Fire detection based on hidden Markov models

In this paper, a novel method of real-time fire detection based on HMMs is presented. First, we present an analysis of fire characteristics that provides evidence supporting the use of HMMs to detect fire; second, we propose an algorithm for detecting candidate fire pixels that entails the detection of moving pixels, fire-color inspection, and pixels clustering. The main contribution of this paper is the establishment and application of a hidden Markov fire model by combining the state transition between fire and non-fire with fire motion information to reduce data redundancy. The final decision is based on this model which includes training and application; the training provides parameters for the HMM application. The experimental results show that the method provides both a high detection rate and a low false alarm rate. Furthermore, real-time detection has been effectively realized via the learned parameters of the HMM, since the most time-consuming components such as HMM training are performed off-line.     

Mobile robot localization with gyroscope and constrained Kalman filter

The odometry information used in mobile robot localization can contain a significant number of errors when robot experiences slippage. To offset the presence of these errors, the use of a low-cost gyroscope in conjunction with Kalman filtering methods has been considered by many researchers. However, results from conventional Kalman filtering methods that use a gyroscope with odometry can unfeasible because the parameters are estimated regardless of the physical constraints of the robot. In this paper, a novel constrained Kalman filtering method is proposed that estimates the parameters under the physical constraints using a general constrained optimization technique. The state observability is improved by additional state variables and the accuracy is also improved through the use of a nonapproximated Kalman filter design. Experimental results show that the proposed method effectively offsets the localization error while yielding feasible parameter estimation.     

Modeling and simulation of powered hip orthosis by pneumatic actuators

In this study, a simulation model for a powered hip orthosis (PHO) with air muscles to predict the gait of paraplegics is presented which can be used as a design tool for hip orthoses. Before simulation, mathematical models for a human dummy with an orthosis and a pneumatic muscle actuator were generated. For the air muscle, coefficients required were obtained by static and dynamic experiments of the air muscle and experiments for the valve controlling the air pressure. The computation was conducted on the ADAMS package together with MATLAB. Computer simulation of the flexion of hip joints by the pneumatic muscle results in similar values to those from gait analysis. With the development of a simulation model for a PHO, the gait simulation model using pneumatic muscles can be used to analyze and evaluate the characteristics and efficiency of a PHO by setting the input and boundary conditions.     

Driving characteristics of the electrowetting-on-dielectric device using atomic-layer-deposited aluminum oxide as the dielectric

Electrowetting on dielectric (EWOD) is useful in manipulating droplets for digital (droplet-based) microfluidics, but its high driving voltage over several tens of volts has been a barrier to overcome. This article presents the characteristics of EWOD device with aluminum oxide (Al₂O₃, ε _r  ≈ 10) deposited by atomic layer deposition (ALD), for the first time as the high-k dielectric for lowering the EWOD driving voltage substantially. The EWOD device of the single-plate configuration was fabricated by several steps for the control electrode array of 1 mm × 1 mm squares with 50 μm space, the dielectric layer of 1,270 Å thick ALD Al₂O₃, the reference electrode of 20 μm wide line electrode, and the hydrophobic surface treatment by Teflon-AF coating, respectively. We observed the movement of a 2 μl water droplet in an air environment, applying a voltage between one of the control electrodes and the reference electrode in contact with the droplet. The droplet velocity exponentially depending on the applied voltage below 15 V was obtained. The measured threshold voltage to move the droplet was as low as 3 V which is the lowest voltage reported so far in the EWOD researches. This result opens a possibility of manipulating droplets, without any surfactant or oil treatment, at only a few volts by EWOD using ALD Al₂O₃ as the dielectric.     

Design optimization of a viscous micropump with two rotating cylinders for maximizing efficiency

This study presents design optimization of a viscous micropump with two rotating cylinders. The desired performance of the pump is to maximize the pumping efficiency while satisfying the constraints on flow rate and geometry. As a preliminary step, the effects of geometric configurations on the pumping performance are investigated by carrying out parametric studies using an unstructured grid Navier–Stokes method. Next, an optimization problem is formulated to determine the design variable values which maximize the pumping efficiency subject to the constraints. Then, a computational procedure, combining the analysis method with a sequential metamodel-based optimization method, is established to solve the optimization problem formulated. Finally, this procedure is applied to the optimization of ten design cases with varying flow rates specified. The optimization results demonstrate the effectiveness of the design optimization method presented in this study by showing that the efficiencies of the optimally designed micropumps are enhanced without any constraint violations.     

Adaptive product tracking in RFID-enabled large-scale supply chain

The Radio Frequency Identification (RFID) technology is gradually being adopted and deployed for product flow management in the supply chain. In order to track RFID-tagged products efficiently in the RFID-enabled, large-scale supply chain, this paper first presents the design of a product tracking system that can collaborate with the EPC Network, a suite of network services for RFID data management in the supply chain. Next, we explain a product monitoring procedure that is performed by comparing the actual path of a product with its planned path. Finally, we develop an adaptive product search algorithm based on a reinforcement learning technique to efficiently locate a product deviated from its planned path. Experiment results are provided to show the performance of the search algorithm.     

Self-adaptive and dynamic clustering for online anomaly detection

As recent Internet threats are evolving more rapidly than ever before, one of the major challenges in designing an intrusion detection system is to provide early and accurate detection of emerging threats. In this study, a novel framework is developed for fully unsupervised training and online anomaly detection. The framework is designed so that an initial model is constructed and then it gradually evolves according to the current state of online data without any human intervention. In the framework, a self-organizing map (SOM) that is seamlessly combined with K-means clustering is transformed into an adaptive and dynamic algorithm suitable for real-time processing. The performance of the proposed approach is evaluated through experiments using the well-known KDD Cup 1999 data set and further experiments using the honeypot data recently collected from Kyoto University. It is shown that the proposed approach can significantly increase the detection rate while the false alarm rate remains low. In particular, it is capable of detecting new types of attacks at the earliest possible time.     

Intelligent adaptive process control using dynamic deadband for semiconductor manufacturing

This paper proposes an efficient control method to minimize process error and to reduce process variance in semiconductor manufacturing. The photolithography (photo) process forms a complex semiconductor circuit and is important for quality. Obstacles to the process include the facility itself, vibration, wear and tear, product/process changes and environmental influences. Control methodologies being currently used to address these issues often amplify the variation of the process by failing to perform adequate process control. Therefore, this paper proposes an effective process control method to reduce process variance by quickly detecting and identifying process disturbances and accurately reflecting the degree of change to process control. This study proposes dynamic deadband control that uses a region (band) to detect the status of a process change. It adjusts the process control based on the changes detected. In this research, the semiconductor manufacturing company is supported to perform control that is more precise and reduces fluctuations by producing products of uniform quality. In addition, it can contribute to yield due to the quality incentive and increased process control of semiconductor manufacturing.     

Logistic regression in sealed-bid auctions with multiple rounds: Application in Korean court auction

This paper proposes a forecasting method for court auction information system using logistic regression model with heterogeneity across the multiple round. The goal is to predict whether an individual auction item in a certain round will be sold or not. A simple linear regression and the least angle regression (LARS) containing random effect terms were used to select meaningful variables for our logit model. The link function of the proposed logit model is represented by two bundles of parameters. The former part consists of the parameters whose values do not change over rounds. The latter part has parameters whose values interact with rounds. The observed data corresponding to an appraiser price as well as an intercept term reflecting local characteristics are used without any change. Data that corresponds to all the other parameters is not directly used, but transformed based on similarities between the original item and the surrounding auction items being recommended by the court auction experts. We tested the Bayesian logistic regression by establishing different priors: Dunson’s prior, Gelman’s prior and Ansari’s prior. Dunson’s prior was found to perform the best. Little significant difference was found between the results of the other two priors. These findings indicate that logistic regression taking the heterogeneity of multi-round into account performs better than a one-layered neural network over all time periods.     

Service improvement by business process management using customer complaints in financial service industry

In financial service industry, service improvement should be considered from process viewpoint and customer viewpoint because the value creation is ultimately linked with internal business processes on the back office and customers are involved as a co-producer of value. In this perspective, customer complaints through call centers are adequate to support the analysis for service improvement in financial service industry. In this study, we propose a web-based decision support system for business process management employing customer complaints, namely Voice of the Customer (VOC), and its handling data for service improvement. It involves VOC conversion for data enrichment and includes analysis of summarization, exception and comparison. The proposed system is evaluated on a major credit card company in South Korea.