Flash memory under cosmic and alpha irradiation

Neutron and proton irradiation to simulate cosmic ray jeopardy were used to establish that NOR Flash memory (conventional floating polySi gate or ONO floating gate MirrorBit) soft error failure rate (cross section) is 3-5 orders of magnitude better than SRAM. Flash memory soft error rate for a given dose of alpha particle irradiation is much less than for the same dose from simulated cosmic rays.     

Fuse regrowth kinetics

The kinetics of fuse regrowth during temperature humidity bias (THB) stresses were modeled with a power law for relative humidity (exponent /spl sim/2.3) and a power law for bias voltage (exponent /spl sim/1.7). Using these results we determined the optimal laser cut parameters to maximize product reliability.     

Product-specific moisture levels: A conceptual framework

An improved schedule to evaluate surface-mount technology popcorn jeopardy based on physics of moisture absorption is proposed. Key features are: 1) each lettered level implies a certain performance irrespective of package thickness (i.e., no penalty for thinner packages); 2) each successive level has stepped moisture concentration at the interior (i.e., clear stepped discrimination between levels); and 3) the time required for preconditioning is substantially reduced from current practice and an acceleration factor can be clearly computed.     

Cathodic protection of existing URD systems

The corrosion control experiences of the author with underground rural distribution (URD) systems are presented. The discussion includes (1) survey testing procedures, (2) designing of cathodic protection (CP) systems, (3) criteria for selecting material, (4) installation and maintenance procedures of CP systems, and (5) an economical evaluation of CP system installations. It is the opinion of the author that impressed current systems, supplemented by galvanic sacrificial anodes, can provide economical corrosion protection for existing direct-buried copper concentric neutral wires     

How we can better learn from electrical accidents

Using a corporate case study with data analysis, this article provides an example of how one company recognized the limitations of available electrical accident data and implemented a process for collecting information to support the improvement of the organizational culture and management systems around electrical safety. The business, manufacturing, engineering, behavioral safety, and regulatory compliance implications of the findings are discussed. Limitations in national and industrial electrical accident and fatality data are reviewed. Finally, future research directions are considered     

An Architecture for Human‐Guided Autonomy: Team TROOPER at the DARPA Robotics Challenge Finals

Recent robotics efforts have automated simple, repetitive tasks to increase execution speed and lessen an operator's cognitive load, allowing them to focus on higher‐level objectives. However, an autonomous system will eventually encounter something unexpected, and if this exceeds the tolerance of automated solutions, there must be a way to fall back to teleoperation. Our solution is a largely autonomous system with the ability to determine when it is necessary to ask a human operator for guidance. We call this approach human‐guided autonomy. Our design emphasizes human‐on‐the‐loop control where an operator expresses a desired high‐level goal for which the reasoning component assembles an appropriate chain of subtasks. We introduce our work in the context of the DARPA Robotics Challenge (DRC) Finals. We describe the software architecture Team TROOPER developed and used to control an Atlas humanoid robot. We employ perception, planning, and control automation for execution of subtasks. If subtasks fail, or if changing environmental conditions invalidate the planned subtasks, the system automatically generates a new task chain. The operator is able to intervene at any stage of execution, to provide input and adjustment to any control layer, enabling operator involvement to increase as confidence in automation decreases. We present our performance at the DRC Finals and a discussion about lessons learned.     

Query-based learning for aerospace applications

Models of real-world applications often include a large number of parameters with a wide dynamic range, which contributes to the difficulties of neural network training. Creating the training data set for such applications becomes costly, if not impossible. In order to overcome the challenge, one can employ an active learning technique known as query-based learning (QBL) to add performance-critical data to the training set during the learning phase, thereby efficiently improving the overall learning/generalization. The performance-critical data can be obtained using an inverse mapping called network inversion (discrete network inversion and continuous network inversion) followed by oracle query. This paper investigates the use of both inversion techniques for QBL learning, and introduces an original heuristic to select the inversion target values for continuous network inversion method. Efficiency and generalization was further enhanced by employing node decoupled extended Kalman filter (NDEKF) training and a causality index (CI) as a means to reduce the input search dimensionality. The benefits of the overall QBL approach are experimentally demonstrated in two aerospace applications: a classification problem with large input space and a control distribution problem.     

Training neural networks using Central Force Optimization and Particle Swarm Optimization: Insights and comparisons

Central Force Optimization (CFO) is a novel and upcoming metaheuristic technique that is based upon physical kinematics. It has previously been demonstrated that CFO is effective when compared with other metaheuristic techniques when applied to multiple benchmark problems and some real world applications. This work applies the CFO algorithm to training neural networks for data classification. As a proof of concept, the CFO algorithm is first applied to train a basic neural network that represents the logical XOR function. This work is then extended to train two different neural networks in order to properly classify members of the Iris data set. These results are compared and contrasted to results gathered using Particle Swarm Optimization (PSO) in the same applications. Similarities and differences between CFO and PSO are also explored in the areas of algorithm design, computational complexity, and natural basis. The paper concludes that CFO is a novel and promising meta-heuristic that is competitive with if not superior to the PSO algorithm, and there is much room to further improve it.     

Adaptive critic designs

We discuss a variety of adaptive critic designs (ACDs) for neurocontrol. These are suitable for learning in noisy, nonlinear, and nonstationary environments. They have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Our discussion of these origins leads to an explanation of three design families: heuristic dynamic programming, dual heuristic programming, and globalized dual heuristic programming (GDHP). The main emphasis is on DHP and GDHP as advanced ACDs. We suggest two new modifications of the original GDHP design that are currently the only working implementations of GDHP. They promise to be useful for many engineering applications in the areas of optimization and optimal control. Based on one of these modifications, we present a unified approach to all ACDs. This leads to a generalized training procedure for ACDs.     

The effectiveness of control structure diagrams in source codecomprehension activities

Recently, the first two in a series of planned comprehension experiments were performed to measure the effect of the control structure diagram (CSD) on program comprehensibility. Upper- and lower-division computer science and software engineering students were asked to respond to questions regarding the structure and execution of one source code module of a public domain graphics library. The time taken for each response and the correctness of each response was recorded. Statistical analysis of the data collected from these two experiments revealed that the CSD was highly significant in enhancing the subjects' performance in this program comprehension task. The results of these initial experiments promise to shed light on fundamental questions regarding the effect of software visualizations on program comprehensibility