Smart factories merge various technologies in a manufacturing environment in order to improve factory performance and product quality. In recent years, these smart factories have received a lot of attention from researchers. In this paper, we introduce a defective product classification system based on deep learning for application in smart factories. The key component of the proposed system is a programmable logic controller (PLC) artificial intelligence (AI) embedded board; we call this an AI Edge-PLC module. A pre-trained defective product classification model is uploaded to a cloud service from where the AI Edge-PLC can access and download it for use on a certain product, in this case, electrical wiring. Next, we setup the system to collect electrical wiring data in a real-world factory environment. Then, we applied preprocessing to the collected data in order to extract a region of interest (ROI) from the images. Due to limitations on the availability of appropriate labeled data, we used the transfer learning method to re-train a classification model for our purposes. The pre-trained models were then optimized for applications on AI Edge-PLC boards. After carrying out classification tasks, on our electrical wire dataset and on a previously published casting dataset, using various deep neural networks including VGGNet, ResNet, DenseNet, and GoogLeNet, we analyzed the results achieved by our system. The experimental results show that our system is able to classify defective products quickly with high accuracy in a real-world manufacturing environment.

Under market environments, electric customers are offered choices to manage and pay for their electricity usage corresponding to the wholesale market price; this can be referred to as demand response. This paper presents a framework for demand response of commercial buildings, particularly applies to the pumping systems of tall (high) buildings. The problem is to control the pumping systems in response to the market price to minimize the electricity charge. The idea is to utilize the water reservoirs on the top as energy storage devices to manage the electricity consumed by the pumping system from on-peak to off-peak hours, at the same time, maintains water level enough for the demand. The problem is formulated and solved by dynamic programming method. Then, it is tested in a case study assuming markets for real-time pricing.

One of the main causes of traffic collisions for cyclists is incoming vehicles in blind spots. In this paper, we present a helmet-mounted camera system that can identify moving objects which are considered potentially dangerous for cyclists. We first compute the road areas and allocate the positions of vehicles in the image sequences based on the road properties, the Haar-like features, and AdaBoost cascade classifier. Then, we introduce a new algorithm based on ORB features matching to evaluate the probability of collision with the cyclist. A prompt warning sign will show up if a collision is classified as the most likely. Several experiments were conducted in crowded traffic areas under different weather conditions. The experimental results show that our algorithm is highly effective in terms of predicting the danger due to moving vehicles in blind spots and can be processed in real-time.