Big Data - Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques

This article intends to systematically identify and comparatively analyze state-of-the-art supply chain (SC) forecasting strategies and technologies. A novel framework has been proposed incorporating Big Data Analytics in SC Management (problem identification, data sources, exploratory data analysis, machine-learning model training, hyperparameter tuning, performance evaluation, and optimization), forecasting effects on human-workforce, inventory, and overall SC. Initially, the need to collect data according to SC strategy and how to collect them has been discussed. The article discusses the need for different types of forecasting according to the period or SC objective. The SC KPIs and the error-measurement systems have been recommended to optimize the top-performing model. The adverse effects of phantom inventory on forecasting and the dependence of managerial decisions on the SC KPIs for determining model performance parameters and improving operations management, transparency, and planning efficiency have been illustrated. The cyclic connection within the framework introduces preprocessing optimization based on the post-process KPIs, optimizing the overall control process (inventory management, workforce determination, cost, production and capacity planning). The contribution of this research lies in the standard SC process framework proposal, recommended forecasting data analysis, forecasting effects on SC performance, machine learning algorithms optimization followed, and in shedding light on future research

5MHz PWM-controlled current-mode resonant DC-DC converter using GaN-FETs

In this paper, the method of the realization of a MHz level switching frequency DC-DC converter for high power-density is presented. For high power-density, Gallium Nitride field effect transistor (GaN-FET) and current-mode resonant DC-DC converter are adopted. In addition, the proposed pulse width modulation (PWM) control method which is suitable for the isolated current-mode resonant DC-DC converter operated at MHz level switching frequency, and the novel primary-side zero voltage switching (ZVS) turn on method for the proposed PWM control are presented. Some experiments have been done with 5MHz isolated DC-DC converter which has GaN-FET, and the total volume of the circuit is 16.14cm3. With the proposed PWM control method, input voltage range is 36-44V, and maximum load current range is 8A at Vi = 44V. The primary-side ZVS turn on is confirmed, and the maximum power-efficiency is 89.4%.7th International Power Electronics Conference, IPEC-Hiroshima - ECCE Asia 2014; Hiroshima; Japan; 18 May 2014 through 21 May 201

Five-Megahertz PWM-Controlled Current-Mode Resonant DC?DC Step-Down Converter Using GaN-HEMTs

High power efficiency and high power density are required in regulated isolated dc-dc converters. In this paper, a novel pulsewidth modulation (PWM) control method that is suitable for an isolated current-mode resonant dc-dc converter operated at a megahertz-level switching frequency is proposed. The output voltage with the proposed method can be regulated with no additional components at a fixed switching frequency. In addition, the zero-voltage switching (ZVS) of primary-side switches at turn on can be maintained. The principle of the proposed method and the method of the ZVS operation in the proposed method are explained. Some experiments have been performed with a 5-MHz isolated step-down dc-dc converter using gallium nitride high-electron-mobility transistors; the output voltage is 12 V, and the total volume of the circuit is 16.14 cm3. With the proposed PWM control method, the input voltage range is 42-45.5 V, and the maximum load current range is 10 A at Vi = 45.5 V. The ZVS of the primary-side switches at turn on is confirmed in all experimental regions, and the maximum power efficiency is 89.2%

High frequency PWM-controlled current-mode resonant DC-DC converter with boost conversion

In this paper, a new pulse width modulation (PWM) control method for the isolated current-mode resonant converter with a fixed switching frequency is presented. The circuit topology is the same as a conventional resonant converter with synchronous rectification and without any additional components. The control technique for the output voltage regulation is proposed with the unique PWM control for synchronously-rectifying switches. By using the transformer\u27s leakage inductance and the PWM control, the boost conversion can be realized. Also, the zero-voltage switching (ZVS) operation can be done for primary switches, simultaneously. Some experiments have been done with 5MHz isolated DC-DC converter which has Gallium Nitride field effect transistor (GaN-FET).2013 15th European Conference on Power Electronics and Applications, EPE 2013; Lille; France; 2 September 2013 through 6 September 201

YOLIC: An Efficient Method for Object Localization and Classification on Edge Devices

In the realm of Tiny AI, we introduce "You Only Look at Interested Cells" (YOLIC), an efficient method for object localization and classification on edge devices. Seamlessly blending the strengths of semantic segmentation and object detection, YOLIC offers superior computational efficiency and precision. By adopting Cells of Interest for classification instead of individual pixels, YOLIC encapsulates relevant information, reduces computational load, and enables rough object shape inference. Importantly, the need for bounding box regression is obviated, as YOLIC capitalizes on the predetermined cell configuration that provides information about potential object location, size, and shape. To tackle the issue of single-label classification limitations, a multi-label classification approach is applied to each cell, effectively recognizing overlapping or closely situated objects. This paper presents extensive experiments on multiple datasets, demonstrating that YOLIC achieves detection performance comparable to the state-of-the-art YOLO algorithms while surpassing in speed, exceeding 30fps on a Raspberry Pi 4B CPU. All resources related to this study, including datasets, cell designer, image annotation tool, and source code, have been made publicly available on our project website at https://kai3316.github.io/yolic.github.i

A Dynamic Ensemble Selection of Deepfake Detectors Specialized for Individual Face Parts

The development of deepfake technology, based on deep learning, has made it easier to create images of fake human faces that are indistinguishable from the real thing. Many deepfake methods and programs are publicly available and can be used maliciously, for example, by creating fake social media accounts with images of non-existent human faces. To prevent the misuse of such fake images, several deepfake detection methods have been proposed as a countermeasure and have proven capable of detecting deepfakes with high accuracy when the target deepfake model has been identified. However, the existing approaches are not robust to partial editing and/or occlusion caused by masks, glasses, or manual editing, all of which can lead to an unacceptable drop in accuracy. In this paper, we propose a novel deepfake detection approach based on a dynamic configuration of an ensemble model that consists of deepfake detectors. These deepfake detectors are based on convolutional neural networks (CNNs) and are specialized to detect deepfakes by focusing on individual parts of the face. We demonstrate that a dynamic selection of face parts and an ensemble of selected CNN models is effective at realizing highly accurate deepfake detection even from partly edited and occluded images