This paper presents an effective improved artificial potential field-based regression search (improved APF-based RS) method that can obtain a better and shorter path efficiently without local minima and oscillations in an environment including known, partially known or unknown, static, and dynamic environments. We redefine potential functions to eliminate oscillations and local minima problems, and use improved wall-following methods for the robots to escape non-reachable target problems. Meanwhile, we develop a regression search method to optimise the planned path. The optimisation path is calculated by connecting the sequential points produced by improved APF. The simulations demonstrate that the improved APF method easily escapes from local minima, oscillations, and non-reachable target problems. Moreover, the simulation results confirm that our proposed path planning approach can calculate a shorter or more nearly optimal than the general APF can. Results prove our improved APF-based RS method's feasibility and efficiency for solving path planning.
Wireless sensor networks (WSNs) are often deployed in harsh and unattended environments, which may cause the generation of abnormal or low quality data. The inaccurate and unreliable sensor data may increase generation of false alarms and erroneous decisions, so it’s very important to detect outliers in sensor data efficiently and accurately to ensure sound scientific decision-making. In this paper, an outlier detection algorithm (TSVDD) using model selection-based support vector data description (SVDD) is proposed. Firstly, the Toeplitz matrix random feature mapping is used to reduce the time and space complexity of outlier detection. Secondly, a novel model selection strategy is realized to keep the algorithm stable under the low feature dimensions, this strategy can select a relatively optimal decision model and avoid both under-fitting and overfitting phenomena. The simulation results on SensorScope and IBRL datasets demonstrate that, TSVDD achieves higher accuracy and lower time complexity for outlier detection in WSNs compared with existing methods.
During harvesting and storage, slight bruises on apple surface caused by impact, compression, vibration, or abrasion are inevitable. To find an appropriate method to identify the bruised apples at five stages (1 min, 1 day, 2 days, 3 days and 4 days after bruising), 108 Fuji apples were collected as samples. Hyperspectral images of apples covering the wavelength between 400 and 1000 nm were acquired by the SOC710-VP hyperspectral imaging system. The standard normal variate (SNV) method was utilized for smoothing and denoising of the original hyperspectral data. Classification models, including Extreme Learning Machine (ELM), Partial Least Squares Linear Discriminant Analysis (PLS-DA) and Classification and Regression Tree (CART), coupled with a variable selection method named Minimum Redundancy Maximum Relevance (mRMR), were built to identify the bruised apples. The results showed that the ELM models exhibited the best classification capability, with the mean correct classification rate of 95.97%. The bruised samples are easier to be identified over time. Minimum noise fraction (MNF) method was implemented to classify the bruised region of apples based on the selected wavelengths. The overall classification accuracy of MNF is 92.9%, which indicates that MNF is an effective method for identifying bruised regions of apples.
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