Central Carolina Hosiery Oct 2015 - Jun 2016
Controller
Swish White River Ltd. Mar 2014 - Mar 2015
Director of Finance | Controller | Accounting Manager
Swish White River Ltd. Oct 1989 - Mar 2014
Vice President | Controller | Accounting Manager
First Twin State Bank Jun 1987 - Oct 1989
Commercial Loan Analyst
Carolinasdentist.com Jun 1987 - Oct 1989
Education:
Lyndon State College 1983 - 1987
Bachelors, Accounting
University of Vermont 1977 - 1980
Skills:
Accounting Financial Reporting General Ledger Finance Budgets Financial Analysis Quickbooks Business Analysis Business Planning Business Strategy Business Process Improvement Key Performance Indicators Financial Accounting Banking Change Management Leadership Microsoft Office Microsoft Word Microsoft Excel Outlook Data Analysis Team Building Forecasting Human Resources Inventory Management Insurance Logistics Linux Management Operations Management Strategic Planning Public Speaking Retail Teaching Training Troubleshooting Telecommunications Vendor Management Windows Windows Server Windows Xp Cfos Controllers Finance Management Financial Statements Sales Tax Tax Preparation Software Installation Health Insurance Sage 50
Interests:
Environment Children Education Economic Empowerment
Dr. Ladd graduated from the Tulane University School of Medicine in 1979. He works in Houma, LA and specializes in Cardiovascular Disease and Interventional Cardiology. Dr. Ladd is affiliated with Terrebonne General Medical Center and Thibodaux Regional Medical Center.
- Singapore, SG William Knox Ladd - Raleigh NC, US
International Classification:
G06F 12/14
Abstract:
A system and corresponding method alter memory accesses using machine learning. The system comprises a system controller coupled to a processing system that is coupled to a memory system. The system further comprises a learning system coupled to the system controller. The learning system identifies, via a machine learning process, variations on a manner for altering memory access of the memory system to meet at least one goal. The system controller applies the variations identified to the processing system. The machine learning process employs at least one monitored parameter to converge on a given variation of the variations identified and applied. The at least one monitored parameter is affected by the memory access. The given variation enables the at least one goal to be met, improving the processing system, such as by increasing throughput, reducing latency, reducing power consumption, reducing temperature, etc.
System And Method For Neural Network-Based Autonomous Driving
A system and corresponding method for autonomous driving of a vehicle are provided. The system comprises at least one neural network (NN) that generates at least one output for controlling the autonomous driving. The system further comprises a main data path that routes bulk sensor data to the at least one NN and a low-latency data path with reduced latency relative to the main data path. The low-latency data path routes limited sensor data to the at least one NN which, in turn, employs the limited sensor data to improve performance of the at least one NN's processing of the bulk sensor data for generating the at least one output. Improving performance of the at least one NN's processing of the bulk sensor data enables the system to, for example, identify a safety hazard sooner, enabling the autonomous driving to divert the vehicle and avoid contact with the safety hazard.
- DAVIDSON NC, US WILLIAM FRANKLIN LADD - RALEIGH NC, US
International Classification:
F24F 11/00 F24F 11/06 G05B 15/02
Abstract:
A system and method of optimizing water cooling system energy efficiency, including a monitoring device to receive heat data corresponding to heat energy of a unit of water associated with a recirculating water system, power data corresponding to power being applied to the system, and load data corresponding to a load associated with the system. The monitoring device determines a measured metric by calculating a measured rate of water traversing the recirculating water system based on the heat data and determining a ratio of the power data and the measured rate of water. The monitoring device determines an efficiency metric of the system by comparing the load data to a look-up table and, based thereon, calculates a key performance indicator of the recirculating water system as a ratio of the efficiency metric and the measured metric, which is output to a graphical user interface.