Simple Logic Research Articles

Analysis of Satellite Images Using Deep Learning Techniques and Remotely Piloted Aircraft for a Detailed Description of Tertiary Roads

This document presents the results of a proof of concept for describing with more detail the social and complementary infrastructure around the tertiary roads of the Taminango region in the department of Nariño, Colombia. A dataset with samples of free satellite images from Google Maps and OpenStreetMaps was obtained. Then, a supervised deep learning algorithm with FCN (Fully Convolutional Network) topology is applied for the points of interest labeling process and the identification of the state of the roads using Keras and TensorFlow. Subsequently, a system consisting of a desktop application and a mobile application that integrates the functionalities of the trained algorithm through an intuitive interface and simple logic that stimulates interaction with the consultant is proposed. The desktop application includes a GUI designed in Python for tagging points of interest. The mobile application was developed with Flutter and comprises a database with documentation of the routes and road network in the region. It includes an augmented reality system in Vuforia Engine and Unity with virtual content developed in Blender and SolidWorks; A 3D model of the map of the region has been recreated for easier interaction and visualization of the points of interest and the status of the studied roads. In addition, complementary information was collected through remotely piloted aircraft for data acquisition in environments difficult to access, and through the community participation for the description and identification of areas not visible on official maps or statistics. This study addresses a method for the classification and identification of state of tertiary road network of the studied region, as well as labeling points of interest for the efficient management of resources for the development of new infrastructure there.

Development of a Hybrid Electric Vehicle Simulation Tool with a Rule-Based Topology

The performance of hybrid electric vehicles (HEVs) greatly depends on the various sub-system components and their architecture, and designers need comprehensive reviews of HEVs before vehicle investigation and manufacturing. Simulations facilitate development of virtual prototypes that make it possible to rapidly see the effects of design modifications, avoiding the need to manufacture multiple expensive physical prototypes. To achieve the required levels of emissions and hardware costs, designers must use control strategies and tools such as computational modeling and optimization. However, most hybrid simulation tools do not share their principles and control logic algorithms in the open literature. With this motivation, the author developed a hybrid simulation tool with a rule-based topology. The major advantage of this tool is enhanced flexibility to choose different control and energy management strategies, enabling the user to explore a wide range of hybrid topologies. The tool provides the user with the ability to modify any sub-system according to one’s own requirements. In addition, the author introduces a simple logic control for a rule-base strategy as an example to show the flexibility of the tool in allowing the adaptation of any logic algorithm by the user. The results match the experimental data quite well. Details regarding modeling principle and control logic are provided for the user’s benefit.

Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier

Analog electronic circuits play an essential role in many industrial applications and control systems. The traditional way of diagnosing failures in such circuits can be an inaccurate and time-consuming process; therefore, it can affect the industrial outcome negatively. In this paper, an intelligent fault diagnosis and identification approach for analog electronic circuits is proposed and investigated. The proposed method relies on a simple statistical analysis approach of the frequency response of the analog circuit and a simple rule-based fuzzy logic classification model to detect and identify the faulty component in the circuit. The proposed approach is tested and evaluated using a commonly used low-pass filter circuit. The test result of the presented approach shows that it can identify the fault and detect the faulty component in the circuit with an average of 98% F-score accuracy. The proposed approach shows comparable performance to more intricate related works.

An early intervention for adolescent depression targeting emotional mental images and memory specificity: a process evaluation

We have evaluated a novel early intervention for adolescent depression (age 16–18) in a feasibility randomised controlled trial. This nested process evaluation aimed to understand how this complex intervention worked. We sought to understand participants’ views and experiences of receiving and interacting with the intervention to evaluate whether the underpinning theoretical basis of the intervention is justified and whether it contributes to valued outcomes for participants. Twelve participants were invited to take part in semi-structured interviews. Framework analysis was employed to identify important aspects of adolescents’ experiences. The active ingredients identified by participants were consistent with and extended our understanding of the theoretical basis of the intervention. Four principle themes were identified: understanding how memory works and being able to remember memories in more detail; processing negative experiences and letting go; imagining positive future events; and understanding and being kinder to myself. The outcomes of the intervention were valued by participants. Six principle themes were identified: improving mood and well-being; reducing impact of negative memories; motivation and goal-directed behaviour; overcoming avoidance and rumination; relationships, communication and being open; and self-understanding and acceptance. A simplified logic model is also proposed to connect the intervention components, active ingredients, and valued outcomes. The findings provide an in-depth understanding of how participants interacted with the intervention and what they derived from it. For example, the findings establish processing negative experiences as a core intervention component, extend it to include letting go of these memories, and highlight that reducing the impact of negative memories is valued by participants. This richer understanding guides further intervention development and future implementation.

Demonstration of the reliability of a 5-kW-level oscillating-amplifying integrated fiber laser.

A novel fiber laser called an oscillating-amplifying integrated fiber laser was studied experimentally, in which the oscillating section and amplifying section share the pump between them. Based on this configuration, a 5-kW fiber laser system with optical-optical efficiency of 80.9% and M2 factor of 1.5 was achieved. The startup and shutdown sequence of the laser was studied in detail. When pumps of the laser were deliberately turned on in an inverted order, such as switching on/off the amplifying section before/after the oscillating section, which is normally disastrous in a classic fiber amplifier, the laser system turned out to operate stably at full power level. Thus, it is verified that there is no priority between the amplifier and the seed in this laser system. It combines the advantages of conventional fiber oscillators and fiber amplifiers, including high efficiency, high reliability, good anti-backreflection, and simple control logic.

A Magnetic Reconfigurable Ternary NOR/NAND Logic for Logic-in-Memory Applications

Logic-in memory has emerged as a promising solution to reduce the significant time gap between processor and memory. Simple logics such as NOR and NAND can be embedded in the memory for the purpose of data processing. Besides, ternary logic has been suggested to reduce the interconnects and the complexity of operations. In this paper, a reconfigurable ternary NOR/NAND logic compatible with ternary memories has been proposed. This scheme exploits magnetic tunnel junction as a nonvolatile memory element and a variable resistance, and carbon nanotube field-effect transistor for designing the peripheral circuits to achieve performance and efficiency. The proposed circuit is simulated using HSPICE and the results have validated the correct operation and high performance of the proposed design. Furthermore, the proposed designs are exploited in image processing applications to evaluate their performance in real applications, which gain averagely 52% improvement in the case of data loss.

Adaptive robust tracking control for underwater gliders with uncertainty and time-varying input delay

Underwater gliders (UGs) profile vertically by changing buoyancy and generally do not employ sophisticated controllers for depth control, but instead the simple logic rules-based controller. This paper first raises the problem of UGs operating beyond the prescribed depth due to employing the conventional logic rules-based depth control (LRDC). In order to achieve effective depth and attitude tracking control, a dual closed-loop control structure based on integral sliding mode control (ISMC) and adaptive robust time-delay control (ARTDC) is proposed to deal with model uncertainties, environmental disturbances and time-varying uncertain input delays without any explicit knowledge of delay variation. A simplified reduced-order motion equation of UG in the vertical plane is described for the first time. Second-order extended state observers are designed to estimate the unmeasurable velocity and acceleration term of UG. The rigorous stability analysis based on Lyapunov synthesis conforms the uniform ultimate boundedness of the closed-loop system tracking error. Razumikhin-type stability analysis combined with linear matrix inequality technique is employed to facilitate the selection of controller parameters. Finally, simulation results compared with LRDC and proportional–integral–derivative with delay compensator scheme validate the good tracking performance and superior robustness of the proposed control scheme. Experimental studies show the practical effectiveness for preventing operating beyond the prescribed depth and tolerating the input delay.

A new modulation strategy and power balance method of unsymmetrical CHB inverter

There exist the problems of the low utilization rate of DC voltage, power imbalance and current backflow in the modulation process of traditional unsymmetrical cascaded units. In view of these, a pulse width modulation strategy with an improved trapezoidal wave as the modulation wave and its corresponding power balance method are proposed. First, through double Fourier analysis of the output line voltage, an appropriate sine coefficient δ is obtained. Second, the waveform of the modulation wave is determined according to δ. Third, through the simple logic operation of the initial driving signals of the switch transistors, the required switch driving signals are obtained, and the power balance of each unit with the same voltage level is realized. In order to verify the effectiveness of the strategy and the method, a simulation and experiment platform are built, and a large number of simulations and experiments are carried out. The results show that compared with the traditional sinusoidal modulation method, the utilization rate of DC voltage obtained by the method can be improved by 22.28% and compared with the trapezoidal wave modulation method, the waveform distortion coefficient of the output voltage can be significantly reduced, and the utilization rate of DC voltage can be further improved. Meanwhile, the power balance can be achieved within one voltage cycle and the phenomenon of current backflow is eliminated.

Amusement Industry Geotechnics

Wooden coasters have existed for over a century, steel coasters about the same. For as long as coasters have been around, so have coaster enthusiasts, some of whom purport the concept of negative g's. But those of you who hold the same opinion should go back and take a refresher course in statics and dynamics. By simple logic, there's no such thing as negative gravity because a person or an object cannot weigh less than zero. The confused coaster enthusiast mistakes weightlessness or “airtime” for negative g's.

Belief Functions on Distributive Lattices

The Dempster-Shafer theory of belief functions is an important approach to deal with uncertainty in AI.In the theory, belief functions are defined on Boolean algebras of events. In many applications of belief functions in real world problems, however, the objects that we manipulateis no more a Boolean algebra but a distributive lattice. In this paper, we extend the Dempster-Shafer theory to the setting of distributive lattices, which has a mathematical theory as attractive as in that of Boolean algebras.Moreover, we apply this more general theory to a simple epistemic logic the first-degree-entailment fragment of relevance logic R, provide a sound and complete axiomatization for reasoning about belief functions for this logic and show that the complexity of the satisfiability problem of a belief formula with respect to the class of the corresponding Dempster-Shafer structures is NP-complete.

A Non-binary C-R Hybrid DAC for 12 b 100 MS/s CMOS SAR ADCs with Fast Residue Settling

This work proposes two versions of a 12 b 100 MS/s successive-approximation register (SAR) ADC based on a non-binary C-R hybrid DAC. The proposed DAC applies a non-binary weighted capacitor array to the 7 MSBs to meet the settling requirement of the DAC output and determines the remaining 5 LSBs using the reference voltages generated from a simple resistor string to reduce the DAC area significantly. The Version 1 ADC in a 28 ㎚ CMOS adopts a synchronous SAR logic and a comparator with a tail capacitor and a reset switch to minimize power consumption. The Version 2 ADC in a 0.18 ㎛ CMOS employs an asynchronous SAR logic with simple meta-stability correction logic to achieve high-speed operation. The Version 1 ADC which has an active die area of 0.042 ㎟ shows a maximum signal-to-noise-and-distortion ratio (SNDR) and spurious-free dynamic range (SFDR) of 62.3 and 77.3 ㏈, respectively, consuming 1.3 ㎽ with a 1.0 V supply voltage. The Version 2 ADC is based on a similar analog circuit topology, showing a maximum SNDR and SFDR of 60.1 and 73.5 ㏈, respectively, with an active die area of 0.30 ㎟, operating at a 1.8 V supply voltage.

Active voltage balancing strategy of asymmetric stacked multilevel inverter

Multilevel inverters (MLI) play an important role in AC applications and are undergoing continuous development in topology and control. In higher levels inverters, conventional MLIs have high components count which calls for modification of these topologies to obtain the same number of levels with fewer components to reduce cost and size. Balancing of the capacitors voltages is crucial for the operation of the MLI and it becomes more challenging in higher levels. This paper presents an active voltage balancing strategy for a reduced switch count five-leve topology which is the asymmetric stacked multilevel inverter (ASMLI). The ASMLI uses fewer components than the conventional MLIs when used in their five-levelconfiguration. The proposed active voltage balancing strategy uses simple measurements and logic to assure a balanced capacitors voltages during steady state and transients. The performance was examined and compared based on two modulation techniques with LCL filter and RL load using MATLAB/Simulink. The results show that the active voltage balancing strategy can trace all capacitors voltages to the reference value simultaneously with less than 1% voltage error, fast dynamic response, and an acceptable total harmonic distortion (THD) which allows the proposed setup to be an available option for medium voltage applications.<br /><div> </div>

Simple Rules: An Exploration of Contextual Contingencies on Competitive Performance

We investigate the performance efficacy of simple rules under four theoretically anticipated contextual contingencies: environmental uncertainty, technology intensity, firm age, and firm size. Our theorizing is based on the efficiency-flexibility balance that underpins simple rule logic as well as notions from contingency theory. We use a unique survey dataset of 254 US-based executives to test our hypotheses. We find that simple rule usage is broadly beneficial to competitive performance. Counterintuitively, we find that environmental uncertainty does not moderate the simple rule usage – competitive performance relationship. We find that simple rule usage is associated with competitive performance in firms with high technological intensity but does not offer differential benefit based on firm age or size. Post-hoc analysis indicates that managers tend to limit usage of simple rules in uncertain environments (mismatching with performance indications) and promote usage in firms with high technological intensity and large size. Broadly, we contribute to the simple rules literature by highlighting the boundary conditions under which simple rules are effective for competitive performance.

Study and Optimization of Full Flow Test Method for Core Safety Injection System

Due to the high pressure safety injection full-flow test of the nuclear power plant core safety injection system (RIS), there are problems such as the limit of the pump starting condition, the “blind adjustment” of the valve flow, the test logic defect, and the number of unqualified tests, etc. This research has improved the test principle and operation steps of the RIS high pressure safety injection full-flow periodic test, and describes in detail the comparison between the new scheme and the existing RIS high pressure safety injection full-flow test scheme, and the actual verification effect. The research results show that the optimized RIS high pressure safety injection full-flow test has the advantages of simple logic, easy operation, high qualification rate, and short test period, and it is worthy of popularization and application in this reactor type.

Diagnostic Data Integration Using Deep Neural Networks for Real-Time Plasma Analysis

Recent advances in acquisition equipment are providing experiments with growing amounts of precise, yet affordable sensors. At the same time, an improved computational power, coming from new hardware resources [GPU, field-programmable gate array (FPGA), adaptive compute acceleration platform (ACAP)] has been made available at relatively low costs. This led us to explore the possibility of completely renewing the chain of acquisition for a fusion experiment, where many high-rate sources of data, coming from different diagnostics, can be combined in a wide framework of algorithms. If, on the one hand, adding new data sources with different diagnostics enriches our knowledge about physical aspects, on the other hand, the dimensions of the overall model grow, making relations among variables more and more opaque. A new approach for integrating such heterogeneous diagnostics, based on the composition of deep variational autoencoders, could ease this problem, acting as a structural sparse regularizer. This has been applied to RFX-mod experimental data, integrating the soft X-ray linear images of plasma temperature with the magnetic state. However, to ensure a real-time signal analysis, these algorithmic techniques must be adapted to run in well-suited hardware. In particular, it is shown that, attempting a quantization of neuron transfer functions, such models can be adapted to run in an embedded programmable logic device. The resulting firmware, approximating the deep inference model to a set of simple operations, fits well with the simple logic units that are largely abundant in FPGAs. This is the key factor that permits the use of affordable hardware with complex deep neural topology and operates them in real-time.

Superconducting Circuits without Inductors Based on Bistable Josephson Junctions

Magnetic flux quantization in superconductors allows the implementation of fast and energy-efficient digital superconducting circuits. However, the information representation in magnetic flux severely limits their functional density presenting a long-standing problem. Here we introduce a concept of superconducting digital circuits that do not utilize magnetic flux and have no inductors. We argue that neither the use of geometrical nor kinetic inductance is promising for the deep scaling of superconducting circuits. The key idea of our approach is the utilization of bistable Josephson junctions allowing the representation of information in their Josephson energy. Since the proposed circuits are composed of Josephson junctions only, they can be called all-Josephson junction (all-JJ) circuits. We present a methodology for the design of the circuits consisting of conventional and bistable junctions. We analyze the principles of the circuit functioning, ranging from simple logic cells and ending with an 8-bit parallel adder. The utilization of bistable junctions in the all-JJ circuits is promising in the aspects of simplification of schematics and the decrease of the JJ count leading to space-efficiency.

Radical SAM Enzymes and Ribosomally-Synthesized and Post-translationally Modified Peptides: A Growing Importance in the Microbiomes.

To face the current antibiotic resistance crisis, novel strategies are urgently required. Indeed, in the last 30 years, despite considerable efforts involving notably high-throughput screening and combinatorial libraries, only few antibiotics have been launched to the market. Natural products have markedly contributed to the discovery of novel antibiotics, chemistry and drug leads, with more than half anti-infective and anticancer drugs approved by the FDA being of natural origin or inspired by natural products. Among them, thanks to their modular structure and simple biosynthetic logic, ribosomally synthesized and posttranslationally modified peptides (RiPPs) are promising scaffolds. In addition, recent studies have highlighted the pivotal role of RiPPs in the human microbiota which remains an untapped source of natural products. In this review, we report on recent developments in radical SAM enzymology and how these unique biocatalysts have been shown to install complex and sometimes unprecedented posttranslational modifications in RiPPs with a special focus on microbiome derived enzymes.

Competence versus Priorities: Negative Electoral Responses to Education Quality in Brazil

Do voters reward politicians for the quality of public services? We address this question by studying voters’ responses to signals of municipal school quality in Brazil, a setting particularly favorable to electoral accountability. Findings from a regression discontinuity design and a field experiment are strikingly consistent. Contrary to expectations, signals of school quality decrease electoral support for the local incumbent. However, we find the expected effect among citizens for whom school quality should be most salient—parents with children in municipal schools. Using an online survey experiment, we argue that voters who do not value education interpret school quality as an indicator of municipal policy priorities and perceive trade-offs with other services. Voters may hold politicians accountable not only for their competence but also for their representation of potentially conflicting interests—a fact that complicates the simple logic behind many accountability interventions.

Inkjet Printed Textile Force Sensitive Resistors for Wearable and Healthcare Devices.

Pressure sensors for wearable healthcare devices, particularly force sensitive resistors (FSRs) are widely used to monitor physiological signals and human motions. However, current FSRs are not suitable for integration into wearable platforms. This work presents a novel technique for developing textile FSRs (TFSRs) using a combination of inkjet printing of metal-organic decomposition silver inks and heat pressing for facile integration into textiles. The insulating void by a thermoplastic polyurethane (TPU) membrane between the top and bottom textile electrodes creates an architectured piezoresistive structure. The structure functions as a simple logic switch where under a threshold pressure the electrodes make contact to create conductive paths (on-state) and without pressure return to the prior insulated condition (off-state). The TFSR can be controlled by arranging the number of layers and hole diameters of the TPU spacer to specify a wide range of activation pressures from 4.9kPa to 7.1MPa. For a use-case scenario in wearable healthcare technologies, the TFSR connected with a readout circuit and a mobile app shows highly stable signal acquisition from finger movement. According to the on/off state of the TFSR with LED bulbs by different weights, it can be utilized as a textile switch showing tactile feedback.

Neural Network Training Acceleration With RRAM-Based Hybrid Synapses.

Hardware neural network (HNN) based on analog synapse array excels in accelerating parallel computations. To implement an energy-efficient HNN with high accuracy, high-precision synaptic devices and fully-parallel array operations are essential. However, existing resistive memory (RRAM) devices can represent only a finite number of conductance states. Recently, there have been attempts to compensate device nonidealities using multiple devices per weight. While there is a benefit, it is difficult to apply the existing parallel updating scheme to the synaptic units, which significantly increases updating process’s cost in terms of computation speed, energy, and complexity. Here, we propose an RRAM-based hybrid synaptic unit consisting of a “big” synapse and a “small” synapse, and a related training method. Unlike previous attempts, array-wise fully-parallel learning is possible with our proposed architecture with a simple array selection logic. To experimentally verify the hybrid synapse, we exploit Mo/TiOx RRAM, which shows promising synaptic properties and areal dependency of conductance precision. By realizing the intrinsic gain via proportionally scaled device area, we show that the big and small synapse can be implemented at the device-level without modifications to the operational scheme. Through neural network simulations, we confirm that RRAM-based hybrid synapse with the proposed learning method achieves maximum accuracy of 97 %, comparable to floating-point implementation (97.92%) of the software even with only 50 conductance states in each device. Our results promise training efficiency and inference accuracy by using existing RRAM devices.