Binary Search Method Research Articles

A Gradient-Oriented Binary Search Method for Photonic Device Design

Inverse design method is widely used in photonic device design. To accelerate the speed to find the possible solutions in design space, optimization method with high efficiency to exhaust the parameter space is pivotal. In this paper, we propose a gradient-oriented binary search method for photonic device design. It utilizes the gradient of refractive index change to the figure of merit to guide the flipping process between binary materials. This method can accelerate the optimization convergence speed compared with direct binary search method, while maintain the advantages of no additional binarization procedure and well critical dimension constraints. Based on this design methodology, we design a bimodal interferometer on silicon photonics platform, which can generate interference pattern of TE1 and TE2 mode using fundamental mode as input. The device layout is 4 μm × 8 μm. To verify the universality of the method, we design a TE1-TE0 mode converter and 3-dB optical power splitter. Results also verifies that the optimize speed of this method is much faster than direct binary search method. And the convergence iteration steps would not be significantly increased with the increase of unit cells. Our approach provides a promising supplement to the direct binary method, and can be implemented in a variety of device design.

A DVFS-Weakly Dependent Energy-Efficient Scheduling Approach for Deadline-Constrained Parallel Applications on Heterogeneous Systems

Heterogeneous computing systems are being increasingly deployed on time-critical applications, where tasks need to meet execution deadlines and the energy consumption is to be minimized. Dynamic voltage and frequency scaling (DVFS) has been widely applied for energy saving on computing devices. Unfortunately, DVFS may introduce transient errors and shorten the processor lifetime. There is also time and energy overhead when computing and making the switching. In this article, we investigate scheduling approaches—that are independent of, or weakly dependent on DVFS—for parallel real-time applications with hard deadlines running on heterogeneous computing systems. The aim is to minimise the energy consumption while keeping all deadlines satisfied. First, in the domain without DVFS, we propose a DVFS-nondependent scheduling algorithm (DNDS), which prioritises tasks of high energy consumption during reassignment with slack time. Second, we propose a DVFS-weakly dependent scheduling (DWDS) algorithm, which finds an appropriate frequency for each processor in an iterative manner. DVFS is only allowed when switching applications. Third, based on DWDS, we further propose an algorithm Fast_DWDS, which quickly converges by deploying a binary search method. Our proposed scheduling approaches are evaluated with a large number of directed acyclic graph-based applications of high, low, and random parallelism. The results show that they significantly reduce the energy cost compared to their existing counterparts, i.e., without and with DVFS, respectively, while all deadlines remain satisfied.

A Novel Hybrid Tag Identification Protocol for Large-Scale RFID Systems

Radio frequency identification technology is one of the main technologies of Internet of Things (IoT). Through the transmission and reflection of wireless radio frequency signals, non-contact identification is realized, and multiple objects identification can be realized. However, when multiple tags communicate with a singleton reader simultaneously, collision will occur between the signals, which hinders the successful transmissions. To effectively avoid the tag collision problem and improve the reading performance of RFID systems, two advanced tag identification algorithms namely Adaptive M-ary tree slotted Aloha (AMTS) based on the characteristics of Aloha-based and Query tree-based algorithms are proposed. In AMTS, the reader firstly uses the framed slotted Aloha protocol to map the tag set to different time slots, and then identify the collided tags using binary search method based on collision factor or mapping table. Both performance analysis and extensive experimental results indicate that our proposed algorithms significantly outperforms most existing anti-collision approaches in tag dense RFID systems.

A scheduling and control integration optimization method for regenerative braking energy utilization

The utilization of regenerative braking energy is of great significance to the energy saving of subway. Therefore, this paper proposes an optimization method for scheduling and control integration, which not only adjusts the timetable but also optimizes the speed curves of trains. When there is a train braking, this method will try to find a train that accelerates to absorb the regenerative energy generated by the braking train. Firstly, this paper establishes the timetable energy saving optimization model, based on which the speed curves will be optimized. Furthermore, we design a scheduling optimization algorithm based on genetic algorithm, and optimizes speed curves of trains by binary search method to obtain a good solution. Finally, simulations are given using the real data of Beijing Metro Line 4 to evaluate the proposed method, and the results show that the integrated scheduling and control optimization method can reduce energy consumption by 15.18%. In the random disturbance simulations, the proposed method shows good robustness, which makes it possible to apply this method to the real subway operations.

Formulation and Solution Methodology for Reducing Energy Consumption in Two-Machine Bernoulli Serial Lines

Machines consume intensive energy in some production systems. Although substantial efforts have been devoted to performance analysis, continuous improvement, and design of production systems, the research on reducing the energy consumption in these systems is limited. In this article, the problem of minimizing the energy consumed by machines in the two-machine Bernoulli serial line, which has been formulated as nonlinear programming with production rate constraint, is investigated. Specifically, structural characteristics and optimality conditions of the problem are analyzed, and two nonlinear algebraic optimality equations are established. To solve the optimality equations, their properties are explored, and an effective algorithm based on the binary search method is developed. Furthermore, the sensitivity of the optimal solution with respect to system parameters is quantitatively analyzed. Based on the sensitivity analysis, some useful insights on the optimal objective value are extracted. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In energy-intensive manufacturing enterprises, reducing the energy consumption in their production systems is urgent and challenging. In this article, an energy consumption optimization problem in a simple model of production systems (i.e., in the two-machine Bernoulli line) is investigated. Machine efficiencies are optimized so that the total energy consumption of machines is minimized, while a required production rate is ensured. The reported results enable a novel managerial paradigm for production systems, especially for energy-intensive systems. Although the model of the two-machine Bernoulli line is simple, as a cornerstone, this research will be extended, in the future, to long Bernoulli lines and more practical production systems, e.g., lines with geometric, exponential, and non-exponential machine reliability models.

Adversarial attacks on deep-learning-based radar range profile target recognition

Target recognition based on a high-resolution range profile (HRRP) has always been a research hotspot in the radar signal interpretation field. Deep learning has been an important method for HRRP target recognition. However, recent research has shown that optical image target recognition methods based on deep learning are vulnerable to adversarial samples. Whether HRRP target recognition methods based on deep learning can be attacked remains an open question. In this paper, four methods of generating adversarial perturbations are proposed. Algorithm 1 generates the nontargeted fine-grained perturbation based on the binary search method. Algorithm 2 generates the targeted fine-grained perturbation based on the multiple-iteration method. Algorithm 3 generates the nontargeted universal adversarial perturbation (UAP) based on aggregating some fine-grained perturbations. Algorithm 4 generates the targeted universal perturbation based on scaling one fine-grained perturbation. These perturbations are used to generate adversarial samples to attack HRRP target recognition methods based on deep learning under white-box and black-box attacks. The experiments are conducted with actual radar data and show that the HRRP adversarial samples have certain aggressiveness. Therefore, HRRP target recognition methods based on deep learning have potential security risks.

Performance Analysis for Heterogeneous Cloud Servers Using Queueing Theory

In this article, we consider the problem of selecting appropriate heterogeneous servers in cloud centers for stochastically arriving requests in order to obtain an optimal tradeoff between the expected response time and power consumption. Heterogeneous servers with uncertain setup times are far more common than homogenous ones. The heterogeneity of servers and stochastic requests pose great challenges in relation to the tradeoff between the two conflicting objectives. Using the Markov decision process, the expected response time of requests is analyzed in terms of a given number of available candidate servers. For a given system availability, a binary search method is presented to determine the number of servers selected from the candidates. An iterative improvement method is proposed to determine the best servers to select for the considered objectives. After evaluating the performance of the system parameters on the performance of algorithms using the analysis of variance, the proposed algorithm and three of its variants are compared over a large number of random and real instances. The results indicate that proposed algorithm is much more effective than the other four algorithms within acceptable CPU times.

A Flexible and Generic Gaussian Sampler With Power Side-Channel Countermeasures for Quantum-Secure Internet of Things

Post-quantum cryptography (PQC) great potential in providing reliable communication security for Internet-of-Things (IoT) devices against the quantum computer in the future. The Gaussian sampler is a crucial part in lattice-based post-quantum cryptosystems, thus being the most vulnerable module to side-channel attack as well. However, research on the countermeasures for the Gaussian sampler against power side-channel attacks is almost blank. In this article, a flexible and generic cumulative distribution table (CDT)-based Gaussian sampler using the hardware-software approach is proposed. The proposed CDT sampler has an AHB interface and can be reconfigured to support various parameter sets, while utilizing just 77 Slices on a Xilinx Spartan-6 FPGA with constant response time. Additionally, the first simple power analysis (SPA) attack on the CDT sampler is presented. The presented attack mainly takes advantage of the chosen input and the SPA vulnerability associated with the binary search method, hence the attacker is able to recover every sampled value by comparing a few pairs of power consumption traces. To further protect against chosen input SPA attack, this article identifies the vulnerability associated with three main operations in every binary search state and construct an effective countermeasure based on randomization at the cost of only extra 58.4% Slices. Compared to other related works, the merits of the proposed CDT sampler are the high hardware flexibility, side-channel security, and suitability for resource-constrained IoT nodes.

OPTIMIZATION OF TURNING PARAMETERS USING AN ALGORTIM BASED ON COMBINED LINEAR AND BINARY SEARCH METHODS

Optimization of cutting parameters in machining operations is a complex task requiring extensive knowledge and experience to reach the maximum potential for cost reductions in manufacturing. Through the work presented in this paper a cutting parameters optimization algorithm for roughing and finishing turning has been realized. The proposed optimization algorithm is based on a combination of linear search and binary search methods, heaving as objective criterion the minimization of the machining time. Examples for roughing and finishing turning have been presented to illustrate the application of the proposed algorithm and analyse the results.

OPTIMIZATION OF TURNING PARAMETERS USING AN ALGORTIM BASED ON COMBINED LINEAR AND BINARY SEARCH METHODS

Optimization of cutting parameters in machining operations is a complex task requiring extensive knowledge and experience to reach the maximum potential for cost reductions in manufacturing. Through the work presented in this paper a cutting parameters optimization algorithm for roughing and finishing turning has been realized. The proposed optimization algorithm is based on a combination of linear search and binary search methods, heaving as objective criterion the minimization of the machining time. Examples for roughing and finishing turning have been presented to illustrate the application of the proposed algorithm and analyse the results.

Deriving of Time Constants in Timed Automata for Hazard Transition Sequences for STAMP/STPA

Abstract In recent years, information systems have become large-scale and complicated. The demand for research on the cause analysis of information systems that can fail and the construction of countermeasures has been increasing. Systems Theoretic Accident Model and Processes (STAMP) is an accident model based on systems theory. STAMP has the feature that it can analyze not only failures of system components and human errors but also hazards caused by unsafe interaction between components and between components and humans. An analysis method based on the STAMP model System-Theoretic Process Analysis (STPA) is a method for analyzing in advance the possibility of a system accident for the interaction between a controller and a controlled device. As a preliminary study, we have performed STAMP/STPA to ‘Fallen Barrier Trap at Railroad Crossing,” which is an example of STAMP/STPA analysis. We have analyzed with the time automaton model checker UPPAAL in cooperation with STAMP Workbench, a STAMP/STPA support tool. From the preliminary study, we consider that the analysis procedure can be automated in order to reduce the burden on analysts. In this paper, we propose a method for deriving hazard transition sequences as a STAMP/STPA support method. Specifically, the model definition is described in STAMP Workbench, the method of deriving files that can be used in UPPAAL from the definition statement is described, and in order to automate model checking, the SAT/SMT solver is used. The constraints for the clock variables in each component are automatically derived. This paper describes a method for deriving the shortest path as a counterexample that does not satisfy the safety constraint equation by using the calculation method of the concrete value the constraints of and the binary search method. The proposed method is applied manually as the evaluation experiment. We confirmed that an effective sequence corresponding to some hazard scenarios derived by the conventional procedure could be derived.

EFFECTIVENESS OF THE BINARY SEARCH METHOD IN DATABASE FILES IN THE CASE OF A GENERALIZED DISTRIBUTION OF PROBABILITIES OF ACCESS TO RECORDS.

The article presents a formula for calculating the mathematical expectation of the number of comparisons needed to nd a record in a le in the case of generalized distribution of the probabilities of accessing records. We compare the eectiveness of the binary search method in the case of generalized distribution of probabilities of access to records and Zipf's law.

EFFECTIVENESS OF THE BINARY SEARCH METHOD IN DATABASE FILES IN THE CASE OF A GENERALIZED DISTRIBUTION OF PROBABILITIES OF ACCESS TO RECORDS.

The article presents a formula for calculating the mathematical expectation of the number of comparisons needed to nd a record in a le in the case of generalized distribution of the probabilities of accessing records. We compare the eectiveness of the binary search method in the case of generalized distribution of probabilities of access to records and Zipf's law.

Determining resource requirements for elections using indifference-zone generalized binary search

Election lines are more than a nuisance. In recent elections, needing to wait in lines deterred hundreds of thousands from voting and likely changed the winner in multiple cases. Part of the challenge is that even after the voter reaches the front of the line in some locations, it can require more than ten or twenty minutes to cast a ballot. Moreover, the ballot in one precinct might be twice the length of another precinct's in the same county because of additional referenda and levies. We consider the decision problem faced by election officials and other leaders: How many resources (poll books, workers, booths, or voting machines) should be allocated to each location so that lines can reasonably be expected to be minimal? We formulate a simulation optimization problem to identify the combinations that minimize resource requirements while guaranteeing acceptable user-defined service levels. We propose an Indifference Zone Generalized Binary Search (IZGBS) method with rigorous assurances on solution quality, and demonstrate it using the 2016 presidential election. In that election, we describe how the methods helped to reduce waiting times by more than three hours in at least one location affecting thousands of voters and likely increasing turnout.

Digital halftoning method with simultaneously optimized perceptual image quality and drive current for multi-tonal electrophoretic displays.

While using digital halftoning to achieve multi-tones in a 1 bit electrophoretic display (EPD), e.g., a three-pigment chromatic EPD, the drive current is significantly increased because of frequently reversed pixel values. Aimed at this issue, this study first establishes a model that can accurately predict the drive current from image content. Next, based on the direct binary search method, a new halftoning method is proposed by constructing a combined merit function that incorporates both the perceptual image quality and the drive current. As a result, in experiments using a 13.5 in. three-pigment EPD and several test images, compared with the well-developed error-diffusion method, the proposed method produces little image quality degradation, whereas the drive current increase with respect to the minimum current of the EPD is reduced from 71.8 to 33.0 mA, for a significant reduction of 54.0%.

Fault detection and isolation of control moment gyros for satellite attitude control subsystem

Abstract Control moment gyros, as one of the most commonly used actuators onboard satellites, are prone to faults and failures. The ability to detect faults, isolate their location, and identify their severity can enhance mission success rate and reduce maintenance and damage costs extensively. Therefore, in this paper, a model-based fault detection, isolation, and identification scheme is proposed and evaluated. Firstly, an adaptive threshold fault detection algorithm is proposed, using Unscented Kalman filters in conjunction with residual and innovation sequences. Secondly, a fault isolation and identification algorithm is proposed using a binary grid search method to adapt joint estimation Kalman filter’s covariance matrix once a fault is detected. Extensive Monte Carlo simulations are conducted to evaluate the performance of the proposed schemes for a 3-axis stabilized satellite with a four-single-gimbal control moment gyro cluster. Results show superior performance of the proposed methods with faster tracking and more accuracy.

A Binary Search Method for the General Coupled Task Scheduling Problem

The coupled task scheduling problem aims to schedule a set of jobs, each with at least two tasks and there is an exact delay period between two consecutive tasks, on a set of machines to optimize a performance criterion. We study the problem of scheduling a set of coupled jobs to be processed on a single machine with the objective of minimizing the makespan, which is known to be strongly NP-hard. We obtain competitive lower bounds for the problem through different procedures, including solving 0-1 knapsack problems. We obtain an upper bound by applying a heuristic algorithm. We then propose a binary search heuristic algorithm for the coupled task scheduling problem. We perform extensive computational experiments and show that the proposed method is able to obtain quality solutions. The results also indicate that the proposed solution method outperforms the standard exact solver Gurobi.

Rapid Prototyping of Photovoltaic Emulator Using Buck Converter Based on Fast Convergence Resistance Feedback Method

Using a photovoltaic (PV) emulator (PVE) simplifies the testing of the PV generation system. However, conventional controllers used for PVEs suffer from oscillating output voltage, requiring a high number of iterations, or being too complex to be implemented. This paper proposes a controller based on a resistance feedback control strategy that produces a stable and fast converging operating point for the PVE. The resistance feedback control strategy requires a new type of PV model, which is the current–resistance ( I – R ) PV model. This model is computed using a binary search method at a fast convergence rate. It is combined with a closed-loop buck converter using a proportional-integral controller to form the resistance feedback control strategy. The PVE's controller is implemented into dSPACE ds1104 hardware platform for experimental validation. The acquired experimental results show that the proposed PVE is able to follow the current–voltage characteristic of the PV module accurately. In addition, the PVE's efficiency is more than 90% under maximum power point operation. The transient response of the proposed PVE is similar to the PV panel during irradiance changes.

Networked [formula omitted] filtering under a weighted TOD protocol

This paper is concerned with the problem of networked H∞ filtering under a weighted try-once-discard (TOD) protocol. Firstly, a weighted TOD protocol is investigated to orchestrate the sampled-data transmission from sensors to filter via a bandwidth-limited network. Taking into account the network-induced delay, the studied system is transformed into an impulsive system with an interval time-varying delay. Secondly, by using the Lyapunov–Krasovskii method, sufficient conditions for ensuring the exponential stability with a prescribed H∞ performance and the existence of suitable H∞ filter are obtained in the form of linear matrix inequalities (LMIs). Then, for minimizing H∞ performance, an algorithm is presented by using the binary search method. Finally, an example of quarter-car model with an active suspension is used to verify the validity of proposed method.

Analysis and Application of Three-Phase SEIG With Power Converters for Supplying Single-Phase Grid From Wind Energy

A power converter configuration has been proposed instead of using phase balancing components for operating three-phase wind-driven induction generators (IGs) with single-phase grid. A closed loop control technique has been developed and implemented using field programmable gate array for peak power point operation of the proposed system. Balanced loading of IG, peak power point tracking of wind turbine (WT), unity power factor at the grid, and viability to use the same system with an additional leg in the inverter when the three-phase system is installed are the major advantages of the present configuration. By this way, the system turns out to be an economical alternative to the conventional configurations. A systematic procedure has been devised for the end-to-end analysis of the proposed system for any specified wind velocity. This procedure includes the mathematical modeling of WT, performance evaluation of IG using binary search method, and power converter analysis using power balance technique. The successful working of the proposed system has been ascertained both in steady-state and dynamic conditions. The performance parameters obtained using the devised method are substantiated with the experimental results obtained on a prototype system of 3.7 kW capacity developed in the laboratory.