In this paper, we propose two general multiple instance active learning (MIAL) algorithms, multiple-instance active learning with a simple margin strategy (S-MIAL) and multiple- instance active learning with fisher in...
详细信息
In this paper, we propose two general multiple instance active learning (MIAL) algorithms, multiple-instance active learning with a simple margin strategy (S-MIAL) and multiple- instance active learning with fisher information (F-MIAL), and apply them to the relevance feedback in localized content based image retrieval (LCBIR). S-MIAL considers the most ambiguous picture as the most valuable one, while F-MIAL can utilize the fisher information and analyze the value of the unlabeled pictures by assigning different labels to them. We show that F-MIAL can be integrated more naturally into the multiple instance learning scenario. In experiments, we will show their superior performances on some real-world image datasets.
Dezert-smarandache theory (DSmT) was extended with fuzzy theory by considering the different Fuzzy T-norm operators, in order to develop a more general and flexible combinational rule for more extensive application. A...
详细信息
Dezert-smarandache theory (DSmT) was extended with fuzzy theory by considering the different Fuzzy T-norm operators, in order to develop a more general and flexible combinational rule for more extensive application. At the same time, fuzzy-extended DSmT was applied to mobile robot's sensing environment with the help of new self-localization method based on δ neighboring field appearance matching and also the perception effect was compared with different T-norm operators. Finally, an effective approach to solv sensing fusion of uncertainty environment was found.
B-scan ultrasound is the primary means for the diagnosis of fatty liver. However, due to use of various ultrasound equipments, poor quality of ultrasonic images and physical differences of patients, fatty liver diagno...
B-scan ultrasound is the primary means for the diagnosis of fatty liver. However, due to use of various ultrasound equipments, poor quality of ultrasonic images and physical differences of patients, fatty liver diagnosis is mainly qualitative, and often depends on the subjective judgment of technicians and doctors. Therefore, computer-aided feature extraction and quantitative analysis of liver B-scan ultrasonic images will help to improve clinical diagnostic accuracy, repeatability and efficiency, and could provide a measure for severity of hepatic steatosis. This paper proposed a novel method of fatty liver diagnosis based on liver B-mode ultrasonic images using support vector machine (SVM). Fatty liver diagnosis was transformed into a pattern recognition problem of liver ultrasound image features. According to the different characteristics of fatty liver and healthy liver, important image features were extracted and selected to distinguish between the two categories. These features could be represented by near-field light-spot density, near-far-field grayscale ratio, grayscale co-occurrence matrix, and neighborhood gray-tone difference matrix (NGTDM). A SVM classifier was modeled and trained using the clinical ultrasound images of both fatty liver and normal liver. It was then exploited to classify normal and fatty livers, achieving a high recognition rate. The diagnostic results are satisfactorily consistent with those made by doctors. This method could be used for computer-aided diagnosis of fatty liver, and help doctors identify the fatty liver ultrasonic images rapidly, objectively and accurately.
A multi-agent social evolutionary algorithm for the precedence and resource constrained single-mode project optimization scheduling (RCPSP-MASEA) is proposed. RCPSP-MASEEA is used to obtain the optimal scheduling sequ...
详细信息
A multi-agent social evolutionary algorithm for the precedence and resource constrained single-mode project optimization scheduling (RCPSP-MASEA) is proposed. RCPSP-MASEEA is used to obtain the optimal scheduling sequences so that the duration of the project is minimized. With the intrinsic properties of RCPSP in mind, the multi-agent systems, social acquaintance net and evolutionary algorithms are integrated to form a new algorithm. In this algorithm, all agents live in lattice-like environment. Making use of the designed behaviors, RCPSP-MASEA realizes the ability of agents to sense and act on the environment in which they live, and the local environments of all the agents are constructed by social acquaintance net. Based on the characteristics of project optimization scheduling, the encoding of solution, the operators such as competitive, crossover and self-learning are given. During the process of interacting with the environment and the other agents, each agent increases energy as much as possible, so that RCPSP-MASEA can find the optima. Through a thorough computational study for a standard set of project instances in PSPLIB, the performance of algorithm is analyzed. The experimental results show RCPSP-MASEA has a good performance and it can reach near-optimal solutions in reasonable times. Compared with other heuristic algorithms, RCPSP-MASEA also has some advantages.
A resource-constrained transport task scheduling problem (RCTTSP) with two optimal objectives was considered, and a multi-objective hybrid genetic algorithm (HGA) was proposed. The proposed algorithm used the serial s...
详细信息
A resource-constrained transport task scheduling problem (RCTTSP) with two optimal objectives was considered, and a multi-objective hybrid genetic algorithm (HGA) was proposed. The proposed algorithm used the serial scheduling method to initialize the population and evaluated the individual. It used the weighted sum method and the rank-based fitness assignment method to assign the individual fitness. Firstly, this paper described the multi-objective RCTTSP and presented the principle of the HGA, and then developed the algorithm to implement several experimental cases with different problem size;lastly the effectiveness and efficiency of the algorithm were compared. The numerical result indicated that the proposed multi-objective HGA can resolve the proposed multi-objective RCTTSP efficiently.
Intensive task-oriented repetitive physical therapies provided by individualized interaction between the patient and a rehabilitation specialist can improve hand motor performance in patients survived from stroke and ...
详细信息
Intensive task-oriented repetitive physical therapies provided by individualized interaction between the patient and a rehabilitation specialist can improve hand motor performance in patients survived from stroke and traumatic brain injury. However, the therapy process is long and expensive and difficult to evaluate quantitatively and objectively. The goal of this research is to develop a novel wearable device for robotic assisted hand repetitive therapy. We designed a pneumatic muscle (PM) driven therapeutic device that is wearable and provides assistive forces required for grasping and release movements. The robot has two distinct degrees of freedom at the thumb and the fingers. The embedded sensors feedback position and force information for robot control and quantitative evaluation of task performance. It has the potential of providing supplemental at-home therapy in addition to in the clinic treatment.
An efficient image denoising algorithm is introduced. Firstly, image pixels are classified into noisy pixels and noise-free pixels by four directional operators. Then an adaptive weighted median filter is designed to ...
详细信息
An efficient image denoising algorithm is introduced. Firstly, image pixels are classified into noisy pixels and noise-free pixels by four directional operators. Then an adaptive weighted median filter is designed to remove and restore the detected noisy pixels and keep the noise-free ones unchanged. Experimental results indicate that the proposed algorithm preserves image details well while removing impulsive noise efficiently, and its filtering performance is significantly superior to the classical median filter and some other typical and recently developed improved median filters.
DNA computing is a new vista of computation, which is of biochemical type. Since each piece of information is encoded in biological sequences, their design is crucial for successful DNA computation. DNA sequence desig...
详细信息
DNA computing is a new vista of computation, which is of biochemical type. Since each piece of information is encoded in biological sequences, their design is crucial for successful DNA computation. DNA sequence design is involved with a number of design criteria, which is difficult to be solved by the traditional optimization methods. In this paper, the multi-objective carrier chaotic evolution algorithm (MCCEA) is introduced to solve the DNA sequence design problem. By merging the chaotic search base on power function carrier, a set of good DNA sequences are generated. Furthermore, the simulation results show the efficiency of our method.
In noncooperative Iris recognition one should deal with uncontrolled behavior of the subject as well as uncontrolled lighting conditions. That means imperfect focus, contrast, brightness, and orientation among the oth...
详细信息
In noncooperative Iris recognition one should deal with uncontrolled behavior of the subject as well as uncontrolled lighting conditions. That means imperfect focus, contrast, brightness, and orientation among the others. To cope with this situation we propose to take iris images at both near infrared (NIR) and visible light (VL) and use them simultaneously for recognition. In this paper, a novel approach for iris recognition is proposed so that extracted features of NIR and VL images are fused to improve the recognition rate. When the images do not have enough quality due to focus, contrast, etc., effects of feature fusion is more pronounced. This is the situation in UTIRIS database, which is used in our experiments. Experimental results show that the proposed approach, especially in small training samples, leads to a remarkable improvement on recognition rate compared with either NIR or VL recognition.
暂无评论