Global optimization of case-based reasoning for breast cytology diagnosis

Case-based reasoning (CBR) is one of the most popular prediction techniques in medical domains because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation – its prediction performance is generally lower than other AI techniques like artificial neural networks (ANN). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GA). Our model improves the prediction performance in three ways – (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating useless or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.     

Simultaneous optimization of artificial neural networks for financial forecasting

Artificial neural networks (ANNs) have been popularly applied for stock market prediction, since they offer superlative learning ability. However, they often result in inconsistent and unpredictable performance in the prediction of noisy financial data due to the problems of determining factors involved in design. Prior studies have suggested genetic algorithm (GA) to mitigate the problems, but most of them are designed to optimize only one or two architectural factors of ANN. With this background, the paper presents a global optimization approach of ANN to predict the stock price index. In this study, GA optimizes multiple architectural factors and feature transformations of ANN to relieve the limitations of the conventional backpropagation algorithm synergistically. Experiments show our proposed model outperforms conventional approaches in the prediction of the stock price index.     

Persistent UAV Service: An Improved Scheduling Formulation and Prototypes of System Components

The flight duration of unmanned aerial vehicles (UAVs) is limited by their battery or fuel capacity. As a consequence, the duration of missions that can be pursued by UAVs without supporting logistics is restricted. However, a system of UAVs that is supported by automated logistics structures, such as fuel service stations and orchestration algorithms, may pursue missions of conceivably indefinite duration. This may be accomplished by handing off the mission tasks to fully fueled replacement UAVs when the current fleet grows weary. The drained UAVs then seek replenishment from nearby logistics support facilities. To support the vision of a persistent fleet of UAVs pursuing missions across a field of operations, we develop an improved mixed integer linear programming (MILP) model that can serve to support the system’s efforts to orchestrate the operations of numerous UAVs, missions and logistics facilities. Further, we look toward the future implementation of such a persistent fleet outdoors and develop prototype components required for such a system. In particular, we develop and demonstrate the concerted operation of a scheduling model, UAV onboard vision-based guidance system and replenishment stations.     

Hydrogen production from low temperature WGS reaction on co-precipitated Cu–CeO2 catalysts: An optimization of Cu loading

Low temperature water–gas shift (WGS) reaction has been carried out at the gas hourly space velocity of 72,152 h⁻¹ over Cu–CeO₂ catalyst prepared by a co-precipitation method. Cu loading was optimized to obtain highly active co-precipitated Cu–CeO₂ catalysts for low temperature WGS. 80 wt% Cu–CeO₂ exhibited the highest CO conversion as well as the most stable activity (X_CO > 46% at 240 °C for 100 h). The excellent catalytic performance is mainly due to a strong metal to support interaction, resulting in the prevention of Cu sintering.     

Tablet PC-based Visual Target-Following System for Quadrotors

This paper proposes a vision-based, closed-loop target-following system for quadrotors. The system consists of a vision-based target detection algorithm that uses the color and image moment of a given target. Flight control commands are directly generated based on the offset of the target from the image frame center. The image processing and control algorithms have been implemented on a latest tablet computer, which is capable of running those algorithms in real time. The proposed system was demonstrated using a commercially available quadrotor platform equipped with a forward-facing camera. Experiments and their analyses showed satisfactory target following performance.     

Selective temporal filtering and its application to hand gesture recognition

In temporal data analysis, noisy data is inevitable in both testing and training. This noise can seriously influence the performance of the temporal data analysis. To address this problem, we propose a novel method, termed Selective Temporal Filtering that builds a noise-free model for classification during training and identifies key-feature vectors that are noise-filtered data from the input sequence during testing. The use of these key-feature vectors makes the classifier robust to noise within the input space. The proposed method is validated on a synthetic-dataset and a database of American Sign Language. Using key-feature vectors results in robust performance with respect to the noise content. Futhermore, we are able to show that the proposed method not only outperforms Conditional Random Fields and Hidden Markov Models in noisy environments, but also in a well-controlled environment where we assume no significant noise vectors exist.     

Automatic logo transition detection in digital video contents

The amount of user created contents has been increasing rapidly and is associated with a serious copyright problem. Automatic logo detection and recognition in videos is a natural and efficient way of overcoming the copyright problem. However, logos have varying characteristics, which make logo detection and recognition very difficult. Moreover, logo transitions between two different logos exist in one video comprising several video contents. This disrupts the automatic logo detection and recognition. Therefore, in order to improve logo detection, it is necessary to take into account the logo transitions explicitly. This paper proposes an accurate logo transition detection method for recognizing logos in digital video contents. The proposed method accurately segments a video according to logo and efficiently recognizes various types of logos. The experimental results demonstrate the effectiveness of the proposed method for logo detection and video segmentation according to logo.     

Numerical modeling and simulations of active direct methanol fuel cell (DMFC) systems under various ambient temperatures and operating conditions

Direct methanol fuel cells (DMFCs) are potential candidates for portable backup power generation and auxiliary power units owing to their advantageous features, such as ease of fuel storage and delivery. Optimizing each component of a DMFC system is critical to improving the overall system performance and power density. This paper presents an active DMFC system model, in which a one-dimensional DMFC stack model is combined with major system components, including fuel and water tanks, liquid–gas separator, heat exchangers, pumps, and blowers. The model is implemented using a commercial flow-sheet simulator, ASPEN-HYSYS, and then applied to an active DMFC system to analyze the effects of the DMFC operating parameters and heat management. Special emphasis is placed on establishing active control strategies for the DMFC stack temperature, methanol crossover rate, and water recovery by optimizing the system components and operating conditions. Overall, this study helps identify innovative active DMFC system designs and configurations.