https://pubsonline.informs.org/doi/pdf/10.1287/mksc.22.4.544.erratum2

Demand and Supply Dynamics for Sequentially Released Products in International Markets: The Case of Motion Pictures

Digital twins to enhance the integration of ergonomics in the workplace design

Owning to the booming of social media, making comments or expressing opinions about merchandises online becomes easier than before. Data from social media might be one of the essential inputs for forecasting sales of vehicles. Besides, some other effects, such as stock market values, have influences on purchasing power of vehicles. In this paper, both multivariate regression models with social media data and stock market values and time series models are employed to predict monthly total vehicle sales. The least squares support vector regression (LSSVR) models are used to deal with multivariate regression data. Three types of data, namely sentiment scores of tweets, stock market values, and hybrid data, are employed in this paper to forecast monthly total vehicle sales in USA. The hybrid data contain both sentiment scores of tweets and stock market values. In addition, seasonal factors of monthly total vehicle sales are employed to deseasonalizing both monthly total vehicle sales and three types of input data. The time series models include the naive model, the exponential smoothing model, the autoregressive integrated moving average model, the seasonal autoregressive integrated moving average model, and backpropagation neural networks and LSSVR with time series models. The numerical results indicate that using hybrid data with deseasonalizing procedures by the LSSVR models can obtain more accurate results than other models with different data. Thus, both social media data and stock values are essential to forecast monthly total vehicle sales; and deseasonalizing procedures can improve forecasting accuracy in predicting monthly total vehicle sales.

Predicting Vehicle Sales by Sentiment Analysis of Twitter Data and Stock Market Values

Improved decisions for marketing, supply and purchasing: Mining big data through an integration of sentiment analysis and intuitionistic fuzzy multi criteria assessment

With the breakthroughs in artificial intelligence technology and the rapid development of intelligent manufacturing, industry and artificial intelligence (AI) are gradually being deeply integrated. On the basis of artificial intelligence, we systematically expounded the generation, definition, characteristics, classification, technical system, and current situation of industrial artificial intelligence (I-AI). Combining existing research and industrial projects, we propose a detailed framework and a reference model for I-AI in industry. The framework contains seven dimensions: objects of I-AI, domain of I-AI, application stages of I-AI, application requirements of I-AI, intelligent technology of I-AI, intelligent function of I-AI, and solutions of I-AI. Secondly, based on the application scenarios of artificial intelligence and industrial convergence, we propose a detailed overall planning for I-AI. Finally, five typical industrial fields are selected, and the I-AI solutions based on TFV (technology and function integration in industrial value chain) unit and 6W1H method are used for new application scenarios of the proposed framework. In addition, a detailed case of implementing for I-AI in port equipment industry is given. The research results of this paper have achieved good results in the related industrial field and can provide some reference for other industrial enterprises to plan, design, implement, and apply artificial intelligence.

A reference framework and overall planning of industrial artificial intelligence (I-AI) for new application scenarios

Box-office forecasting based on sentiments of movie reviews and Independent subspace method

Digital pre-assembly process can evaluate flaws in design prior to manufacturing.As these reports are written in free text form, they are not fully utilized.This paper proposes a method of applying text mining techniques to extract insights.The proposed method successfully extracts useful information from the text database. After digitally designing components of vehicles, a design team creates a virtual manufacturing environment that resembles actual manufacturing facilities. During this digital pre-assembly process, a review team examines each component, and records its problems and requirements in part verification reports. Once these reports are delivered to specific design team responsible for each part, the design team can make appropriate adjustments to their designs. This digital pre-assembly process can evaluate and prevent flaws in design prior to actual manufacturing, improving production quality and reducing manufacturing cost. As these reports are written in free text form, they, however, are not fully utilized for understanding problems arising from the design process. This paper proposes a method of applying text mining techniques on verification reports to extract insights for quality improvement. In this paper, following three text mining approaches are proposed: (1) Extracting n-grams for text preprocessing and constructing domain ontology; (2) Extracting meaningful insights from text preprocessing; (3) Creating intuitive visual tools to understand the extracted insights. The proposed method is applied on approximately 140,000 reports, and is validated through the quality of the answers obtained for the questions posed by the domain experts. The proposed method successfully extracts useful information from the text database, and provides intuitive graphical interface, thereby satisfying the need of the domain experts. This paper proposes a systematic framework of transforming huge amount of raw text data into intuitive visualization. Through this framework, meaningful knowledge can be extracted, analyzed and shared to improve the quality of the products. Main contribution of our paper is that it proposes a framework for knowledge extraction from pre-assembly process. Not only does it systematically arrange the data, but it also combines various data sources and creates a knowledge system to improve efficiency of the design process.

Knowledge extraction and visualization of digital design process

In shipbuilding, the man-hour is a unit widely used for production planning, with systematic prediction of man-hour taking greater importance in cost reduction. However, as the man-hours are predicted by experts at shipyards, existing methods have often resulted in incorrect predictions and cost significant amount of time. There have been several attempts made by many researchers to overcome such problems resulting from prediction by experts. Yet, their approaches considered only a limited number of factors such as ship specifications, and were not highly applicable at shipyards. In this study, we propose a system that predicts man-hours with deployable data in different times of manufacturing process and that can be applied in practical shipbuilding. The results demonstrated the possibility that our prediction system could be a good alternative to existing prediction methods.

A study on the man-hour prediction system for shipbuilding

Modern object detection and instance segmentation networks stumble when picking out humans in crowded or highly occluded scenes. Yet, these are often scenarios where we require our detectors to work well. Many works have approached this problem with model-centric improvements. While they have been shown to work to some extent, these supervised methods still need sufficient relevant examples (i.e. occluded humans) during training for the improvements to be maximised. In our work, we propose a simple yet effective data-centric approach, Occlusion Copy&Paste, to introduce occluded examples to models during training - we tailor the general copy&paste augmentation approach to tackle the difficult problem of same-class occlusion. It improves instance segmentation performance on occluded scenarios for"free"just by leveraging on existing large-scale datasets, without additional data or manual labelling needed. In a principled study, we show whether various proposed add-ons to the copy&paste augmentation indeed contribute to better performance. Our Occlusion Copy&Paste augmentation is easily interoperable with any models: by simply applying it to a recent generic instance segmentation model without explicit model architectural design to tackle occlusion, we achieve state-of-the-art instance segmentation performance on the very challenging OCHuman dataset. Source code is available at https://github.com/levan92/occlusion-copy-paste.

Humans need not label more humans: Occlusion Copy & Paste for Occluded Human Instance Segmentation

We present a new paradigm for fine-tuning large-scale vision-language pre-trained models on downstream task, dubbed Prompt Regularization (ProReg). Different from traditional fine-tuning which easily overfits to the downstream task data, ProReg uses the prediction by prompting the pretrained model to regularize the fine-tuning. The motivation is: by prompting the large model “a photo of a [CLASS]”, the fill-in answer is only dependent on the pretraining encyclopedic knowledge while independent of the task data distribution, which is usually biased. Specifically, given a training sample prediction during fine-tuning, we first calculate its Kullback-Leibler loss of the prompt prediction and Cross-Entropy loss of the ground-truth label, and then combine them with a proposed sample-wise adaptive trade- off weight, which automatically adjusts the transfer between the pretrained and downstream domains. On various out-of-distribution benchmarks, we show the consistently strong performance of ProReg compared with conventional fine-tuning, zero-shot prompt, prompt tuning, and other state-of-the-art methods.

Minhoe Hur

Papers

Box-office forecasting based on sentiments of movie reviews and Independent subspace method

Knowledge extraction and visualization of digital design process

A study on the man-hour prediction system for shipbuilding

Humans need not label more humans: Occlusion Copy & Paste for Occluded Human Instance Segmentation

Debiased Fine-Tuning for Vision-language Models by Prompt Regularization