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opencc-by-4.0Jun 2010View details →

Figure 2: Optimization in natural ants collective behavior: foraging and clustering (from [8])-Self-organization and social insects algorithms

On figure 2, two examples of self-organization in natural ants are presented. On the left side, the well-known Deneubourg experiment consists to highlight with a very simple device the ant foraging problem. The ant objectives is to find the optimal way from nest to food source, using pheromone trail deposition. On the right side, cemetery clustering formation are shown at 4 successive times: ants form piles of corpses to clean their nests. Each of them has elementary actions, unknowing the whole situation, but dealing only with local information. There is no supervisor to lead the piles formation which emerges from ant interactions.

Figure 6. One chromosome from the population and the five chromosomes existing in the evaluation partition.-Genetic Algorithms Principles Towards Hidden Markov Model

For example comparing the chromosome given in Figure 6 with the first chromosome in the evaluation partition, the difference between the relation Med-Med and Med-High as a pair is 0.0 and the difference between the relation High-High and High-Med as a pair is 0.1. Similarly the difference between the relation Med-Cold and Med-Hot as a pair is 0.1 and the difference between the relation High-Cold and High-Hot as a pair is 0.2. We sum all these differences to get the value of compare(i,j), the sum value is 0+0.1+0.1+0.2 = 0.4. Using the same approach we compute the compare function with the other four chromosomes and we get values 0.4, 0.5,0.4 and 0.6. Now we sum the five values 0.4 + 0.4 + 0.5+ 0.4 +0.6 = 2.3. The fitness value is then 1/ 2.3 = 0.434. The highest is the fitness value, the better is the performance of the chromosome.

Figure 4. Incorrect crossover operation. The High-High and High-Med probability values summation should be 1.-Genetic Algorithms Principles Towards Hidden Markov Model

In this genetic operator, we choose two chromosomes at random and apply crossover between them. Figure 3 shows the proposed crossover. We choose a crossing cut site at random. It is to be noted that the crossing cut site should be even number. We should have two crossing cut sites. If we make crossing cut site odd number, the resultant child will not have a correct value of probability. The incorrect crossover is shown in Figure 4.

Figure 5. Mutation process. This is happened by decreasing 0.2 from Med-Cold probability and adding 0.2 to Med- Hot.-Genetic Algorithms Principles Towards Hidden Markov Model

Figure 5 illustrates an example of mutation process. In Figure 5, Med-Cold:0.9 and Med-Hot:0.1 before mutation and become Med-Cold:0.7 and Med-Hot:0.3 after mutation. This is done by decreasing 0.2 from Med-Cold probability and adding 0.2 to Med-Hot probability.

Figure 3. The crossover operation between two HMM chromosomes Figure-Genetic Algorithms Principles Towards Hidden Markov Model

Crossover In this genetic operator, we choose two chromosomes at random and apply crossover between them. Figure 3 shows the proposed crossover. We choose a crossing cut site at random. It is to be noted that the crossing cut site should be even number. We should have two crossing cut sites. If we make crossing cut site odd number, the resultant child will not have a correct value of probability. The incorrect crossover is shown in Figure 4.

Figure 2. The general structure of the proposed approach-Genetic Algorithms Principles Towards Hidden Markov Model

The chromosome contains 8 genes, each is represented by the relation between two states accompanied with a probability value. The genes should be formed in this way because this is important in the crossover operation as to be explained later. The most important thing is that each two genes has the probability summation of 1.0. For example Med-Med:02 and Med-High:08 have the summation of 1.0. Similarly High-High:0.6 and High-Med:0.4 have the summation of 1.0. Each two genes with summation of 1.0 should be neighbors.

Figure 1. HMM to describe a relation between the states Med. and High with the observations (invisible states) cold and hot.-Genetic Algorithms Principles Towards Hidden Markov Model

Hewahi [4] presented a modified version of Censored Production Rule (CPR) called Modified Censored Production Rules (MCPR). CPR is proposed by Michalski and Winston [6 ] to capture real time situations. MCPR can fit with hidden Markov model and present a scheme to compute the certainty values of the obtained conclusions out of the induced rules. To compute the certainty values for the rule actions (conclusions), the approach exploited only the probability values associated with the hidden Markov model without using any of the other well known certainty computation approaches. Hewahi [3] also proposed an intelligent networking management system based on the induced MCPRs extracted from a networking structure based on HMM. The advantage of using this technique is that MCPRs are very useful in real time applications and can be adapted over time based on the obtained experience of the networking working process. Let us consider the HMM presented in Figure 1.

opencc-by-4.0Oct 2013View details →

BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 12. Genetic Algorithm flowchart

Then next generation are produced by combination of the elites (15%), crossover (55%) and mutation (30%) of the initial population. Elites are the best chromosomes which are directly transferred to the next generation. Because of long chromosome length, for crossover, five points are randomly chosen in each parent as cut points. Figure 11 shows a typical crossover with two cutpoints and Figure 12 illustrates a flowchart for the proposed evolutionary model. Selections are based on Roulette Wheel selection, more detailed information can be found in.

opencc-by-4.0Nov 2011View details →

Figure 9. Trajectory Algorithm Simulation-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

We have presented the system for a fully autonomous navigation of an UAV based on Omni directional vision system and image processing. we explain vision system configuration ,image processing and feature extraction methods and finaly suggest an algorithm based on potential field for navigation of an UAV.

opencc-by-4.0Apr 2024View details →

A Point Cloud Dataset of Vehicles Passing Through a Toll Station for use in Training Classification Algorithms

This work presents a point cloud dataset of vehicles passing through a toll station in Colombia to be used to train artificial vision and computational intelligence algorithms. This article details the process of creating the dataset, covering initial data acquisition, range information preprocessing, point cloud validation, and vehicle labeling. Additionally, a detailed description of the structure and content of the dataset is provided, along with some potential applications of its use. The dataset consists of 36,026 total object classes: 31,432 cars, campers, vans and 2-axle trucks with a single tire on the rear axle, 452 minibuses with a single tire on the rear axle, 1158 buses, 1179 2-axle small trucks, 797 2-axle large trucks, and 1008 trucks with 3 or more axles. The point clouds were captured using a LiDAR sensor and Doppler effect speed sensors. The dataset can be used to train and evaluate algorithms for range data processing, vehicle classification, vehicle counting, and traffic flow analysis. The dataset can also be used to develop new applications for intelligent transportation systems. <table> <tbody> <tr> <td>Type</td> <td>Description</td> <td>Quantity</td> </tr> <tr> <td>1</td> <td>Cars, campers, vans and 2-axle trucks with a single tire on the rear axle</td> <td>31,432</td> </tr> <tr> <td>2</td> <td>Minibuses with a single tire on the rear axle</td> <td>452</td> </tr> <tr> <td>3</td> <td>Buses</td> <td>1,158</td> </tr> <tr> <td>4</td> <td>Trucks with 3 or more axles</td> <td>1,008</td> </tr> <tr> <td>5</td> <td>2-axle small trucks</td> <td>1,179</td> </tr> <tr> <td>6</td> <td>2-axle large truck</td> <td>797</td> </tr> <tr> <td>Total</td> <td> </td> <td>36,026</td> </tr> </tbody> </table>

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Cloud_ICA: A deterministic cloud-overlap algorithm for generating a complete set of independent column atmospheres

In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community's concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating them, but with individual studies it is difficult to compare. Here, we collect a wide range of such simplifications in either radiative transfer modeling or atmospheric conditions and assess potential errors within a consistent framework on climate‐relevant scales. We build benchmarking capability around a solar heating code (Solar‐J) that doubles as a photolysis code for chemistry and can be readily adapted to consider other errors and uncertainties. The broad classes here include: use of broad wavelength bands to integrate over spectral features; scattering approximations that alter phase function and optical depths for clouds and gases; uncertainty in ice‐cloud optics; treatment of fractional cloud cover including overlap; and variability of ocean surface albedo. We geographically map the errors in W m−2 using a full climate re‐creation for January 2015 from a weather forecasting model. For many approximations assessed here, mean errors are ∼2 W m−2 with greater latitudinal biases and are likely to affect a model's ability to match the current climate state. Combining this work with previous studies, we make priority recommendations for fixing these simplifications based on both the magnitude of error and the ease or computational cost of the fix.

opencc-zeroMay 2024View details →

opencc-by-4.0May 2024View details →

Dataset for Twitter Sentiment Analysis on Criminal Data Propagation using Naive Bayes Algorithm

This study presents a dataset tailored for conducting sentiment analysis on Twitter regarding the propagation of criminal data. Leveraging the Naive Bayes algorithm, the dataset aims to facilitate research into public perceptions surrounding the dissemination of criminal data on social media platforms. Through a currated collection of tweets, researchers can explore the nuanced sentiments and attitudes expressed by users in response to this phenomenon.

Figure 8. F1 scores for YOLOv5 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean

Figure 8. F1 scores for YOLOv5 indicating the harmonic mean between precision and recall scores. Data indicated that detection results for both species would be best at a confidence threshold of 0.298.

Figure 11. YOLOv5 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean

Figure 11. YOLOv5 precision (A), recall (B), and F1 score (C) changes as a function of Amoronthus polmeri density (plants m−2).

Figure 10. Detection results for YOLOv5 with a in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean

Figure 10. Detection results for YOLOv5 with a confidence interval of 0.15. False-positive detections of Mollugo verticillata and Abutilon theophrasti as Amoronthus polmeri are denoted by arrows pointing from "A" and "B," respectively.

Figure 9. YOLOv5 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean

Figure 9. YOLOv5 detection results for Amoronthus polmeri and soybean using confidence thresholds of 0.15 (A) and 0.70 (B). The likelihood of false-negative (FN) detections increases as confidence thresholds increase, as can be seen in B. Objects assigned a confidence interval of less than 0.70 are not detected in B. FN A. palmeri and soybean detections in B are indicated by the orange and white arrows, respectively.

Figure 7 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean

Figure 7. Image annotation of soybean at the R2 growth stage. As soybean populations were much higher than Amoronthus polmeri populations, there was a high level of soybean overlap. Therefore, it was necessary to include multiple soybean plants in each image. However, A. polmeri plants typically did not have as much overlap, and in most cases, it was much easier to identify and label individual plants.

Figure 3 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean

Figure 3. Intersection over union (IoU) equation, defined as the overlap between the ground truth annotation and the computer prediction bounding box, divided by the total area of the two bounding boxes.IoU overlaps greater than 0.5 were considered true-positive predictions,whereas overlaps less than 0.5 were considered false-positive predictions.

Figure 2 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean

Figure 2. Illustration of the annotation process. Amoronthus polmeri and soybean plants are labeled in this figure with orange and white boxes, respectively. Bounding boxes overlap with neighboring bounding boxes when plant features are irregular. In cases where a single bounding box could not encompass a plant without including a plant of another species, multiple irregular bounding boxes were drawn on a single specimen.

Figure 4 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean

Figure 4. Mean average precision (mAP) results of each model after training. YOLOv5 was considered the best-performing algorithm of each tested model with a mAP of 0.77.