Pine wilt disease spreading prevention system using semantic segmentation

Pine wilt disease is a disease that affects ecosystems by rapidly killing trees in a short period of time due to the close interaction between three factors such as trees, mediates, and pathogens. There is no 100% mortality infectious forest pests. According to the Korea Forest Service survey, as of April 2019, the damage of pine re-nematode disease was about 490,000 dead trees in 117 cities, counties and wards across the country. It's a fatal condition. In order to prevent this problem, this paper proposes a system that detects dead trees, early infection trees, and the like, using deep learning-based semantic segmentation. In addition, drones were used to photograph the area of the forest, and a separate pixel segmentation label could be used to identify three levels of transmission information: Suspicion, attention, and confirmation. This allows the user to grasp information such as area, location, and alarm to prevent the spread of re-nematode disease

Personal customized recommendation system reflecting purchase criteria and product reviews sentiment analysis

As the size of the e-commerce market grows, the consequences of it are appearing throughout society. The business environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the user's subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final sentiment score is weighted according to the purchase criteria priority, recommends the results to the user

Human activity recognition by using convolutional neural network

In recent years, many researchers have studied the HAR (Human Activity Recognition) system. HAR using smart home sensor is based on computing in smart environment, and intelligent surveillance system conducts intensive research on peripheral support life. The previous system studied in some of the activities is a fixed motion and the methodology is less accurate. In this paper, vision-based studies using thermal imaging cameras improve the accuracy of motion recognition in intelligent surveillance systems. We use one of the deep learning architectures widely used in image recognition systems called Convolutional Neural Networks (CNN). Therefore, we use CNN and thermal cameras to provide accuracy and many features through the proposed method

Product recommendation system based user purchase criteria and product reviews

In this paper, we propose a system that provides customized product recommendation information after crawling product review data of internet shopping mall with unstructured data, morphological analysis using Python. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product. And extracts and analyzes only the review including the purchase criterion selected by the user among the product reviews left by other users. The positive and negative evaluations contained in the extracted product review data are quantified and using the average value, we extract the top 10 products with good product evaluation, sort and recommend to users. And provides user-customized information that reflects the user's preference by arranging and providing a center around the criteria that the user occupies the largest portion of the product purchase. This allows users to reduce the time it takes to purchase a product and make more efficient purchasing decisions

Sensor data identification based reagent cabinet management system

Recently, a reagent cabinet is used in a laboratory or a laboratory is required to have a system capable of identifying a dangerous situation through sensor data as various sensors are utilized. The existing system identifies the dangerous situation through various sensor data, but there is a problem that the server performs all the operations and the operation of the device is performed manually. In order to solve this problem, this paper proposes a system that can identify the dangerous situation and automatically operate the equipment through the internal environment data of the reagent cabinet. Identification of the hazardous situation is done through the master node used in the reagent cabinet, not the server. The server can continuously update the sensor data through the master node and monitor the real-time status of the reagent cabinet through the application. In this way, it is expected that the risk situation will be promptly addressed by identifying the dangerous situation in the reagent cabinet and operating the device

Fruit tree disease classification system using generative adversarial networks

Smart farm refers to a farm that can remotely and automatically maintain proper growth and management of crops and livestock by integrating technology with agriculture. Currently, smart farms are concentrated in the field of smart horticulture, and although spreading research is being conducted in limited spaces. In addition, it is difficult to obtain a sufficient amount of data to be used for learning, and there is a problem that data imbalance occurs because it is difficult to obtain a similar amount for each class. In this paper, we propose a method to amplify a small amount of data and to solve the problems of imbalance data by using a feature that can learn to mimic the data of a generative adversarial network. The proposed method can create dataset of various crops and also show high hit rate. Dataset generated from crops would be used to solve problems of data imbalance by learning

Body Information Analysis based Personal Exercise Management System

Recently, people's interest in health is deepening. So health-related systems are being developed. Existing exercise management systems provided users with exercise related information using PC or smart phone. However, there is a problem that the accuracy of the algorithm for analyzing the user's body information and providing information is low.In this paper, we analyze users' body mass index (BMI) and basal metabolic rate (BMR) and we propose a system that provides the user with necessary information through recommendation algorithm. It informs the user of exercise intensity and momentum, and graphs the exercise history of the user. It also allows the user to refer to the fitness history of other users in the same BMI group. This allows the user to receive more personalized services than the existing exercise management system, thereby enabling efficient exercise

Web Server-based Distributed Machine Socialization System

In recent years, there has been an increasing trend of offering services that are useful to users, such as Google's Nest, through machine socialization between parts and devices in specific spaces such as automobiles, homes, and factories. The existing inter - device collaboration system is a centralized system using router, and it controls collaboration between devices by building OpenWrt and web server on router. However, due to the limited hardware resources on the router, it generates network traffic congestion as the number of requests from the client increases or the number of clients connected to the server increases. In this paper, we propose a distributed machine collaboration system based on web server using inter - device collaboration algorithm. The study of Micro Controller Unit (MCU) has reduced the traffic incidence by solving the request sent to the router from each device by oneself

An Optimization Method for Personnel Statistics Based on YOLOv4 + DPAC

Compared to traditional detection methods, image-based flow statistics that determine the number of people in a space are contactless, non-perceptual, and high-speed statistical methods that have broad application prospects and potential economic value in business, education, transportation, and other fields. In this paper, we propose that the distributed probability-adjusted confidence (DPAC) function can optimize the reliability of model prediction according to the actual situation. That is, the reliability can be adjusted using the distribution characteristics of the target in the field of view, and a target can be determined with a confidence level that is greater than 0.5 and more accurately. DPAC can assign different target occurrence probability weights to different regions according to target distribution. Adding the DPAC function to a YOLOv4 network model on the basis of having the target confidence of the YOLOv4 network can reduce or improve confidence according to the target distribution and can then output the final confidence level. Using YOLOv4 + DPAC on the brainwash dataset can improve precision by 0.05% compared to the YOLOv4 model when the target confidence threshold is equal to 0.5; it can improve the recall of the model by 0.12% and the AP of the model by 0.12%. This paper also proposes that the distribution in the DPAC function be obtained based on unsupervised learning and verifies its effectiveness

Gated Attention Recurrent Neural Network: A Deeping Learning Approach for Radar-Based Precipitation Nowcasting

Precipitation nowcasting predicts the future rainfall intensity in local areas in a brief time that impacts directly on human life. In this paper, we express the precipitation nowcasting as a spatiotemporal sequence prediction problem. Predictive learning for a spatiotemporal sequence aims to construct a model of natural spatiotemporal processes to predict the future frames based on historical frames. The spatiotemporal process is an abstraction of some of the spatial things in nature that change with time, and they usually do not change very dramatically. To simplify the model and facilitate the training, we considered that the spatiotemporal process satisfies the generalized Markov properties. The natural spatiotemporal processes are nonlinear and non-stationary in many aspects. The processes are not satisfied with the first-order Markov properties when making predictions, such as the nonlinear movement, expansion, dissipation, and intensity enhancement of echoes. To describe such complex spatiotemporal variations, higher-order Markov models need to be used for the modeling. However, many of the previous models for spatiotemporal prediction constructed were based on first-order Markov properties, losing information on the higher-order variations. Thus, we propose a recurrent neural network which satisfies the multi-order Markov properties to create more accurate spatiotemporal predictions. In this network, the core component is the memory cell structure of the gated attention mechanism, which combines the current input information, extracts the historical state that best matches the existing input from the historical multi-period memory information, and then predicts the future. Through this principle of the gated attention, we could extract the historical state information that is richer and deeper to predict the future and more accurately describe the changing characteristics of motion. The experiments show that our GARNN network captures the spatiotemporal characteristics better and obtains excellent results in the precipitation forecasting with radar echoes