does not exceed the budget, and the total weight of associated elements is maximized. In this paper, we propose a variable depth local search algorithm (VDLS) for the BMCP. VDLS first generates an initial solution by a greedy algorithm, then iteratively improves the solution through a partial depth-first search method, that can improve the solution by simultaneously changing the states (selected or not) of multiple subsets. Such method allows VDLS to explore the solution space widely and deeply, and to yield high-quality solutions. We further propose a neighbour structure to boost the algorithm performance, that is, both subsets have a neighbour relation if they have at least one common associated element. By applying the neighbour structure, VDLS can adjust the selected subsets while losing as few covered elements as possible. Since the existing BMCP benchmarks only have simple structures and small scales, we design 60 new instances with relatively large scales and complex structures to enrich the diversity of the BMCP instances. Experimental results on 30 public instances and 60 new instances we designed demonstrate that VDLS significantly outperforms the existing heuristic and the general CPLEX exact solver, for the BMCP.

cs.DS↗

arXiv2022

A Strengthened Branch and Bound Algorithm for the Maximum Common (Connected) Subgraph Problem

We propose a new and strengthened Branch-and-Bound (BnB) algorithm for the maximum common (connected) induced subgraph problem based on two new operators, Long-Short Memory (LSM) and Leaf vertex Union Match (LUM). Given two graphs for which we search for the maximum common (connected) induced subgraph, the first operator of LSM maintains a score for the branching node using the short-term reward of each vertex of the first graph and the long-term reward of each vertex pair of the two graphs. In this way, the BnB process learns to reduce the search tree size significantly and improve the algorithm performance. The second operator of LUM further improves the performance by simultaneously matching the leaf vertices connected to the current matched vertices, and allows the algorithm to match multiple vertex pairs without affecting the solution optimality. We incorporate the two operators into the state-of-the-art BnB algorithm McSplit, and denote the resulting algorithm as McSplit+LL. Experiments show that McSplit+LL outperforms McSplit+RL, a more recent variant of McSplit using reinforcement learning that is superior than McSplit.

cs.DS↗

arXiv2021

Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem

We address the Traveling Salesman Problem (TSP), a famous NP-hard combinatorial optimization problem. And we propose a variable strategy reinforced approach, denoted as VSR-LKH, which combines three reinforcement learning methods (Q-learning, Sarsa and Monte Carlo) with the well-known TSP algorithm, called Lin-Kernighan-Helsgaun (LKH). VSR-LKH replaces the inflexible traversal operation in LKH, and lets the program learn to make choice at each search step by reinforcement learning. Experimental results on 111 TSP benchmarks from the TSPLIB with up to 85,900 cities demonstrate the excellent performance of the proposed method.

cs.AI↗

arXiv2021

A novel energy resolved neutron imaging detector based on TPX3Cam for the CSNS

The China Spallation Neutron Source (CSNS) operates in pulsed mode and has a high neutron flux. This provides opportunities for energy resolved neutron imaging by using the TOF (Time Of Flight) approach. An Energy resolved neutron imaging instrument (ERNI) is being built at the CSNS but significant challenges for the detector persist because it simultaneously requires a spatial resolution of less than 100 μm, as well as a microsecond-scale timing resolution. This study constructs a prototype of an energy resolved neutron imaging detector based on the fast optical camera, TPX3Cam coupled with an image intensifier. To evaluate its performance, a series of proof of principle experiments were performed in the BL20 at the CSNS to measure the spatial resolution and the neutron wavelength spectrum, and perform neutron imaging with sliced wavelengths and Bragg edge imaging of the steel sample. A spatial resolution of 57 μm was obtained for neutron imaging by using the centroiding algorithm, the timing resolution was on the microsecond scale and the measured wavelength spectrum was identical to that measured by a beam monitor. In addition, any wavelengths can be selected for the neutron imaging of the given object, and the detector can be used for Bragg edge imaging. The results show that our detector has good performances and can satisfy the requirements of ERNI for detectors at the CSNS

physics.ins-det↗

arXiv2020

A sealed ceramic GEM-based neutron detector

The GEM-based neutron detector has flourished in the past decade. However almost all the GEM-based neutron detectors work in the flow-gas mode, and the long-term performances of the detectors may be unstable due to the dynamic changes of atmospheric pressure and ambient temperature. In this paper, a sealed ceramic GEM-based neutron detector was developed at China Spallation Neutron Source (CSNS) and its sensitive area was 100 mm * 100 mm. The characterizations of the detector were presented and discussed, which included the plateau curve, the neutron beam profile, the neutron wavelength spectrum, the spatial resolution (FWHM: 2.77 mm), the two-dimensional (2D) imaging ability, the neutron detection efficiency and the counting rate instability (Relative Standard Deviation (RSD): 0.7%). The results show that the detector has good performances in sealed mode, and it can be used for the measurement of the direct neutron beam at CSNS.

physics.ins-det↗

arXiv2020

Stochastic Item Descent Method for Large Scale Equal Circle Packing Problem

Stochastic gradient descent (SGD) is a powerful method for large-scale optimization problems in the area of machine learning, especially for a finite-sum formulation with numerous variables. In recent years, mini-batch SGD gains great success and has become a standard technique for training deep neural networks fed with big amount of data. Inspired by its success in deep learning, we apply the idea of SGD with batch selection of samples to a classic optimization problem in decision version. Given $n$ unit circles, the equal circle packing problem (ECPP) asks whether there exist a feasible packing that could put all the circles inside a circular container without overlapping. Specifically, we propose a stochastic item descent method (SIDM) for ECPP in large scale, which randomly divides the unit circles into batches and runs Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm on the corresponding batch function iteratively to speedup the calculation. We also increase the batch size during the batch iterations to gain higher quality solution. Comparing to the current best packing algorithms, SIDM greatly speeds up the calculation of optimization process and guarantees the solution quality for large scale instances with up to 1500 circle items, while the baseline algorithms usually handle about 300 circle items. The results indicate the highly efficiency of SIDM for this classic optimization problem in large scale, and show potential for other large scale classic optimization problems in which gradient descent is used for optimization.

math.OC↗

arXiv2018

A fast spatial resolution optimizing method for track-ion using a GEM detector based on the time information

In this work, a fast method of vertex reconstructing for incident ions in GEM detectors was proposed. As inspired by the Time Projection Chamber (TPC), the time information of the consecutive signal samples from Front End Electronics (FEE) was employed. To demonstrate the method, an experiment involving a 2D-readout THGEM detector with an APV-25 FEE was taken. With one experiment dependent parameter in the analysis, the proposed method led to a spatial resolution of 0.45 mm, compared with the same number of 9.10 mm from the traditional center of gravity method.

physics.ins-det↗

arXiv2018

Design of TDC ASIC based on Temperature Compensation

.On the basis of requirement of CSNS, we designed a TDC chip with temperature compensation function in this paper, which employed TSMC 180nm process. Using delay unit bufx8 as the major method, delay lines in each level delayed input signal line through the bufx8 unit to realize fundamental measurement function. The time intervals of two fixed delay standard pulses did not change with temperature variation via intra-chip phase-locked loop. After that, the two standard pulses were sent to TDC internal delay line and measured their values. Then the measured values and standard values were compared. According to the result of comparing and decision switch, the structure of delay lines was reconstructed and their levels were recorded at the same time. We could ensure that the total length of the effective delay line were close to clock cycle as much as possible under the current temperature. The chip was tested after the completion of design. It was found that the time resolution of TDC ASIC was 73ps under 1.8V power supply at room temperature while the time resolutions were 103ps and 62ps at 85$^\circ$ and 0$^\circ$, respectively.

physics.ins-det↗

arXiv2017

Experimental Study of Boron-coated Straws with a Neutron Source

Multiple types of high quality neutron detectors are proposed for the first phase of the China Spallation Neutron Source (CSNS), which will be commissioned in 2018. Considering the shortage of 3He supply, a detector module composed of 49 boron-coated straws (BCS) was developed by Proportional Technologies Inc. (PTI). Each straw has a length of 1000 mm and a diameter of 7.5 mm. Seven straws are tightly packed in a tube, and seven tubes are organized in a row to form a detector module. The charge division method is used for longitudinal positioning. A specific readout system was utilized to output the signal and simultaneously encode each straw. The performance of this detector module was studied using a moderated 252Cf source at the Institute of High Energy Physics (IHEP). The signal amplitude spectrum indicates its n-gamma discrimination capability. Despite the complex readout method, a longitudinal resolution of 6.1-0.5 mm was obtained. The three-dimensional positioning ability qualifies this BCS detector module as a promising neutron scattering spectrometer detector.

physics.ins-det↗