Traditional perception systems for TJA (Traffic Jam Assistance) are mostly implemented by fusing images with radar or lidar. As computer vision techniques become more powerful, cameras can almost replace the need for ...
Traditional perception systems for TJA (Traffic Jam Assistance) are mostly implemented by fusing images with radar or lidar. As computer vision techniques become more powerful, cameras can almost replace the need for radar and lidar in perception tasks, which reduces the hardware cost of the system. In this research, we propose a camera-only perception system for TJA, which is able to provide the information of the vehicles ahead and the drivable area. The proposed system has been evaluated through real-world scenario sequences, and proved that it achieves high robustness, which is highly possible to be adopted for TJA development.
Not only common issues on object detection task need to be deal with, for Unmanned Aerial Vehicle (UAV) applications, small object is one of the critical problems that needs to be solved. YOLOv7 is a powerful network ...
Not only common issues on object detection task need to be deal with, for Unmanned Aerial Vehicle (UAV) applications, small object is one of the critical problems that needs to be solved. YOLOv7 is a powerful network architecture that provides high efficiency and accuracy object detection results. This paper adopts YOLOv7 as an object detection model for two different kinds of targets, one is vehicle, and the other is ocean flotsam. By training the model with open datasets and fine-tuning the model with self-collected datasets, we prove through sequences collected from real-world scenarios that YOLOv7 is able to provide robust and accurate object detection results, including vehicles and ocean flotsam, with real-time efficiency. Based on such experimental result, we confirmed that YOLOv7 can be the baseline for object detection model development.
In this paper, a simple method is proposed to evolve artificial neural networks(ANNs) using augmenting weight matrix method(AWMM). ANNs' architecture and connection weights can be evolved simultaneously by AWMM, a...
In this paper, a simple method is proposed to evolve artificial neural networks(ANNs) using augmenting weight matrix method(AWMM). ANNs' architecture and connection weights can be evolved simultaneously by AWMM, and their structures incrementally are growing up from minimal structure. It is a non-mating method. It employs 5 mutation operators: add connection, add node, delete connection, delete node, and new initial weight. And the connection weight is trained by the simplified alopex method, which is a correlation based method for solving optimization problem. In AWMM, structural information is encoded to weighting matrix, and the matrix is augmenting as the hidden nodes are added.
Today Artificial Intelligence (AI) supports difficult decisions about policy, health, and our personal lives. The AI algorithms we develop and deploy to make sense of information, are informed by data, and based on mo...
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Today Artificial Intelligence (AI) supports difficult decisions about policy, health, and our personal lives. The AI algorithms we develop and deploy to make sense of information, are informed by data, and based on models that capture and use pertinent details of the population or phenomenon being analyzed. For any application area, more importantly in precision medicine which directly impacts human lives, the data upon which algorithms are run must be procured, cleaned, and organized well to assure reliable and interpretable results, and to assure that they do not perpetrate or amplify human prejudices. This must be done without violating basic assumptions of the algorithms in use. Algorithmic results need to be clearly communicated to stakeholders and domain experts to enable sound conclusions. Our position is that AI holds great promise for supporting precision medicine, but we need to move forward with great care, with consideration for possible ethical implications. We make the case that a no-boundary or convergent approach is essential to support sound and ethical decisions. No-boundary thinking supports problem definition and solving with teams of experts possessing diverse perspectives. When dealing with AI and the data needed to use AI, there is a spectrum of activities that needs the attention of a no-boundary team. This is necessary if we are to draw viable conclusions and develop actions and policies based on the AI, the data, and the scientific foundations of the domain in question.
Conventional boost converters operating with hard-switching result in low conversion efficiency and increased electromagnetic interference emissions. In this paper, a cost-efficient passive snubber is proposed with a ...
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ISBN:
(数字)9784885523472
ISBN:
(纸本)9798350349498
Conventional boost converters operating with hard-switching result in low conversion efficiency and increased electromagnetic interference emissions. In this paper, a cost-efficient passive snubber is proposed with a few additional components: two diodes, one capacitor, and one inductor. Moreover, because these snubber components are not located on the main power processing path, they only require low ratings, resulting in improved cost-effectiveness.
The ambiguity function is a measure of a radar's range and Doppler detection capability. Cognitive radar systems require the capability to adjust the waveform in realtime to obtain desired range/Doppler detection ...
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The ambiguity function is a measure of a radar's range and Doppler detection capability. Cognitive radar systems require the capability to adjust the waveform in realtime to obtain desired range/Doppler detection capability while meeting stringent spectral requirements. While the ambiguity function of the waveform input to the transmitter can be simulated, it is the ambiguity function of the transmitter power amplifier's output waveform that will be used for the detection. As such, it is very helpful to be able to measure the ambiguity function output from a power amplifier in the optimization process. This paper describes a technique that can be used to quickly calculate the ambiguity function for the output waveform from the radar amplifier as measured on an oscilloscope. Brief examination is also given to the effect of amplifier nonlinearity on the ambiguity function.
Real-time identification of tool wear in shop floor environment is essential for optimization of machining processes and implementation of automated manufacturing systems. In this paper. the signals obtained from acou...
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Real-time identification of tool wear in shop floor environment is essential for optimization of machining processes and implementation of automated manufacturing systems. In this paper. the signals obtained from acoustic emission and power sensors during machining processes are analyzed and a set of feature parameters characterizing the tool wear condition are extracted. In order to realize the realtime tool wear condition monitoring for different cutting conditions, a sensor integration strategy which combines the information from multiple sensors (acoustic emission sensor and power sensor) and machining parameters is proposed. A neural network based on improved back-propogation algorithm is developed and a prototype scheme for realtime identification of tool wear is implemented. Experiments under different conditions have proved that a higher rate of tool wear identification can be achieved by using the sensor integration model with neural network. The results also indicated that the neural network is a very effective method of sensor integration for online monitoring of tool abnormalities.< >
Mean-shift algorithm shows robust performances in various object-tracking technologies including face tracking. Due to its robustness and accuracy, mean-shift algorithm is regarded as one of the best ways to apply in ...
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Mean-shift algorithm shows robust performances in various object-tracking technologies including face tracking. Due to its robustness and accuracy, mean-shift algorithm is regarded as one of the best ways to apply in object-tracking technology in computer vision fields. However, it has a drawback of getting into a bottleneck state when faced with a speedy object moving beyond its window size within one image frame interval time. The time required to calculate mean-shift vector could be much lessened with lesser memory when color model is adjusted to the previously known target information. This paper shows the building process of target-adjusted model with a non-uniform quantization. The target color model dealt in this paper is the one used for deriving mean-shift vector. It is a kernel model containing both the color and distance information. This paper gives scheme to efficiently deal with color information in the model. Through a proper selection of color bins, unimportant color values were reduced to a small amount. As a result, the computing time of the mean-shift vector in face-tracking was shortened while maintaining robustness and accuracy.
Phylogenies depicting the evolutionary history of genetically heterogeneous subpopulations of cells from the same cancer, i.e., cancer phylogenies, offer valuable insights about cancer development and guide treatment ...
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The problem of determining a path between two nodes in a network that must visit specific intermediate nodes arises in a number of contexts. For example, one might require traffic to visit nodes where it can be monito...
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The problem of determining a path between two nodes in a network that must visit specific intermediate nodes arises in a number of contexts. For example, one might require traffic to visit nodes where it can be monitored by deep packet inspection for security reasons. In this paper a new recursive heuristic is proposed for finding the shortest loopless path, from a source node to a target node, that visits a specified set of nodes in a network. In order to provide survivability to failures along the path, the proposed heuristic is modified to ensure that the calculated path can be protected by a node-disjoint backup path. The performance of the heuristic, calculating a path with and without protection, is evaluated by comparing with an integer linear programming (ILP) formulation for each of the considered problems. The ILP solver may fail to obtain a solution in a reasonable amount of time, especially in large networks, which justifies the need for effective, computationally efficient heuristics for solving these problems. Our numerical results are also compared with previous heuristics in the literature.
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