An Intelligent Auxiliary Vacuum Brake System

The purpose of this paper focuses on designing an intelligent, compact, reliable, and robust auxiliary vacuum brake system (VBS) with Kalman filter and self-diagnosis scheme. All of the circuit elements in the designed system are integrated into one programmable system-on-chip (PSoC) with entire computational algorithms implemented by software. In this system, three main goals are achieved: (a) Kalman filter and hysteresis controller algorithms are employed within PSoC chip by software to surpass the noises and disturbances from hostile surrounding in a vehicle. (b) Self-diagnosis scheme is employed to identify any breakdown element of the auxiliary vacuum brake system. (c) Power MOSFET is utilized to implement PWM pump control and compared with relay control. More accurate vacuum pressure control has been accomplished as well as power energy saving. In the end, a prototype has been built and tested to confirm all of the performances claimed above

Team Quotients, Resilience, and Performance of Software Development Projects

Past studies have examined actions and strategies that software project teams can take to reduce the negative impact of uncertainties, such as changing requirements. Software development project teams often have to be flexible to follow the pre-defined plans and strive to meet project goals. Sometimes uncertainty may go extreme to temporarily slow projects down and set project teams into reduced productivity. Project teams should be resilient to recover from the reduce productivity condition and move forward toward predefined goals. This study focuses on understanding the importance of team resilience for software project teams and exploring the antecedents of team resilience. Specifically, we investigate the impacts of intelligence and emotional quotient on team resilience capability, the extent to which project team can recover from the impediment and move forward. This is a research-in-progress work. A future empirical test plan has been discussed at the end

THE ANTECEDENTS OF AN INDIVIDUAL\u27S COMMITMENTS TOWARD CONTINUOUSLY USING SOCIAL NETWORK SITE

The Social network sites (SNS) has been rapid diffusion around the world. With the increasing importance of SNS, continuance intention also becomes a popular issue in the SNS context. SNS providers have to maintain better relationships with users and make individuals continue to use their sites. Based on this phenomenon, the objective of this study is to gain a better understanding of the continuance intention of SNS through examining the effects of commitments. Specifically, followed Meyer and Allen’s three-component model of commitment, we develop a theoretical model to understand the factors that influence normative, affective and continued commitment and investigate the effects of commitments on continuance intention in the SNS context. Through a survey-based empirical investigation, we anticipate the results to enhance our existing knowledge on continuance intention in the SNS context

A Combined Dynamical and Statistical Downscaling Technique to Reduce Biases in Climate Projections: An Example for Winter Precipitation and Snowpack in the Western United States

Large biases associated with climate projections are problematic when it comes to their regional application in the assessment of water resources and ecosystems. Here, we demonstrate a method that can reduce systematic biases in regional climate projections. The global and regional climate models employed to demonstrate the technique are the Community Climate System Model (CCSM)and the Weather Research and Forecasting (WRF) model. The method first utilized a statistical regression technique and a global reanalysis dataset to correct biases in the CCSM-simulated variables (e.g., temperature, geopotential height, specific humidity, and winds) that are subsequently used to drive the WRF model. The WRF simulations were conducted for the western United States and were driven with (a) global reanalysis, (b) original CCSM, and (c) bias-corrected CCSM data. The bias-corrected CCSM data led to a more realistic regional climate simulation of precipitation and associated atmospheric dynamics, as well as snow water equivalent (SWE), in comparison to the original CCSM-driven WRF simulation. Since most climate applications rely on existing global model output as the forcing data (i.e., they cannot re-run or change the global model), which often contain large biases, this method provides an effective and economical tool to reduce biases in regional climate downscaling simulations of water resource variables

Resonance-induced sensitivity enhancement method for conductivity sensors

Methods and systems for improving the sensitivity of a variety of conductivity sensing devices, in particular capacitively-coupled contactless conductivity detectors. A parallel inductor is added to the conductivity sensor. The sensor with the parallel inductor is operated at a resonant frequency of the equivalent circuit model. At the resonant frequency, parasitic capacitances that are either in series or in parallel with the conductance (and possibly a series resistance) is substantially removed from the equivalent circuit, leaving a purely resistive impedance. An appreciably higher sensor sensitivity results. Experimental verification shows that sensitivity improvements of the order of 10,000-fold are possible. Examples of detecting particulates with high precision by application of the apparatus and methods of operation are described