Identifying Brand Consistency by Product Differentiation Using CNN

This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions.Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands.The investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late Stage.Results show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world.The relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style roadmaps.In addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred boundary.The study provides insights into practical problems for designers,manufacturers,and marketers in product design.It not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency.Designers can use these techniques to find features that influence brand style.Then,capture these features as innovative design elements and maintain core brand values.

Non-specific Arm Pain

Non-specific arm pain is a special clinical condition that can occur in work-related activities that involve maintaining a static position for prolonged periods or repetitive and frequent movements of the hand or entire arm. Such activities include typing on a keyboard, maneuvering a computer mouse, playing musical instruments （such as piano and guitar） and many forms of manual labor. The pain is dull and diffuse; It is localized in the forearm or in the hand but quickly can expand to the entire extremity. Non-specific arm pain is the most frequent type of work-related pain after lower-back pain. It thus has important socio-economic significance as a major cause of absence from work. The designation of ＂non-specific＂ originates from the fact that it has no obvious signs of tissue damage, unlike the ＂specific＂ pain accompanying carpal tunnel syndrome, tenosinovitis de Quervain, or lateral epicondylitis. Suggested causes of the pain include microtrauma of soft tissue followed by an inflammatory reaction, ischemia, fatigue, hyper-sensitization of nociceptors, focal dystonia of the hand and/or psychological stress. Treatment consists of application of NSAIDs, physical modalities, stretching and aerobic exercises. Prevention focuses on ergonomic modification during manual labor or work on a computer.