Front vehicle detection based on improved fusion method for lidar and visual image

In order to ensure the detection accuracy, an improved adaptive weighted (IAW) method is proposed in this paper to fuse the data of images and lidar sensors for the vehicle object’s detection. Firstly, the IAW method is proposed in this paper and the first simulation is conducted. The unification of two sensors’ time and space should be completed at first. The traditional adaptive weighted average method (AWA) will amplify the noise in the fusion process, so the data filtered with Kalman Filter (KF) algorithm instead of with the AWA method. The proposed IAW method is compared with the AWA method and the Distributed Weighted fusion KF algorithm in the data fusion simulation to verify the superiority of the proposed algorithm. Secondly, the second simulation is conducted to verify the robustness and accuracy of the IAW algorithm. In the two experimental scenarios of sparse and dense vehicles, the vehicle detection based on image and lidar is completed, respectively. The detection data is correlated and merged through the IAW method, and the results show that the IAW method can correctly associate and fuse the data of the two sensors. Finally, the real vehicle test of object vehicle detection in different environments is carried out. The IAW method, the KF algorithm, and the Distributed Weighted fusion KF algorithm are used to complete the target vehicle detection in the real vehicle, respectively. The advantages of the two sensors can give full play, and the misdetection of the target objects can be reduced with proposed method. It has great potential in the application of object acquisition.

Hardware efficient circuit for low error logarithmic converter

The need to implement high-speed Signal processing applications in which multiplication and division play a vital role made logarithmic arithmetic a prominent contender over the traditional arithmetic operations in recent years. But the logarithm and antilogarithm converters are the bottlenecks. In order to reduce the logarithmic conversion complexity, several works have been introduced from time to time for correcting the error in Mitchell’s algorithm but at the cost of hardware. In this work, we propose a 32-bit binary to the binary logarithmic converter with a simple correction circuit compared with existing techniques. Unlike the current methods that use the linear piece-wise approximation in the mantissa, we propose a weighted average method to correct the error in Mitchell’s approximation. The maximum error percentage from the proposed work is 0.91%, which is 16.9% of Mitchell’s error percentage.

Weed Classification Using Explainable Multi-Resolution Slot Attention

In agriculture, explainable deep neural networks (DNNs) can be used to pinpoint the discriminative part of weeds for an imagery classification task, albeit at a low resolution, to control the weed population. This paper proposes the use of a multi-layer attention procedure based on a transformer combined with a fusion rule to present an interpretation of the DNN decision through a high-resolution attention map. The fusion rule is a weighted average method that is used to combine attention maps from different layers based on saliency. Attention maps with an explanation for why a weed is or is not classified as a certain class help agronomists to shape the high-resolution weed identification keys (WIK) that the model perceives. The model is trained and evaluated on two agricultural datasets that contain plants grown under different conditions: the Plant Seedlings Dataset (PSD) and the Open Plant Phenotyping Dataset (OPPD). The model represents attention maps with highlighted requirements and information about misclassification to enable cross-dataset evaluations. State-of-the-art comparisons represent classification developments after applying attention maps. Average accuracies of 95.42% and 96% are gained for the negative and positive explanations of the PSD test sets, respectively. In OPPD evaluations, accuracies of 97.78% and 97.83% are obtained for negative and positive explanations, respectively. The visual comparison between attention maps also shows high-resolution information.

PROJECT-BASED INCIDENT RATE BASED ON WEIGHTED AVERAGE METHOD

The incident rate has been widely used to indicate safety performance. The incident rate of a company can be compared with the national or international incident rate within similar industry or among different type of industries. The comparison is particularly very useful as a safety benchmark to gauge performance with other companies in the same business area. However, many existing methods produce the annual incident rate, which has been formulated on an annual basis. This will lead to incompatibility of the method used in calculating the incident rate for a project that runs for a specific period. This is because the annual incident rate does not consider the duration of the project; it becomes less meaningful in indicating the safety performance of project-based activities such as those in construction industries. The proposed method which is Project-Based Incident Rate (PIR) is found to be able to reflect the actual situation of project-based companies better than the existing annual incident rate method, and it is also can be expressed both on a monthly and yearly basis.

Generative Information Hiding of Iris Feature Data Based on Gaussian Fuzzy Algorithm

The existing methods used for hiding the iris feature data were time-consuming for iris feature extraction. Meanwhile, the information security after hiding was also low, leading to low efficiency and security of information hiding. Therefore, a method of hiding iris features data generative information based on a Gaussian fuzzy algorithm was proposed. In the preprocessing stage of the image, the weighted average method was adopted for the gray-level transformation of the iris image, and the Gaussian fuzzy algorithm was used to smooth the image. In addition, the Laplacian convolution kernel was used to sharpen the image. The iris regions were normalized. The iris feature data was extracted by employing 2D Gabor wavelet. Moreover, the iris feature data was encrypted and decrypted using the AES algorithm, and hence, effectively enhancing the security of the generative information of iris feature data. Experimental results show that the proposed method can extract iris feature information within ten seconds, and the data security coefficient is high thus the proposed method efficiently realizes the information hiding.

Estimating the Thermal Properties of the Cover and the Floor in a Plastic Greenhouse

This study comprehensively analyzed the heat loss and total heat transfer coefficient (U-value) of a single-span experimental plastic greenhouse covered with a double layer of 0.1 mm thick polyethylene. The air temperature and heat flux (W m−2) of the greenhouse components were measured from 18:00 to 06:00, and the energy balance equations under steady-state conditions were determined. The heat flux and U-value of the roof, sides, front and rear, and floor of the greenhouse were determined and compared. The results showed that these values for the roof play an important role in determining the heat load in the greenhouse, and that the average heat transfer through the floor is very small. The average U-value of the greenhouse cover is a comprehensive value which takes the U-values of the roof, sides, and front and rear into account through the use of an area–weighted average method. Finally, an average U-value of 3.69 W m−2 °C −1 was obtained through the analysis of the variations in the U-value, as it is related to the difference in air temperature between the interior and exterior of the greenhouse, as well as to the outdoor wind speed. The relationships between the average U-value and those of the roof, sides, and front and rear of the experimental greenhouse were modeled, and were shown to have a highly linear relationship.

Assessing the Robustness of a Factory Amid the COVID-19 Pandemic: A Fuzzy Collaborative Intelligence Approach

The COVID-19 pandemic has affected the operations of factories worldwide. However, the impact of the COVID-19 pandemic on different factories is not the same. In other words, the robustness of factories to the COVID-19 pandemic varies. To explore this topic, this study proposes a fuzzy collaborative intelligence approach to assess the robustness of a factory to the COVID-19 pandemic. In the proposed methodology, first, a number of experts apply a fuzzy collaborative intelligence approach to jointly evaluate the relative priorities of factors that affect the robustness of a factory to the COVID-19 pandemic. Subsequently, based on the evaluated relative priorities, a fuzzy weighted average method is applied to assess the robustness of a factory to the COVID-19 pandemic. The assessment result can be compared with that of another factory using a fuzzy technique for order preference by similarity to ideal solution. The proposed methodology has been applied to assess the robustness of a wafer fabrication factory in Taiwan to the COVID-19 pandemic.

МОДЕЛИРОВАНИЕ ЛИНГВИСТИЧЕСКОЙ НЕОПРЕДЕЛЕННОСТИ НА ОСНОВЕ Z-ЧИСЕЛ В СИСТЕМАХ МНОГОКРИТЕРИАЛЬНОГО ПРИНЯТИЯ РЕШЕНИЙ

The article is devoted to the problem of multi criteria decision making under linguistic uncertainty. Information of different approaches for modelling linguistic uncertainty have been analyzed. The concept of z- numbers proposed by L. Zadeh have been presented. Z-number is presented as cortege of two fuzzy number A and B, where A is analyzed factor, B is reliability of an assessment. The method of conversion z- numbers into generalized fuzzy numbers have been applied. As test problem have been used supplier selection problem. As decision making model have been used group weighted average method. All calculations and results were presented.