To accurately recognize plant diseases under complex natural conditions, an improved plant disease-recognition model based on the original YOLOv5 network model was established. First, a new InvolutionBottleneck module was used to reduce the numbers of parameters and calculations, and to capture long-distance information in the space. Second, an SE module was added to improve the sensitivity of the model to channel features. Finally, the loss function ‘Generalized Intersection over Union’ was changed to ‘Efficient Intersection over Union’ to address the former’s degeneration into ‘Intersection over Union’. These proposed methods were used to improve the target recognition effect of the network model. In the experimental phase, to verify the effectiveness of the model, sample images were randomly selected from the constructed rubber tree disease database to form training and test sets. The test results showed that the mean average precision of the improved YOLOv5 network reached 70%, which is 5.4% higher than that of the original YOLOv5 network. The precision values of this model for powdery mildew and anthracnose detection were 86.5% and 86.8%, respectively. The overall detection performance of the improved YOLOv5 network was significantly better compared with those of the original YOLOv5 and the YOLOX_nano network models. The improved model accurately identified plant diseases under natural conditions, and it provides a technical reference for the prevention and control of plant diseases.
Deadlock-freedom is a major challenge in developing multithreaded programs, as a deadlock cannot be resolved until one restarts the program (mostly by using manual intervention). To avoid the risk of blocking, a program may use the trylock operations rather than lock operations. In this case, if a thread fails to acquire a lock using trylock, since trylock is non-blocking, the thread can release acquired locks to avoid a deadlock after trylock returns. Although this approach avoids deadlocks, it may also introduce bugs such as livelock and deadlivelock. Moreover, when such bugs are identified in a program, revising the program manually is error-prone.With this motivation, in this paper, we propose an approach for avoiding deadlocks, livelocks and deadlivelocks in the given multi-threaded program. In our approach, we first identify cyclic lock dependencies that may lead to deadlocks, livelocks or deadlivelocks. Subsequently, we map the problem of ensuring freedom from deadlocks, livelocks and deadlivelocks to the weighted partial maximum satisfiability problem. To ensure that the repaired program preserves most of original design, our approach attempts to make minimal changes to the original program.
For the automated robotic picking of bunch-type fruit, the strategy is to roughly determine the location of the bunches, plan the picking route from a remote location, and then locate the picking point precisely at a more appropriate, closer location. The latter can reduce the amount of information to be processed and obtain more precise and detailed features, thus improving the accuracy of the vision system. In this study, a long–close distance coordination control strategy for a litchi picking robot was proposed based on an Intel Realsense D435i camera combined with a point cloud map collected by the camera. The YOLOv5 object detection network and DBSCAN point cloud clustering method were used to determine the location of bunch fruits at a long distance to then deduce the sequence of picking. After reaching the close-distance position, the Mask RCNN instance segmentation method was used to segment the more distinctive bifurcate stems in the field of view. By processing segmentation masks, a dual reference model of “Point + Line” was proposed, which guided picking by the robotic arm. Compared with existing studies, this strategy took into account the advantages and disadvantages of depth cameras. By experimenting with the complete process, the density-clustering approach in long distance was able to classify different bunches at a closer distance, while a success rate of 88.46% was achieved during fruit-bearing branch locating. This was an exploratory work that provided a theoretical and technical reference for future research on fruit-picking robots.
In this paper, we concentrate on automated synthesis of fault recovery mechanism for fault-intolerant componentbased models that encompass a cyber-physical system. We define the notion of fault recovery for cyber-physical component-based models. We also present synthesis constraints that preserve the correctness and cyber-physical nature of a given fault-intolerant model under which recovery can be added. We show that the corresponding synthesis problem is NP-complete and consequently introduce symbolic heuristics to tackle the exponential complexity. Our experimental results validate effectiveness of our heuristics for relatively large models.
Data center energy consumption has become an increasingly significant contributor both to greenhouse emissions and costs. To increase utilization of individual hosts and improve efficiency, most modern data centers co-locate workloads belonging to different application classes, some being latency-sensitive (LS) and others best-effort (BE) which are more tolerant to performance variation. It is therefore necessary to design mechanisms that reduce power consumption even in the resulting high-utilization environment, while preserving LS task performance. Moreover, the abundance of different workloads and the security implications of public cloud make mechanisms that rely on extensive knowledge of workload characteristics or on application-exported metrics challenging to deploy. We present PACT, Per Application Class Turbo Controller, a system that leverages two novel mechanisms to reduce power consumption even in highly-utilized data centers. By treating applications like opaque boxes that do not need to provide application-specific performance signals, the first mechanism, Turbo Control, reduces power consumption by decreasing the operating frequency and throttling only BE tasks, without affecting performance-sensitive LS tasks. We identify the shortcomings of Turbo Control and increase its effectiveness by introducing CPUJailing, a mechanism that allocates different sets of cores to LS and BE applications. We deploy PACT (Turbo Control + CPUJailing) in production * Kostis Kaffes was an intern at Google during this work. † Christos Kozyrakis was partly at Google during this work.
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