Ship Detection And Tracking In Inland Waterways Using Improved Yolov3 And Deep Sort

Ship detection and tracking is an important task in video surveillance in inland waterways. However, ships in inland navigation are faced with accidents such as collisions. For collision avoidance, we should strengthen the monitoring of navigation and the robustness of the entire system. Hence, this paper presents ship detection and tracking of ships using the improved You Only Look Once version 3 (YOLOv3) detection algorithm and Deep Simple Online and Real-time Tracking (Deep SORT) tracking algorithm. Three improvements are made to the YOLOv3 target detection algorithm. Firstly, the Kmeans clustering algorithm is used to optimize the initial value of the anchor frame to make it more suitable for ship application scenarios. Secondly, the output classifier is modified to a single SoftMax classifier to suit our ship dataset which has three ship categories and mutual exclusion. Finally, Soft Non-Maximum Suppression (Soft-NMS) is introduced to solve the deficiencies of the Non-Maximum Suppression (NMS) algorithm when screening candidate frames. Results showed the mean Average Precision (mAP) and Frame Per Second (FPS) of the improved algorithm are increased by about 5% and 2, respectively, compared with the existing YOLOv3 detecting Algorithm. Then the improved YOLOv3 is applied in Deep Sort and the performance result of Deep Sort showed that, it has greater performance in complex scenes, and is robust to interference such as occlusion and camera movement, compared to state of art algorithms such as KCF, MIL, MOSSE, TLD, and Median Flow. With this improvement, it will help in the safety of inland navigation and protection from collisions and accidents

A Novel Approach for Apple Freshness Prediction Based on Gas Sensor Array and Optimized Neural Network

Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: [email protected] (W.Y.).Copyright © 2023 by the authors. Apple is an important cash crop in China, and the prediction of its freshness can effectively reduce its storage risk and avoid economic loss. The change in the concentration of odor information such as ethylene, carbon dioxide, and ethanol emitted during apple storage is an important feature to characterize the freshness of apples. In order to accurately predict the freshness level of apples, an electronic nose system based on a gas sensor array and wireless transmission module is designed, and a neural network prediction model using an improved Sparrow Search Algorithm (SSA) based on chaotic sequence (Tent) to optimize Back Propagation (BP) is proposed. The odor information emitted by apples is studied to complete an apple freshness prediction. Furthermore, by fitting the relationship between the prediction coefficient and the input vector, the accuracy benchmark of the prediction model is set, which further improves the prediction accuracy of apple odor information. Compared with the traditional prediction method, the system has the characteristics of simple operation, low cost, reliable results, mobile portability, and it avoids the damage to apples in the process of freshness prediction to realize non-destructive testing.Shanxi Provincial Natural Science Foundation General Project, China, Project, grant number 202203021221117. Grantee: Wei Wang

Prediction of planned outages of a power transformer using dissolved gas analysis and nonlinear autoregressive neural networks

Abstract: A power transformer is amongst the more expensive and critical equipment installed in the power system. Unplanned power outages as a result of transformer failure have high recovery costs, reduce the life expectancy of the equipment and interrupt continuous power supply to customers. The fault occurrence in an oil-immersed power transformer results in decomposition of mineral oil which in turn causes dissolved gases to be released. To ensure reliability and availability of power transformers, mineral oil needs to be continuous ly monitored and evaluated through condition monitoring. Dissolved Gas Analysis (DGA) was developed to measure, detect, interpret and analyse dissolved gases in mineral oil of a power transformer. Condition monitoring of transformers constitutes an essential step towards prevention of unplanned breakdowns. The literature reviewed identified limitations in DGA techniques as not all incipient fault conditions can be detected by the diagnostic techniques, which makes it difficult to develop failure probability for power transformers. Considerable efforts in research have been made, in the transformer asset management field, to develop accurate models that will provide reliable incipient fault diagnosis and predict planned outage of a power transformer based on the health condition. Studies demonstrated that the Computational Intelligence (CI) and Artificial Intelligence (AI) techniques have the ability to overcome the limitations in DGA techniques. Since there is a lack of common frameworks to determine the accurate health condition and to predict the downtime of a power transformer, this dissertation provides a critical review on mineral oil sampling processes, DGA, CI and AI techniques used to improve the fault diagnostic of a power transformer and predict the planned outage timely. Preventive maintenance is outlined by the preventive model that is built using a combination of Artificial Neural Network (ANN) with DGA techniques to detect accurate incipient fault conditions. The model uses dissolved gases in mineral oil to detect to identify incipient fault conditions in a power transformer. The ANN Multilayer Perceptron (MLP) Feedforward with Back-Propagation (BP) was built using the concentrations of key combustible dissolved gases as input layer, trained using Levenberg-Marquardt (LM) algorithm to obtain the incipient fault conditions as outputs that are enlisted in gas ratio or IEEE C57.108-2004 methods. The results obtained from comparative diagnosis presented in this work show clear improvement and accuracy in the diagnosis of transformer using a combination of ANN with Rodgers ratio or IEEE C57.108-2004 methods over using the diagnostic techniques independently. Limitations of the gas ratio and Duval Triangle methods make it difficult for the model to make decisions if data presented does not fall within the ratio range scheme and fault zones of a triangle. Unidentified diagnosis can have a severe impact on the life of the power transformers. Furthermore, CI and AI algorithms such as fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS), machine learning (ML), ANN and non-linear autoregressive neural networks have been used in various studies to overcome the limitat ions of DGA techniques. Also, the CI and AI algorithms have their limitations. Therefore, when building the preventive and predictive models for power transformers, it is important to select the combinations of methods or techniques that will circumvent most of the limitations and provide reliable and accurate outcomes. Another model that was developed for predictive maintenance is a nonlinear autoregressive exogenous model (NARX) neural network combined with IEEE C57.108.2004. The predictive model is used to study the historical data of dissolved gases and predict the future gas levels that will be used to identify the incipient fault present and predict the planned outage based on results. The proposed models in this dissertation will manage a service life of power transformers efficiently by associating gas concentration values with incipient fault conditions and serve as an early warning in the power system network by predicting planned outage of a transformer.M.Tech. (Electrical and Electronic Engineering Technology

Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports

Container traffic forecasting is important for the operations and the design steps of a seaport facility. In this study, performances of the novel soft computing models were compared for the container traffic forecasting of principal Turkish seaports (Istanbul, Izmir, and Mersin seaports) with excessive container traffic. Four forecasting models were implemented based on Artificial Neural Network with Artificial Bee Colony and Levenberg-Marquardt Algorithms (ANN-ABC and ANN-LM), Multiple Nonlinear Regression with Genetic Algorithm (MNR-GA), and Least Square Support Vector Machine (LSSVM). Forecasts were carried out by using the past records of the gross domestic product, exports, and population of the Turkey as indicators of socioeconomic and demographic status. Performances of the forecasting models were evaluated with several performance metrics. Considering the testing period, the LSSVM, ANN-ABC, and ANN-LM models performed better than the MNR-GA model considering overall fitting and prediction performances of the extreme values in the testing data. The LSSVM model was found to be more reliable compared to the ANN models. Forecasting part of the study suggested that container traffic of the seaports will be increased up to 60%, 67%, and 95% at the 2023 for the Izmir, Mersin, and Istanbul seaports considering official growth scenarios of Turkey

Recent Advances in Steganography

Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced