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제안 방법
- Wen [9] proposed an improved convolutional neural network (CNN) based on Gabor filter to achieve gait recognition. In this paper, a Gabor filter-based walking feature extraction layer was inserted into the existing CNN and used to extract the walking features from the walking silhouette image. A metric learning technique was used to calculate the distance between two walks and a k-nearest neighbor (KNN) classifier was used to classify the walks.
- Thus, the input image is calculated as a threshold value for several Boolean maps, and the initial protrusion map is calculated as the sum of the weights of the generated Boolean maps. Morphological operations and Gaussian filters are used in this paper to further refine the initial map to produce the final high-quality extrusion map.
- This paper considered the improvement and extension of algorithms in a variety of fields, including agriculture, personal authentication, wireless networks, games, biometrics, and image recognition. Therefore, the present paper comprehensively discusses the following technologies: improved defect detection algorithm, survey of deep learning in agriculture techniques, next-generation personal authentication scheme, rate adaptation with Q-learning, evaluation of operational security, reinforcement learning activation functions, fast leaf recognition and retrieval, intelligent management and service, gait recognition, intelligent on-demand routing protocol, thangka image inpainting algorithm, classroom rollcall system, localization algorithm, hybrid genetic ant colony optimization (ACO) algorithm, visual saliency detection, heuristic and statistical prediction algorithms, object augmentation scheme, and dynamic action space handling method. The present paper mainly aims to rapidly provide popular and trendy research to researchers.
- [12] proposed a new face recognition model based on game theory for classroom rollover by using CNNs with outstanding performance in the field of face recognition. This model uses multiple face images as inputs and constructs a student identity list by identifying each face with a confidence score. The optimization goal is then determined by tracking the face with the same identity or low confidence.
- [8] introduced an Internet-based intelligent gas valve management and service system design scheme. This paper added a sensor and GPRS (General Packet Radio Service) module to the existing gas valve, and added a networking function between the gas valve and the server while using wireless packet communication technology. The authors suggested that the method proposed in this paper is more convenient and efficient than the existing gas valve management and service projects.
- This paper considered the improvement and extension of algorithms in a variety of fields, including agriculture, personal authentication, wireless networks, games, biometrics, and image recognition. Therefore, the present paper comprehensively discusses the following technologies: improved defect detection algorithm, survey of deep learning in agriculture techniques, next-generation personal authentication scheme, rate adaptation with Q-learning, evaluation of operational security, reinforcement learning activation functions, fast leaf recognition and retrieval, intelligent management and service, gait recognition, intelligent on-demand routing protocol, thangka image inpainting algorithm, classroom rollcall system, localization algorithm, hybrid genetic ant colony optimization (ACO) algorithm, visual saliency detection, heuristic and statistical prediction algorithms, object augmentation scheme, and dynamic action space handling method.
- Lee [6] proposed an agent using a reinforcement learning algorithm and a neural network to evaluate which activation function can get the best result when an agent learns a game through reinforcement learning in a 2D racing game environment. This paper evaluated the activation functions in the network by switching them together and measured the reward, the output of the advantage function, and the output of the loss function while training and testing them. The experimental result showed that the best activation function for the agent to learn the game and the difference between the best and the worst was 35.
- This paper features 18 high-quality articles following a rigorous review process. This paper reviewed the technologies developed in various research fields, such as improved defect detection algorithm, survey of deep learning in agriculture techniques, next-generation personal authentication scheme, rate adaptation with Q-learning, evaluation of operational security, reinforcement learning activation functions, fast leaf recognition and retrieval, intelligent management and service, gait recognition, intelligent on-demand routing protocol, Thangka image inpainting algorithm, classroom roll-call system, localization algorithm, hybrid genetic ACO algorithm, visual saliency detection, heuristic and statistical prediction algorithms, object augmentation scheme, and dynamic action space handling method.
- Two algorithms are compared to verify the effectiveness of the proposed algorithm. This paper selected a common denim fabric defect sample image, which includes normal image, weft missing, hole breaking, and oil pollution. The experimental results showed a good average detection rate of common defects of denim is more than 91.
- [2] surveyed the development of deep neural-based work efforts in the agriculture domain over the last 5 years and investigated 32 research contributions that apply deep learning techniques to the agriculture domain. This paper surveyed different types of deep neural network architectures in agriculture and the current state-of-the-art methods. They found that deep learning was better in performance than other technologies, which concluded that deep learning will receive more attention and broader applications in future research.
- Cho [4] proposed a reinforcement learning agent based on Q-learning to control the data transmission rates of nodes in carrier sensing multiple access with collision avoidance (CSMA/CA)-based wireless networks. This paper used the ns3-gym framework to simulate reinforcement learning and investigated the effects of the parameters of Q-learning on the performance of the reinforcement learning agent. The experimental results showed that the proposed reinforcement learning agent adequately adjusts the modulation and coding scheme (MCS) levels according to the changes in the network, and achieves a high throughput comparable to those of the existing data transmission rate adaptation schemes.
- In order to express the evaluation index in the evaluation process and to reduce the uncertainty and ambiguity of the evaluation element, the use of the interval number was suggested. Through this, the objectivity and reliability of the evaluation results were improved, and a new idea was provided for the evaluation of operational safety management of high-speed rail passenger stations.
- Plant leaf species recognition using images is difficult because of the large inter-class and small distances between different species. Thus, the authors developed an angular description method of leaf contours by using a new scale generation rule to address such difficulty. The evaluation results show that the proposed method has a faster computation time than the existing method and has high recognition and search accuracy.
- However, the technical limitations of the EEG signal interpretation by using dry electrodes were regarded as an obstacle to the use of the EEG-based encryption system. To overcome this problem, this paper used not only EEG signals but also deep learning techniques that is a multinomial classification with a one-hot encoding.
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