High power dissipation can occur when the response to a test vector is captured by flip-flops in scan testing, resulting in excessive IR drop, which may cause significant capture-induced yield loss in the DSM era. This paper addresses this serious problem with a novel test generation method, featuring a unique algorithm that deterministically generates test cubes not only for fault detection but also for capture power reduction. Compared with previous methods that passively conduct X-filling for unspecified bits in test cubes generated only for fault detection, the new method achieves more capture power reduction with less test set inflation. Experimental results show its effectiveness.

Floating-point units (FPUs) are indispensable in processors, 3D-graphic engines, etc. To improve design productivity of these LSIs, FPU IPs are strongly desired. However, it is impossible to cover wide range of needs by an FPU IP, because there are various kind of options in specifications (e.g., operating frequency, latency, and ability of pipeline operation) and implementations (e.g., hardware algorithms). Thus, multiple IPs are needed even for the same functionality. In this paper, we propose to build an IP Library which consists of large number of FPU IPs with various kind of specifications and implementations, and which has catalogue data that shows not only specifications but also post-layout area and power dissipation of each IP. As the first step of the project, we have developed an IP Library targeted to Rohm 0.35 µm triple-metal process, which consists of 20 IPs for IEEE-754-standard single-precision floating-point division with 5 operating frequencies (50 MHz, 75 MHz, 100 MHz, 125 MHz, and 150 MHz), with two options whether pipelined or not, and with two hardware algorithms (the restoring method and the SRT method). We have also developed a catalogue for the IP Library, which shows post-layout area and power dissipation as well as specification of each IP. We have introduced two metrics "performance-area ratio (MFLOPS/mm2)" and "performance-power ratio (MFLOPS/W)" to afford a good insight into efficiency of implementations. From the catalogue data, the restoring method is, on the average, 1.4 times and 2.3 times better than the SRT method in terms of performance-area ratio and performance-power ratio, respectively. The developed catalogue is usable not only for selection of the optimal IP for a specific application, but also for quantitative analysis at the early stage of architecture design. It is also expected that the catalogue data based on an actual process technology is valuable for education.

This paper presents a new strategy to detect and diagnose fault of a manipulator based on the expression with a Probabilistic Production Rule (PPR). Production Rule (PR) is widely used in the field of computer science as a tool of formal verification. In this work, first of all, PR is used to represent the mapping between highly quantized input and output signals of the dynamical system. By using PR expression, the fault detection and diagnosis algorithm can be implemented with less computational effort. In addition, we introduce a new system description with Probabilistic PR (PPR) wherein the occurrence probability of PRs is assigned to them to improve the robustness with small computational burden. The probability is derived from the statistic characteristics of the observed input and output signals. Then, the fault detection and diagnosis algorithm is developed based on calculating the log-likelihood of the measured data for the designed PPR. Finally, some experiments on a controlled manipulator are demonstrated to confirm the usefulness of the proposed method.

This research develops a method for trajectory planning of robotic systems with differential constraints based on hierarchical partitioning of a continuous state space. Unlike conventional roadmaps which is constructed in the configuration space, the proposed state roadmap also includes additional state information, such as velocity and orientation. A bounded domain of the additional state is partitioned into sub-intervals with multiple resolution levels. Each node of a state roadmap consists of a fixed position and an interval of additional state values. A valid transition is defined between a pair of nodes if any combination of additional states, within their respective intervals, produces a trajectory that satisfies a set of safety constraints. In this manner, a trajectory connecting arbitrary start and goal states subject to safety constraints can be obtained by applying a graph search technique on the state roadmap. The hierarchical nature of the state roadmap reduces the computational cost of roadmap construction, the required storage size of computed roadmaps, as well as the computational cost of path planning. The state roadmap method is evaluated in the trajectory planning examples of an omni-directional mobile robot and a car-like robot with collision avoidance and various types of constraints.

Omni-directional images have been used in wide range of applications including virtual/augmented realities, self-driving cars, robotics simulators, and surveillance systems. For these applications, it would be useful to estimate saliency maps representing probability distributions of gazing points with a head-mounted display, to detect important regions in the omni-directional images. This paper proposes a novel saliency-map estimation model for the omni-directional images by extracting overlapping 2-dimensional (2D) plane images from omni-directional images at various directions and angles of view. While 2D saliency maps tend to have high probability at the center of images (center bias), the high-probability region appears at horizontal directions in omni-directional saliency maps when a head-mounted display is used (equator bias). Therefore, the 2D saliency model with a center-bias layer was fine-tuned with an omni-directional dataset by replacing the center-bias layer to an equator-bias layer conditioned on the elevation angle for the extraction of the 2D plane image. The limited availability of omni-directional images in saliency datasets can be compensated by using the well-established 2D saliency model pretrained by a large number of training images with the ground truth of 2D saliency maps. In addition, this paper proposes a multi-scale estimation method by extracting 2D images in multiple angles of view to detect objects of various sizes with variable receptive fields. The saliency maps estimated from the multiple angles of view were integrated by using pixel-wise attention weights calculated in an integration layer for weighting the optimal scale to each object. The proposed method was evaluated using a publicly available dataset with evaluation metrics for omni-directional saliency maps. It was confirmed that the accuracy of the saliency maps was improved by the proposed method.

This paper develops the generating function method for the discrete-time nonlinear optimal control problem. This method can analytically give the optimal input as state feedforward control in terms of the generating functions. Since the generating functions are nonlinear, we also develop numerical implementations to find their Taylor series expressions. This finally gives optimal solutions expressed only in terms of the pre-computed generating function coefficients and state boundary conditions, such that it is useful for the on-demand optimal solution generation for different boundary conditions. Examples demonstrate the effectiveness of the developed method.

This paper presents a new framework for traffic flow control based on an integrated model description by means of Hybrid Dynamical System (HDS). The geometrical information on the traffic network is characterized by Hybrid Petri Net (HPN). Then, the algebraic behavior of traffic flow is transformed into Mixed Logical Dynamical Systems (MLDS) form in order to introduce an optimization technique. These expressions involve both continuous evolution of traffic flow and event driven behavior of traffic signal. HPN allows us to easily formulate the problem for complicated and large-scale traffic network due to its graphical understanding. MLDS enables us to optimize the control policy for traffic signal by means of its algebraic manipulability and use of model predictive control framework. Since the behavior represented by HPN can be directly transformed into corresponding MLDS form, the seamless incorporation of two different modeling schemes provide a systematic design scenario for traffic flow control.

This paper presents the analysis of the stopping maneuver of the human driver by using a new three-dimensional driving simulator that uses CAVE, which provides stereoscopic immersive vision. First of all, the difference in the driving behavior between 3D and 2D virtual environments is investigated. Secondly, a GMDH is applied to the measured data in order to build a mathematical model of driving behavior. From the obtained model, it is found that the acceleration information has less importance in stopping maneuver under the 2D and 3D environments.

This paper presents a novel identification method for hybrid dynamical system models, where parameters have stochastic and time-varying characteristics. The proposed parameter identification scheme is based on a modified implementation of particle filtering, together with a time-smoothing technique. Parameters of the identified model are considered as time-varying random variables. Parameters are identified independently at each time step, using the Bayesian inference implemented as an iterative particle filtering method. Parameters time dynamics are smoothed using a distribution based moving average technique. Modes of the hybrid system model are handled independently, allowing any type of nonlinear piecewise model to be identified. The proposed identification scheme has low computation burden, and it can be implemented for online use. Effectiveness of the scheme is verified by numerical experiments, and an application of the method is proposed: analysis of driving behavior through identified time-varying parameters.

In scheduling problem for automatic assembly, planning of task sequence is closely related with resource allocation. However, they have been separately carried out with little interaction in previous work. In assembly planning problem, there are many feasible sequences for one mechanical product. In order to find the best assembly sequence, we have to decide the cost function for each task a priori and make decision based on summation of costs in sequence. But the cost of each task depends on the machine which executes the allocated task and it becomes difficult to estimate an exact cost of each task at planning stage. Moreover, no concurrent operation is taken into account at planning stage. Therefore, we must consider the sequence planning and the machine allocation simultaneously. In this paper, we propose a new scheduling method in which sequence planning and machine allocation are considered simultaneously. First of all, we propose a modeling method for an assembly sequence including a manufacturing environment. Secondly, we show a guideline in order to determine the estimate function in A* algorithm for assembly scheduling. Thirdly, a new search method based on combination of A* algorithm and supervisor is proposed. Fourthly, we propose a new technique which can take into consider the repetitive process in manufacturing system so as to improve the calculation time. Finally, numerical experiments of proposed scheduling algorithm are shown and effectiveness of proposed algorithm is verified.

This paper develops the double generating function method for the discrete-time linear quadratic optimal control problem. This method can give generators for optimal solutions only in terms of pre-computed coefficients and boundary conditions, which is useful for the on-line repetitive computation for different boundary conditions. Moreover, since each generator contains inverse terms, the invertibility analysis is also performed to conclude that the terms in the generators constructed by double generating functions with opposite time directions are invertible under some mild conditions, while the terms with the same time directions will become singular when the time goes infinity which may cause instability in numerical computations. Examples demonstrate the effectiveness of the developed method.

This paper presents a new hierarchical scheduling method for a large-scale manufacturing system based on the hybrid Petri-net model, which consists of CPN (Continuous Petri Net) and TPN (Timed Petri Net). The study focuses on an automobile production system, a typical large-scale manufacturing system. At a high level, CPN is used to represent continuous flow in the production process of an entire system, and LP (Linear Programming) is applied to find the optimal flow. At a low level, TPN is used to represent the manufacturing environment of each sub-production line in a decentralized manner, and the MCT algorithm is applied to find feasible semi-optimal process sequences for each sub-production line. Our proposed scheduling method can schedule macroscopically the flow of an entire system while considering microscopically any physical constraints that arise on an actual shop floor.

The PLC (Programmable Logic Controller) has been widely used in the industrial world as a controller for manufacturing systems, as a process controller and so on. The conventional PLC has been designed and verified as a pure Discrete Event System (DES) by using an abstract model of a controlled plant. In verifying the PLC, however, it is also important to take into account the physical behavior (e.g. dynamics, shape of objects) of the controlled plant in order to guarantee such important factors as safety. This paper presents a new verification technique for the PLC-based control system, which takes into account these physical behaviors, based on a Hybrid Dynamical System (HDS) framework. The other key idea described in the paper is the introduction of the concept of signed distance which not only measures the distance between two objects but also checks whether two objects interfere with each other. The developed idea is applied to illustrative material handling problems, and its usefulness is demonstrated.

We have developed a high-efficiency charge-pump power supply circuit with large output current capability for mobile equipment. However, during the commercialization phase, we found that the large inrush current of 270 mA at charge-pump circuit startup-time could cause problems. In this paper we analyze the mechanism that causes this inrush current, and we propose circuitry to reduce it. We show SPICE simulation and measurement results for our proposed circuitry that confirm its effectiveness. By incorporating this circuitry, startup-time inrush current was reduced to 30 mA.

This paper presents a 0.5Vdd-step pumping method for Dickson-type charge-pump circuits that achieve high overall efficiency, including regulator circuitry, even at large output currents, and these circuits are targeted at mobile equipment applications. We have designed positive and negative charge-pump circuits which use a 0.5Vdd-step pumping method, are implemented with advanced control functions, and are fabricated with our custom CMOS process. Measured results showed that efficiency of a 2.5-stage positive charge-pump circuit before regulation is more than 93% (power supply Vdd=5 V, output voltage Vout=16.9 V 3.5Vdd, output current Iout=4 mA), and that of a 1.5-stage negative charge-pump circuit is 93% (power supply Vdd=5 V, output voltage Vout=-7.2 V -1.5Vdd, output current Iout=4 mA).

This paper describes a simple, inexpensive technique for intentionally broadening and flattening the spectrum of a DC-DC converter (switching regulator) to reduce Electro-Magnetic Interference (EMI). This noise spectrum broadening technique involves intentionally introducing pseudo-random dithering of control clock timing, which can be achieved by adding simple digital circuitry. This technique can significantly reduce noise power spectrum peaks at the DC-DC converter output. For our test case circuit, measurements showed that noise power was reduced by 5.7 dBm at the main peak, by 15.6 dBm at the second peak and by 12.8 dBm at the third peak. This simple, inexpensive technique can be applied to most conventional switching regulators by adding simple digital circuitry, and without any modification of the design of other parts.

This paper presents a new hierarchical mode segmentation of the observed driving behavioral data based on the multi-level abstraction of the underlying dynamics. By synthesizing the ideas of a feature vector definition revealing the dynamical characteristics and an unsupervised clustering technique, the hierarchical mode segmentation is achieved. The identified mode can be regarded as a kind of symbol in the abstract model of the behavior. Second, the grammatical inference technique is introduced to develop the context-dependent grammar of the behavior, i.e., the symbolic dynamics of the human behavior. In addition, the behavior prediction based on the obtained symbolic model is performed. The proposed framework enables us to make a bridge between the signal space and the symbolic space in the understanding of the human behavior.

Programmable Logic Controller (PLC) has been widely used in the industrial control. Inherently, the PLC-based system is a class of Hybrid Dynamical System (HDS) in which continuous state of the plant is controlled by the discrete logic-based controller. This paper firstly presents the formal algebraic model of the PLC-based control systems which enable the designer to formulate the various kinds of optimization problem. Secondly, the optimization problem of the 'sensor parameters,' such as the location of the limit switch in the material handling system, the threshold temperature of the thermostat in the temperature control system, is addressed. Finally, we formulate this problem as Mixed Logical Dynamical Systems (MLDS) form which enables us to optimize the sensor parameters by applying the Mixed Integer Programming.

Path planning and motion control are fundamental components to realize safe and reliable autonomous driving. The discrimination of the role of these two components, however, is somewhat obscure because of strong mathematical interaction between these two components. This often results in a redundant computation in the implementation. One of attracting idea to overcome this redundancy is a simultaneous path planning and motion control (SPPMC) based on a model predictive control framework. SPPMC finds the optimal control input considering not only the vehicle dynamics but also the various constraints which reflect the physical limitations, safety constraints and so on to achieve the goal of a given behavior. In driving in the real traffic environment, decision making has also strong interaction with planning and control. This is much more emphasized in the case that several tasks are switched in some context to realize higher-level tasks. This paper presents a basic idea to integrate decision making, path planning and motion control which is able to be executed in realtime. In particular, lane-changing behavior together with the decision of its initiation is selected as the target task. The proposed idea is based on the nonlinear model predictive control and appropriate switching of the cost function and constraints in it. As the result, the decision of the initiation, planning, and control of the lane-changing behavior are achieved by solving a single optimization problem under several constraints such as safety. The validity of the proposed method is tested by using a vehicle simulator.