Ice, Cloud, and land Elevation Satellite (ICESat-2) carries the Advanced Topographic Laser Altimeter System (ATLAS), which enhancing along-track sampling density but introduces substantial noise in photon point cloud data. Therefore, this study establishes a denoising and classification feature parameter system grounded in the three-dimensional spatial distribution characteristics of photon point clouds. Modeling is conducted in two layers: one layer for upper noise photons and canopy signal photons, and another layer for lower noise photons and ground signal photons. Machine learning and neural network algorithms are utilized to denoise and classify the original photon point clouds from ICESat-2, aiming to obtain a transferable and universally applicable supervised classification model for denoising photon point clouds. Recall, Precision, and the harmonic mean of Recall and Precision (F1 Score) are used as evaluation metrics to verify the accuracy of local, transfer, and global models. The results indicate that under various forest types and external conditions, the proposed photon point cloud Layered Denoising and Classification Model (LDCM) outperforms the Differential Regressive and Gaussian Adaptive Nearest Neighbor (DRAGANN, ICESat-2 ATL08 production algorithm), Ordering Points to Identify the Clustering Structure (OPTICS), and Adaptive Elevation Difference Thresholding (AEDTA) algorithms in terms of accuracy. Compared to the DRAGANN algorithm, the maximum accuracy improvement is 60%, with an average improvement of approximately 20%; compared to the OPTICS algorithm, the maximum accuracy improvement is 36%, with an average improvement of about 28%; compared to the AEDTA algorithm, the maximum accuracy improvement is 27%, with an average improvement of about 14%. The F1 Score for the validation set of the machine learning and neural network algorithms is above 0.94, with the Categorical Boosting (CatBoost) algorithm achieving the best performance. Both the transfer model and the global model have F1 Scores above 0.90. Therefore, the proposed photon point cloud LDCM not only demonstrates excellent classification accuracy but also exhibits good transferability and general applicability.

Automatic selection of the most appropriate algorithms for complex optimization problems has emerged as a cutting-edge trend in artificial intelligence. This approach circumvents the interpretability challenges posed through trial and error. A hyperheuristic and reinforcement learning-guided meta-heuristic algorithm recommendation (HHRL-MAR) is proposed to facilitate the adaptive selection of a diverse array of meta-heuristic algorithms tailored to the unique characteristics of various problems in this paper. To this end, four meta-heuristics with distinct advantages are integrated to form the action space within the reinforcement learning, serving as the low-level heuristic for hyperheuristic. The incorporated reward mechanism based on the real-time state of the population enhances both the flexibility and accuracy of the algorithm. Three selection strategies in light of simulated annealing and ε–greedy are avoid premature convergence associated with designed to a singular selection approach. The experimental results show the efficacy of HHRL-MAR for large-scale complex continuous optimization in terms of accuracy, stability, and convergence speed.

This article investigates a distributed assembly no-wait flow-shop scheduling problem (DANWFSP), which has important applications in manufacturing systems. The objective is to minimize the total flowtime. A mixed-integer linear programming model of DANWFSP with total flowtime criterion is proposed. A population-based iterated greedy algorithm (PBIGA) is presented to address the problem. A new constructive heuristic is presented to generate an initial population with high quality. For DANWFSP, an accelerated NR3 algorithm is proposed to assign jobs to the factories, which improves the efficiency of the algorithm and saves CPU time. To enhance the effectiveness of the PBIGA, the local search method and the destruction-construction mechanisms are designed for the product sequence and job sequence, respectively. A selection mechanism is presented to determine, which individuals execute the local search method. An acceptance criterion is proposed to determine whether the offspring are adopted by the population. Finally, the PBIGA and seven state-of-the-art algorithms are tested on 810 large-scale benchmark instances. The experimental results show that the presented PBIGA is an effective algorithm to address the problem and performs better than recently state-of-the-art algorithms compared in this article.

The blocking flow-shop scheduling problem (BFSP) with sequence-dependent setup times (SDST), which has important ramifications in the modern industry, is investigated in this paper. The SDST/BFSP is extended from the BFSP, which included the setup times in processing times. However, the setup times in actual processing depend on the preceding and successive jobs in the processing order. Hence, the setup times are considered independently of processing times in this paper. The mixed-integer linear programming (MILP) model of SDST/BFSP is designed. Furthermore, a discrete whale optimization algorithm (DWOA) based on problem-specific knowledge is proposed to solve certain SDST/BFSP. Firstly, a construction heuristic that depends on the properties of the problem is designed to reduce the blocking time and idle times created by SDSTs. Secondly, the leading whales in DWOA are replaced by the critical factories in SDST/BFSP. Further, three different search strategies including the searching for prey, encircling prey, and bubble-net attacking prey are designed to improve the exploitation and exploration capability of the DWOA. The statistical and computational experimentation in an extensive benchmark testified that the DWOA outperforms the state-of-the-art algorithms regarding efficiency and significance in solving SDST/BFSP.

The distributed blocking flow-shop scheduling problem (DBFSP), which has been proven to be a strongly NP-hard problem, has important applications in a variety of industrial systems. In this paper, a self-adapting water wave optimization (SAWWO) algorithm is proposed to solve the blocking flow-shop scheduling problem with the criterion of minimizing the makespan. In SAWWO, the candidates are represented as discrete job permutations. Two heuristics are utilized to obtain the desirable initial solution. In the propagation phase, the self-adapting spatial dispersal operator is designed to balance the exploration and exploitation of SAWWO. Four local search methods are introduced to intensify the exploitation ability of the algorithm in the local region. Furthermore, the redesigned path-relinking method is presented as the modified refraction operator to help the algorithm jump out the local optimal. Additionally, the performance of the proposed algorithm is evaluated by comparing with five other state-of-the-art algorithms. The statistical results demonstrate the effectiveness of SAWWO for solving the DBFSP.

Continuous data protection, which logs every update to a file system, is an enabling technology to protect file systems against malicious attacks and/or user mistakes, because it allows each file update to be undoable. Existing implementations of continuous data protection work either at disk access interface or within the file system. Despite the implementation complexity, their performance overhead is significant when compared with file systems that do not support continuous data protection. Moreover, such kernel-level file update logging implementation is complex and cannot be easily ported to other operating systems. This paper describes the design and implementation of four user-level continuous data protection implementations for NFS servers, all of which work on top of the NFS protocol and thus can be easily ported to any operating systems that support NFS. Measurements obtained from running standard benchmarks and real-world NFS traces on these user-level continuous data protection systems demonstrate a surprising result: Performance of NFS servers protected by pure user-level continuous data protection schemes is comparable to that of unprotected vanilla NFS servers.

No abstract available.

The increasing popularity of IEEE 802.11-based wireless local area networks (LANs) lends them credibility as a viable alternative to third-generation (3G) wireless technologies. Even though wireless LANs support much higher channel bandwidth than 3G networks, their network-layer handoff latency is still too high to be usable for interactive multimedia applications such as voice over IP or video streaming. Specifically, the peculiarities of commercially available IEEE 802.11b wireless LAN hardware prevent existing mobile Internet protocol (IP) implementations from achieving subsecond Mobile IP handoff latency when the wireless LANs are operating in the infrastructure mode, which is also the prevailing operating mode used in most deployed IEEE 802.11b LANs. In this paper, we propose a low-latency mobile IP handoff scheme that can reduce the handoff latency of infrastructure-mode wireless LANs to less than 100 ms, the fastest known handoff performance for such networks. The proposed scheme overcomes the inability of mobility software to sense the signal strengths of multiple-access points when operating in an infrastructure-mode wireless LAN. It expedites link-layer handoff detection and speeds up network-layer handoff by replaying cached foreign agent advertisements. The proposed scheme strictly adheres to the mobile IP standard specification, and does not require any modifications to existing mobile IP implementations. That is, the proposed mechanism is completely transparent to the existing mobile IP software installed on mobile nodes and wired nodes. As a demonstration of this technology, we show how this low-latency handoff scheme together with a wireless LAN bandwidth guarantee mechanism supports undisrupted playback of remote video streams on mobile stations that are traveling across wireless LAN segments.

The data contents of an information system may be corrupted due to security breaches or human errors. The financial loss of such corruption is typically proportional to the amount of time required to recover the system's data/service. Recognizing that it is impossible to build absolutely secure computer systems and that human errors are inevitable, this project focuses on intrusion tolerance techniques that speed up the process of repairing a damaged system after an intrusion/error takes place. The proposed system, called Repairable File Service (or RFS), is specifically designed to facilitate the reparation of compromised network file servers. An architectural innovation of RFS is that it is decoupled from and requires no modifications on the shared file server that is being protected. RFS supports fine-grained logging to allow roll-back of any file update operation, and keeps track of inter-process dependencies to quickly determine the extent of system damage after an attack/error. Compared with the current practice of manual post-intrusion damage repair, RFS significantly reduces the mean time to repair and thus improves the overall system availability. Empirical measurements on the fully operational RFS prototype shows that the performance overhead of RFS is less than 6%, and that RFS is able to speed up the repair process by at least two orders of magnitude compared to manual repair.