The encryption standard, which has been widely used, is computationally secured. It is reported that encryption standard becomes vulnerable against side-channel attacks (SCA) when it was incorporated in hardware. Therefore, various measures against SCA have been proposed. Evaluation and verification of vulnerability against SCA are the most important priority for the measures. This paper proposes a new method for efficient evaluation of SCA measures on design phase of LSI. The proposed method introduces event-modeling simulation and clustering technique in order to achieve highly efficient evaluation. Moreover, the proposed method can detect the vulnerable cells on designing phase of LSI. Experimental results using 018um CMOS standard cell library prove the validity of the proposed method.
Recently, several illegal attacks against cryptographic circuits were reported. The template attack is one of the most dreadful attacks. To secure the safety of electronic devices in the future, into which cryptographic circuits have been incorporated, template attacks must be thoroughly studied. Therefore, this paper proposes a new template attack. It performs power analysis in frequency domain in order to improve the performance of analysis, while all previous template attacks perform in time domain. Moreover, the proposed template attack introduces an alignment technique and a selection technique of waveforms. The alignment technique, which corrects positions of waveforms, is suitable for template attacks. The selection technique utilizes equalization of the hamming-distance on each state to reduce noises of an analysis. Several experiments using cryptographic LSI prove the validity of the proposed method.
In this paper, structurally-novel manipulator called “hybrid manipulator” is proposed. The hybrid manipulator is defined as a serial connection of a binary manipulator and a (Gough-Stewart type) parallel manipulator. Both structures are known as high rigidity and stability for static loads. The binary manipulator has wide workspace, however, its workspace is discrete. On the other hand, the parallel manipulator has high accuracy for continuous positioning, however, its workspace is narrow. Therefore, the proposed structure has advantages of both the binary manipulator and the parallel manipulator. An inverse kinematics algorithm of hybrid manipulator is also proposed. The proposed algorithm consists of ellipsoidal approximation of the workspace and recursive process. The proposed algorithm can realize fast solution of the inverse kinematics problem. The proposed algorithm is verified through numerical experiments.
Demand for compact and simple sensor with high accuracy in the production line is increasing. A compact sensor using a self-coupling effect of the semiconductor laser has been studied. A measurement method which can detect self-coupling effect from change in terminal voltage of semiconductor laser without photodiode is proposed. It is confirmed that this sensor using terminal voltage change can measure a distance as same as sensor with photodiode. The measurable distance of this sensor is shorter than that with the photodiode. However, this sensor can be used as a shape measuring sensor with high accuracy by closely arranging semiconductor laser in a two-dimensional array.
This paper addresses the model reference control problem, which is a typical control problem in the data-driven controller tuning methods. For non-minimum phase systems, the unstable zeros of the plant should be included in the reference to avoid destabilization of the resulting closed-loop system and improve tracking performance. First, we propose the data-driven controller tuning method considering closed-loop stability with the tuned controller parameters in time-domain. If the plant has unstable zero(s), the proposed method would not lead to destabilizing controller at worst. Closed-loop stability is checked by linear inequalities described by input/output data, which contributes to less computation in the proposed method. Moreover, this paper proposes the data-driven controller tuning method for non-minimum phase plants estimating its unstable zero(s) by the flexible reference model at every parameter updates and reflecting them into the resulting reference model. The effectiveness of the proposed method is confirmed by numerical experiments.
This paper proposes a signature verification technique called combined segmentation-verification based on off-line features and on-line features. We use three different off-line feature vectors extracted from full name Japanese signature image and from the sub-images of the first name and the last name. The Mahalanobis distance for each off-line feature vector is calculated for signature verification. The on-line feature based technique employs dynamic programming (DP) matching technique for time series data of the fullname signature and first name and last name. The final decision (verification) is performed by SVM (Support Vector Machine) based on the three Mahalanobis distances and three dissimilarity of the DP matching. In the evaluation test the proposed technique achieved 3.35% EER (Equal Error Rate) with even FRR (False Acceptance Rate) and FAR (False Rejection Rate), which is 3.10% lower than the best EER obtained by the individual technique. This result shows that the proposed combined segmentation-verification approach improves Japanese signature verification accuracy significantly.
Recently, expectations for camera-based document analysis and recognition have increased by improved performance of digital camera devices. In this paper, we propose a rotation angle estimation method using Gray-Scale Gradient Feature and Modified Quadratic Discriminant Function (MQDF). This method can recognize characters and estimate the rotation angle of those characters rapidly. As the result of the evaluation experiment using printed alphanumeric character, we have confirmed that the low dimensional feature vector is sufficient to estimate the rotation angle of characters. Also, we reduced the number of used eigenvectors of the covariance matrix to calculate the MQDF while keeping estimation accuracy.
Accurate off-line character recognition is still very important for camera based printed document analysis. Due to its inherent conceptual and technical simplicity, conventional recognition strategies relied on features extracted using square block zoning of a character image. In this paper, we propose an isotropic feature extraction method using regular hexagonal zoning and empirically confirm its effectiveness for printed and handwritten character recognition. We accomplished printed character recognition and handwritten character recognition experiments using large-scale evaluating datasets. The average accuracy was improved by 2 % in experiments using gradient features. And the effectiveness of hexagonal zoning for recognition of high stroke count characters and low-resolution characters is confirmed in both printed and handwritten character recognition by the experiments.
This paper describes the rotational motion and load characteristics of a movable 5-DOF platform. The platform utilizes the principle of an inchworm. Six electromagnets and six piezoelectric actuators consists the platform. The rotational displacement of the platform however decreases while the load increases. This result implies that the attractive force of the electromagnets used in the platform is not sufficient.
We have developed an efficient genetic algorithm (GA) for crystal structure search without using the symmetry of crystals. Electronic states are computed from first-principles based on the density functional theory in conjunction with a planewave-pseudopotential method. In our technique, initial crystal structures for the crystal structure optimization are searched using the GA without using the crystal symmetry. These initial structures are optimized using the Broyden-Fletcher-Goldfarb-Shanno method to obtain candidates of the stable crystal structure. In this paper, we report on the results of simulation experiments performed for crystal structure prediction of the hexagonal Boron Nitride.
Solar power business is expanding rapidly in Japan because new feed-in tariff began in July 2012. Generally, monitoring for defect detection and total electric generating capacity is done at power conditioning system (PCS) in large scale photovoltaic (PV) power plant. Although PCS monitoring can detect its own defection or reduction in power generation amount, it is difficult to monitor by PV string/panel base. Additionally, it was also difficult to apply traditional monitoring method in PV string/panel to existing large PV power plant because special joint boxes with wired communication unit were used for its installation. New PV string monitoring method which is easy to install to both existing and new PV power plants is proposed in this paper. This proposed method realizes efficient data capturing by fixed unit-to-portable unit and multiple fixed-to-fixed units wireless communication utilizing DECT (Digital Enhanced Cordless Telecommunications). Finally, the effectiveness of this proposed method is shown in the demonstration experiments using actual F Onomichi power plant.
Amyotrophic Lateral Sclerosis (ALS) patients are unable to successfully communicate their desires, although their mentality is normal, and so, the necessity of Communication Aids (CA) is realized. Therefore, the author is focused on Event-Related Potential (ERP) which is elicited for the target by visual and auditory stimuli. P200, N200 and P300 are components of ERP. These are potentials that are elicited when the subject focuses attention on stimuli that appears infrequently. ALS patient participated in Target Stimulus-Specific Experiment. The category of stimuli are “Word”, “Picture”, “Illustration”, “Character”, “Voice” and “Character & Voice”. Each category of stimulus has 5 stimuli. By detecting ERP, a target stimulus was specified. The author described the number of correct judgment, feature of ERP to each stimulus, and discussed how to improve the number of correct judgment.
This paper deals with the robust feedback-control for the subsidy policy about the purchase of the plug-in electric vehicle (EV) and the business model of the charging stands. The controlled model has three characteristics: (1) A public institution builds the charge stands. (2) The break-even point is calculated by thinking about the maintenance cost and running cost. (3) The set value for the number that is spread of EV is given. However, the model has the uncertainties of the parameter, and a change by the influence of the economy and so on. Therefore, it is necessary to apply the robust control logic that overcomes the uncertainties of the model. We propose that the practice of the subsidy policy is regulated by feedback control. As the control logic, we use Sliding Mode Control. The simulation results show the advantage of a person planning EV purchase and the charge stand manager, and show that Sliding Mode Control is one of the control logic which is effective in the social model.
In this paper, the optimal energy management considering battery characteristics for smart grid and micro grid systems is discussed. The energy storage system is very important for the energy peak shift and peak cut for micro grid and smart grid system because of these backgrounds. However, optimal battery formulation and energy management to suppress by using expensive energy storage system and considering battery characteristics are still in a developmental stage. First, the optimal battery placement for smart grid system with Lithium-ion battery is dealt with for lifetime extending. The battery choice index for this optimal battery determination problem to formulate optimal energy management is discussed. Second, the smart grid system with distributed battery system for grid frequency control considering distributed control and battery characteristics is dealt with. Optimal controllers for each distributed battery systems are designed to reduce battery load and to expend battery lifetime are designed. The effectiveness of the proposed method is shown via simulations by comparing with conventional methods.
In this study, leakage hydrogen gas was measured using Coherent Anti-Stokes Raman Spectroscopy (CARS) using a fiber-coupled, compact spectrometer. The Anti-Stokes light intensity was proportional to the square of the hydrogen gas density, which confirmed that the Anti-Stokes light was generated by CARS. When hydrogen gas was released from a nozzle, the spatial range in which the Anti-Stokes light could be detected was 1mm in the lateral direction directly above the nozzle. The dependence of the detectability of Anti-Stokes light on the material of a reflector placed behind the nozzle was investigated, which showed that Anti-Stokes light could be detected when the reflector was metal, rusted metal, or acrylic. The Anti-Stokes light could also be detected when low concentration (≦4%) hydrogen gas was released from the nozzle. The signal-to-noise ratio exceeded 1 when the concentration was over 0.4%. These results indicated the possibility of a hydrogen leak detector using CARS, to detect minute hydrogen gas leakage faster than conventional leak detectors.
This paper describes a sub-goal generation method in consideration of disturbance for an auto berthing system. In the actual ship operation by human, the operator estimates a predicted trajectory based on an acquired motion model and generates the sub-goal considered disturbance. However, the generation method of the sub-goal for the auto berthing system has not been established. Therefore, this paper proposed the generation method based on the acquired motion model. The effectiveness of the proposed method is shown by simulation and experimental results.
In this paper, we propose an estimation method of musical impression value based on human's KANSEI as the elemental technology for the musical retrieval system. The main focus is to estimate the fuzzy impression value in order to consider the difference in musical impression among audience's individuals. We estimate the fuzzy impression value using linear programming with the fluctuation information which indicates time structure of musical piece. In experiments to estimate the fuzzy impression value, the results showed that this method is reflecting the difference by audience's individuals.
To realize low delay live video streaming system over lossy network, each captured frame must be divided into block images, and these images must be encoded, packetized, and sent selectively. To improve the quality of reproduced frames at the receiver side, the selecting method of the block images must be sophisticated. This paper proposed an optimal selecting method of block images. The method consists of three phases based on the formulated two optimization problems. Firstly, a frequency analysis method is applied to the recent luminance value for each block area. Secondly, sendable block ratio is optimally distributed among block areas based on the result of frequency analysis to satisfy the available bandwidth condition. Finally, the optimal timings of sending are planned for each block area to minimize the error of reproduced image under the condition of assigned sendable block ratio. Some experiments were conducted to evaluate the proposed method using three frequently used reference video sequences. The results of comparison with the conventional method shows that the proposed method improved the mean squared error (MSE) of reproduced image up to 64%. Another results shows that the robustness of the proposed method against packet loss can be changed by modifying the parameter of the optimization problem.
One of challenges in knowledge engineering is analysis and organization of knowledge finding with an appropriate empirical methodology. Although some methodologies are powerful, many of them are passive, in the sense that only a few suggestions are made by the environment. According an idea try to connect real world and abstract space of knowledge for an intellective-insight. In this paper, we proposed a novel approach to design a virtual space to support knowledge methodological environment, by developing knowledge schema as a part of analysis process covering knowledge management level and knowledge object level. We implemented three levels of architectural views: physical view, logical view, and functional view. We used a scene-graph to implement knowledge methodology in virtual space and explained their schematic with KML/XML-standardization. The benefits of the proposed method are not only to support convergence of the knowledge methodology with its environment, but also to encourage a groupware by participating in geographically-distributed development that contributes to knowledge exchange and sharing. We demonstrated our approach by building a prototype system using GoogleTM APIs environment. Our experimental results show the proposed method satisfies on supportability, usability, and utility in knowledge-developing process. Additionally, its convergent design improves knowledge methodological suggestion for wider user with various experiences.
Efficient classification plays a significant role in rule-based Intrusion Detection Systems. In order to make full use of the information in the rule pool, in this paper, a novel approach has been proposed to improve the detection performance by building a Gaussian function for each cluster in the two-dimensional average matching degree space, instead of analyzing the distance in the two-dimensional average matching degree space. A clustering method is also proposed which calculates the number of clusters and their centers depending on the crowdness of the points of each class. Considering the importance of the number of clusters, the performance of the intrusion detection is evaluated by changing the size of clusters. Simulation results show that the proposed approach based on the Gaussian function of each cluster is effective and efficient for distinguishing normal, misuse and anomaly intrusions.
We propose an SSD (Solid State Drive) allocation capacity optimization method between tiered volume and SSD cache and evaluate the storage I/O (Input/Output) response time with this method. When a combination of the volume tiering and SSD cache methods was applied, the SSD allocation capacity between them was optimized by only trial and error. The proposed method dynamically optimizes the SSD allocation capacity between them by simulating allocating larger and smaller SSD capacity to the SSD cache and calculating rates of I/Os to SSD. We show the proposed method optimizes it regardless of I/O characteristics, SSD capacity, and initial SSD allocation capacity between them. It reduces the storage I/O response time by up to 39% compared to the combination method.
In this paper, we target the specific smart grid concept called “Inter-Intelligent Renewable Energy Network (i-Rene)”, which is characteristically associated with PV panel, micro storage and regional electricity market. In this smart grid, every houses has an artificial intelligence agent which leans optimal treading strategy to minimise the payment of residents. From the economic standpoint, we take the cost-and-benefit-analysis (CBA) on the optimal installation of PV panels and micro storages in this smart grid. To take the CBA, we use actual measured values about power demand profiles and power production profiles which were obtained in 2010 at Shiga, Japan. In accordance with this analysis, we also discuss the necessary condition for achieving Grid parity of smart grid i-Rene.
For powder transport in micro total analysis systems, we proposed and fabricated a novel surface acoustic wave (SAW) actuator with a Bragg reflector. By using the SAW actuator, we carried out a powder transport experiment. As a result, it was found that the powder was transported along the upstream of the SAWs, and the powder transport direction was changed at a certain point by the reflection of the SAWs. The zigzag transport path indicates possibility of a more flexible design of a micro powder transport system.
Image coding technologies are essential to use communication channels effectively. We have been studied vector quantization, because it does not cause deterioration in image quality in a high compression region and also has a small computational cost for image coding. The performance of vector quantization depends on a code book that is constructed using clustering algorithms. In this study, we evaluated popularly used typical clustering algorithms in terms of practical usage. Computational experiments showed that there is no notable difference in performance of the clustering algorithms in practical usage of vector quantization.