Hop chains: Secure routing and the establishment of distinct identities

We present a secure routing protocol that is immune to Sybil attacks and that can tolerate collusion of Byzantine routers. It can tolerate either initial collusion of Byzantine routers or runtime collusion of non-adjacent Byzantine routers, both in the absence of runtime collusion between adjacent routers. For these settings, the calculated distance from a destination to a node is not smaller than the actual shortest distance from the destination to the node. The protocol can also simultaneously tolerate initial collusion of Byzantine routers and runtime collusion of adjacent Byzantine routers but in the absence of runtime collusion between non-adjacent routers. For this setting, it guarantees a bound on the difference between the calculated distance and the actual shortest distance. The bound depends on the number of Byzantine routers on a path. The protocol makes very weak timing assumptions and requires synchronization only between neighbors or second neighbors. We propose to use this protocol for secure localization of routers using hop-count distances, which can be then used as a proof of identity of nodes.     

Multisensor based security robot system for intelligent building

Intelligent building can provide safety, convenience, efficiency and entertainment for life in the 21st century. The most importance role of the intelligent building is the security system. We develop a multi sensor-based intelligent security robot (ISR) that is widely employed in intelligent buildings. The intelligent security robot can detect abnormal and dangerous situations and notify users. The robot has the shape of cylinder and its diameter, height, and weight are 50 cm, 130 cm and 100 kg respectively. The function of the ISR contains six parts. There is the software development system; avoiding obstacle and motion planning system, image system, sensor system, remote supervise system and other systems. We develop a multi sensor-based sensor system in the ISR. We use multiple multisensor fusion algorithms to get an exact decision in the detection subsystem of the sensor system. There is an adaptive fusion method, a rule based method, and a statistical signal method. We demonstrate the remote supervisory system to control the ISR using a direct control mode and a behavior control mode. We think that the man–machine interface in a security robot system must have mobility and convenience. Therefore, we use a touch screen to display the system state, and design a general user interface (GUI) to service the user and visitors. The user can remotely control the appliance using a cell phone through a GSM modem, too. The appliance module can feedback reaction results to the user through a cell phone. Finally, we implement the fire detection system in the intelligent security robot (Chung-Cheng-I). If a fire occurs, the intelligent security robot can find out the fire source using the fire detection system. In intruder detection, we program the same scenario to detect the intruder using the intelligent security robot. The intelligent security robot transmits the message of the detection result to the user using a GSM modem for a fire event or intruder, and transmits the detection result to a client computer through the internet.     

Multiscale Bayesian texture segmentation using neural networks and Markov random fields

This paper presents a wavelet-based texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields (MRF) in a multi-scale Bayesian framework. Inputs and outputs of MLP networks are constructed to estimate a posterior probability. The multi-scale features produced by multi-level wavelet decompositions of textured images are classified at each scale by maximum a posterior (MAP) classification and the posterior probabilities from MLP networks. An MRF model is used in order to model the prior distribution of each texture class, and a factor, which fuses the classification information through scales and acts as a guide for the labeling decision, is incorporated into the MAP classification of each scale. By fusing the multi-scale MAP classifications sequentially from coarse to fine scales, our proposed method gets the final and improved segmentation result at the finest scale. In this fusion process, the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. Our texture segmentation method was applied to segmentation of gray-level textured images. The proposed segmentation method shows better performance than texture segmentation using the hidden Markov trees (HMT) model and the HMTseg algorithm, which is a multi-scale Bayesian image segmentation algorithm.     

A variant of the SOM algorithm and its interpretation in the viewpoint of social influence and learning

The conventional self-organizing feature map (SOM) algorithm is usually interpreted as a computational model, which can capture main features of computational maps in the brain. In this paper, we present a variant of the SOM algorithm called the SOM-based optimization (SOMO) algorithm. The development of the SOMO algorithm was motivated by exploring the possibility of applying the SOM algorithm in continuous optimization problems. Through the self-organizing process, good solutions to an optimization problem can be simultaneously explored and exploited by the SOMO algorithm. In our opinion, the SOMO algorithm not only can be regarded as a biologically inspired computational model but also may be regarded as a new approach to a model of social influence and social learning. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm.     

Microinjection molded disposable microfluidic lab-on-a-chip for efficient detection of agglutination

Previous diagnosing methods based on agglutination have a limitation in view of emergency and point-of-care diagnoses due to the requirement of large scale equipments and much agglutination time. In this paper, we propose a low cost microfluidic lab-on-a-chip for more efficient detection of agglutination. In the present lab-on-a-chip, two inlet microwells, flow guiding microchannels, chaotic micromixer and reaction microwell are fully integrated. Mold inserts for the lab-on-a-chip were manufactured by UV photolithography and nickel electroplating process. The complete lab-on-a-chip was realized by the microinjection molding of cyclic olefin copolymer and the subsequent thermal bonding. The improved serpentine laminating micromixer, developed by our group, integrated in the lab-on-a-chip showed the high-level of chaotic mixing, thereby enabling us to get a reliable mixing of sample and reagent. The performance of the fabricated lab-on-a-chip was demonstrated by agglutination experiments with simulated bloods of 10 μl and simulated sera of 10 μl. The results of agglutination inside the reaction microwell were clearly read by means of the level of light transmission. The present microfluidic lab-on-a-chip could be widely applied to various clinical diagnostics based on agglutination tests.     

Situation reactive approach to Vendor Managed Inventory problem

In this research, we deal with VMI (Vendor Managed Inventory) problem where one supplier is responsible for managing a retailer’s inventory under unstable customer demand situation. To cope with the nonstationary demand situation, we develop a retrospective action-reward learning model, a kind of reinforcement learning techniques, which is faster in learning than conventional action-reward learning and more suitable to apply to the control domain where rewards for actions vary over time. The learning model enables the inventory control to become situation reactive in the sense that replenishment quantity for the retailer is automatically adjusted at each period by adapting to the change in customer demand. The replenishment quantity is a function of compensation factor that has an effect of increasing or decreasing the replenishment amount. At each replenishment period, a cost-minimizing compensation factor value is chosen in the candidate set. A simulation based experiment gave us encouraging results for the new approach.     

A method of stepwise benchmarking for inefficient DMUs based on the proximity-based target selection

DEA is a useful nonparametric method of measuring the relative efficiency of a DMU and yielding a reference target for an inefficient DMU. However, it is very difficult for inefficient DMUs to be efficient by benchmarking a target DMU which has different input use. Identifying appropriate benchmarks based on the similarity of input endowment makes it easier for an inefficient DMU to imitate its target DMUs. But it is rare to find out a target DMU, which is both the most efficient and similar in input endowments, in real situation. Therefore, it is necessary to provide an optimal path to the most efficient DMU on the frontier through several times of a proximity-based target selection process. We propose a dynamic method of stepwise benchmarking for inefficient DMUs to improve their efficiency gradually.The empirical study is conducted to compare the performance between the proposed method and the prior methods with a dataset collected from Canadian Bank branches. The comparison result shows that the proposed method is very practical to obtain a gradual improvement for inefficient DMUs while it assures to reach frontier eventually.     

恒温供水装置的自动控制

恒温水是能效标定的重要组成部分.针对目前国内外的研究现状,提出了一种新型的自动恒温供水系统.系统采用制冷半导体和电加热器实现对温度的调节,同时利用积分分离PID抑制系统超调,并应用固态继电器构建开关功放,最后通过ARM LPC2210控制器对系统进行了闭环自动控制.实验结果表明,系统出水温度精确、稳定、可靠;系统在暖水炉、空调等能效标定中将得到广泛应用.     

时间序列分析在轨道交通客流预报中的应用研究

时间序列分析是动态数据分析的重要方法,在多学科领域中得到广泛的研究和运用.研究时间序列分析在城市轨道交通自动售检票系统数据分析中的应用方法,并结合某线路AFC系统的历史沉淀数据,给出两个完整的时间序列分析案例,其分析方法能够为AFC运营管理提供有效决策的手段.