Academic literature on the topic 'Personalized pattern-Making'

Along with the continuous development and improvement of the garment pattern making theory and echnique, as well as the more wide application of computer technique, the garments intelligent renders making pattern more efficient. Due to the complex body measure variation, the intelligent of garment personalized custom which is regularly limited by various spects also needs to continuously explore. Howe-ver, the conventional garment pattern process that is making brand new pattern or triamming the pieces of the standard size designation pattern in accordance with the individual sizes. This study intends to discuss the breakthrough of traditional pattern mode. For example the shirts, based on a series of the garments’ size designation pattern, extract individual samples from the anthropometric database. The series size designation patterns are split into the collar, sleeves, body and other garment parts that may involve variety. According to the need, g-enerate individual measure of the sample, extract the existing silhouette of corresponding parts from the series size designation patterns and fit them. Ultimately cbtain the experimental method of personalized pattern.

Fashionable FMCG clothing brands have emerged in large numbers, and fast fashionable and personalized clothing is increasingly favored by young people. In this era of rapid renewal, clothing has obviously become a fast moving consumer good. China’s textile industry continues to develop, and the speed of development accelerates, coupled with the introduction and promotion of the use of Western textile technology, making the pattern design of Chinese textiles develop more quickly. The long-term disconnection between product pattern design and product production has led to the fact that the research on product pattern has been only on the plane, so there is an urgent need to improve the design level of product pattern and the market competitiveness of products through systematic research on product pattern. Newton’s iteration is an important research content of nonlinear theory, which is a hot spot in the field of scientific research at present. Newton’s iteration is a common method for finding the roots of nonlinear equations, which has at least second-order convergence speed, but requires the calculation of first-order derivative values. In this paper, a preliminary discussion on the design method of pattern design based on Newton’s iterative algorithm is conducted to explore a new way of personalized pattern design while using nonlinear graphs as pattern design materials. The experimental results show that the average value of EME of Newton’s iteration algorithm is 3.479 and 2.072 higher than that of AVG Filter and CB Filter, respectively, and the difference of pattern generation accuracy between CB Filter and AVG Filter is not much, so the accuracy of Newton’s iteration algorithm for pattern generation is better than that of CB Filter and AVG Filter. Therefore, it is proved that it is feasible to transform the special texture effect based on Newton’s iteration algorithm into fabric.

The paper presents the theoretical and algorithmic aspects for making a personalized recommender system (mobile service) designed for public route transport users. The main focus is on identifying and formalizing the concept of "user preferences", which is the basis of modern personalized recommender systems. Informal (verbal) and formal (mathematical) formulations of the corresponding problems of determining "user preferences" in a specific spatial-temporal context are presented: the preferred stops definition and the preferred "transport correspondence" definition. The first task can be represented as a well-known classification problem. Thus, it can be formulated and solved using well-known pattern recognition and machine learning methods. The second is reduced to the construction of dynamic graphs series. The experiments were conducted on data from the mobile application "Pribyvalka-63". The application is the tosamara.ru service part, currently used to inform Samara residents about the public transport movement.

Purpose – The purpose of this paper is to concentrate on the development of individualized prototype of apparel patterns for young females from 3D body scanning data. Design/methodology/approach – The authors presented a new pattern-making approach that is composed of three major steps: to establish the relationships between body features and corresponding elements in a prototype (e.g. curve or a point); to classify the relationship into grades that provide alternatives to fit a variety of bodies; and to assemble each individual element into a personalized prototype. Findings – The experiment demonstrated that this method could be used for customized prototype development from 3D body scanning in a relatively easy way. Research limitations/implications – Currently, the subjects of this study included only Chinese young females, and the regression models were just suitable for the similar body types though, the research method could be extended to other somatotypes and age groups. Social implications – This approach can be used in the field of made-to-measure, mass customization, and the quick response for apparel pattern making. The technology in this paper facilitates to generate an individualized pattern prototype from 3D body scanning data. Originality/value – Originated from the relationship between the features of a human body and the elements of a pattern prototype, the authors presented a new approach to develop an individualized pattern prototype by classifying the features into grades.

Nowadays, location based services (LBS) has become one of the most popular applications with the rapid development of mobile Internet technology. More and more research is focused on discovering the required services among massive information according to the personalized behavior. In this paper, a collaborative filtering (CF) recommendation algorithm is presented based on the Location-aware Hidden Markov Model (LHMM). This approach includes three main stages. First, it clusters users by making a pattern similarity calculation of their historical check-in data. Then, it establishes the location-aware transfer matrix so as to get the next most similar service. Furthermore, it integrates the generated LHMM, user's score and interest migration into the traditional CF algorithm so as to generate a final recommendation list. The LHMM-based CF algorithm mixes the geographic factors and personalized behavior and experimental results show that it outperforms the state-of-the-art algorithms on both precision and recall.

The rise of personalized medicine and its remarkable advancements have revealed new requirements for the availability of appropriate medical decision-making models. Computer science is an area that plays an essential role in the field of personalized medicine, where one of the goals is to provide algorithms and tools to extrapolate knowledge and improve the decision-support process. The minimum clinically important difference (MCID) is the smallest change in PROM scores that patients perceive as meaningful. Treatment that does not achieve the minimum level of improvement is considered inappropriate as well as a potential waste of resources. Using the MCID threshold to identify patients who fail to achieve the minimum change in PROM that results in a meaningful outcome may aid in pre-surgical shared decision-making. The decision tree algorithm is a method for extracting valuable information and providing further meaningful information to the domain expert that supports the decision-making. In the present study, different tools based on machine learning were developed. On the one hand, we compared three XGBoost models to predict the non-achievement of the MCID at six months post-operation in the SF-12 physical score. The prediction score threshold was set to 0.75 to provide three decision-making areas on the basis of the high confidence (HC) intervals; the minority class was re-balanced by weighting the positive class to penalize the loss function (XGBoost cost-sensitive), oversampling the minority class (XGBoost with SMOTE), and re-sampling the negative class (XGBoost with undersampling). On the other hand, we modeled the data through a decision tree (assessment tree), based on different complexity levels, to identify the hidden pattern and to provide a new way to understand possible relationships between the gathered features and the several outcomes. The results showed that all the proposed models were effective as binary classifiers, as they showed moderate predictive performance both regarding the minority or positive class (i.e., our targeted patients, those who will not benefit from surgery) and the negative class. The decision tree visualization can be exploited during the patient assessment status to better understand if those patients will benefit or not from the medical intervention. Both of these tools can come in handy for increasing knowledge about the patient’s psychophysical state and for creating an increasingly specialized assessment of the individual patient.

Personalization in the apparel industry shows importance and the potential for demand, but the existing personalization has unreasonable time cost, labor cost, and resource waste. To solve the problems of the waste of resources as well as both time and labor cost caused by manual pattern making in clothing personalization, a method of automatic garment pattern generation based on a parametric formula and the Python language was proposed. Based on the classification of common curves in patterns, three curve fitting algorithms based on different parameters were derived and combined with the Python language to achieve personalized generation of different patterns by classifying the parameters in the system into key parameters, secondary parameters, and variable parameters. Three different methods for verifying the accuracy of the garment patterns were proposed based on curve fitting similarity and three-dimensional virtual modeling, and the accuracy of the proposed system was verified. The results show that the accuracy and comfort of the patterns generated via the system were high. Meanwhile, the Python-language-based system fits well with the production system of enterprises, which can improve the rapid response capability of garment personalization, greatly save the time cost and labor cost of enterprises, reduce resource loss, and contribute to the sustainable development of the garment industry.

This paper focuses on a new method to classify and convert the lower-body features of young females into the rules of individualized elements that are necessary for pant pattern design. Approaches used to develop a customized apparel pattern and their advantages and disadvantages are discussed in the literature review. Subsequently, a new method to analyze lower-body features for the development of individualized pant patterns is presented. The main method involved is the classification of certain body features into grades that provided alternatives for different body shapes, and the use of elements that are needed for pattern-making but that are difficult to measure directly in a three-dimensional image to predict the girths, half-girths and the other key measurements of a body. Three sets of rules were derived from this method: (a) individualized rules on height to classify the height at a feature into different grades, which help to find the location of the feature based on the height of a body; (b) individualized rules on girth to sort the thickness/width ratio of different cross-sections into grades at different features, which predict the girth of a characteristic feature from the thickness and the width of a feature; and (c) individualized rules on the crotch to find the relationship between the length of the measuring line at the divided points and the thickness of a certain girth, which reveal the individuality of the crotch and benefit the development of an individualized pant pattern. The method presented has the potential to realize individuality in female pant design by converting lower-body features into grades and establishing relationships with personalized parameters. This method can reduce the complexity of customization in pattern design.

Recent years have seen the rise of user-generated contents (UGCs) in online social media. Diverse UGC sources and information overload are making it increasingly difficult to satisfy personalized information needs. To organize UGCs in a user-centered way, we should not only map them based on textual topics but also link them with users and even user communities. We propose a multi-dimensional framework to organize information by connecting UGCs, users, and user communities. First, we use a topic model to generate a topic hierarchy from UGCs. Second, an author-topic model is applied to learn user interests. Third, user communities are detected through a label propagation algorithm. Finally, a multi-dimensional information organization pattern is formulated based on similarities among the topic hierarchies of UGCs, user interests, and user communities. The results reveal that: 1) our proposed framework can organize information from multiple sources in a user-centered way; 2) hierarchical topic structures can provide comprehensive and in-depth topics for users; and, 3) user communities are efficient in helping people to connect with others who have similar interests.

Blood pressure (BP) follows a circadian variation, increasing during active hours, showing a small postprandial valley and a deeper decrease during sleep. Nighttime reduction of 10–20% relative to daytime BP is defined as a dipper pattern, and a reduction of less than 10%, as a non-dipper pattern. Despite this BP variability, hypertension’s diagnostic criteria and therapeutic objectives are usually based on BP average values. Indeed, studies have shown that chrono-pharmacological optimization significantly reduces long-term cardiovascular risk if a BP dipper pattern is maintained. Changes in the effect of antihypertensive medications can be explained by circadian variations in their pharmacokinetics (PK) and pharmacodynamics (PD). Nevertheless, BP circadian variation has been scarcely included in PK-PD models of antihypertensive medications to date. In this work, we developed PK-PD models that include circadian rhythm to find the optimal dosing time (Ta) of first-line antihypertensive medications for dipper and non-dipper patterns. The parameters of the PK-PD models were estimated using global optimization, and models were selected according to the lowest corrected Akaike information criterion value. Simultaneously, sensitivity and identifiability analysis were performed to determine the relevance of the parameters and establish those that can be estimated. Subsequently, Ta parameters were optimized to maximize the effect on BP average, BP peaks, and sleep-time dip. As a result, all selected models included at least one circadian PK component, and circadian parameters had the highest sensitivity. Furthermore, Ta with which BP>130/80 mmHg and a dip of 10–20% are achieved were proposed when possible. We show that the optimal Ta depends on the therapeutic objective, the medication, and the BP profile. Therefore, our results suggest making chrono-pharmacological recommendations in a personalized way.

Les systèmes commerciaux de recommandation de vêtements ont été largement utilisés dans l'industrie de l'habillement. Cependant, les recherches existantes sur la conception de vêtements numériques se sont concentrées sur les évolutions techniques du processus de conception virtuelle, avec peu de retours de métier provenant des designers. La coupe d'un vêtement joue un rôle important dans l'achat de celui-ci par le client. Afin de développer un vêtement correctement ajusté, les stylistes et les modélistes doivent ajuster le patron du vêtement plusieurs fois jusqu'à ce que le client soit satisfait. Actuellement, le modélisme traditionnel présente trois inconvénients majeurs : 1) il est très long et inefficace, 2) il repose trop sur des concepteurs expérimentés, 3) la relation entre la forme du corps humain et le vêtement n'est pas pleinement explorée. Dans la pratique, le styliste joue un rôle clé dans la réussite du processus de conception. Il est nécessaire d'intégrer les connaissances et l'expérience du styliste dans les systèmes actuels de CAD de vêtements afin de fournir rapidement une solution de conception réalisable, centrée sur l'homme et à faible coût, pour chaque besoin personnalisé. En outre, les services basés sur les données, tels que les systèmes de recommandation, la classification des formes corporelles, la modélisation du corps en 3D et l'évaluation de l'ajustement des vêtements, devraient être intégrés dans le système de CAD de l'habillement afin d'améliorer l'efficacité du processus de conception.Sur la base de ces besoins, cette thèse propose un système de recommandation intelligent composé de modèles de vêtements ajustables pour conduire à la conception de vêtements personnalisés. Le système fonctionne en combinaison avec un nouveau processus de conception nouvellement développé, à savoir l'identification de la forme du corps humain - la recommandation d'une solution de conception - la représentation virtuelle 3D et l'évaluation - l'ajustement des paramètres de conception. Ce processus peut être répété jusqu'à ce que l'utilisateur soit satisfait. Le système de recommandation proposé a été validé par quelques cas pratiques de conception réussis
Commercial garment recommendation systems have been widely used in the apparel industry. However, existing research on digital garment design has focused on the technical development of the virtual design process, with little knowledge of traditional designers. The fit of a garment plays a significant role in whether a customer purchases that garment. In order to develop a well-fitting garment, designers and pattern makers should adjust the garment pattern several times until the customer is satisfied. Currently, there are three main disadvantages of traditional pattern-making: 1) it is very time-consuming and inefficient, 2) it relies too much on experienced designers, 3) the relationship between the human body shape and the garment is not fully explored. In practice, the designer plays a key role in a successful design process. There is a need to integrate the designer's knowledge and experience into current garment CAD systems to provide a feasible human-centered, low-cost design solution quickly for each personalized requirement. Also, data-based services such as recommendation systems, body shape classification, 3D body modelling, and garment fit assessment should be integrated into the apparel CAD system to improve the efficiency of the design process.Based on the above issues, in this thesis, a fit-oriented garment pattern intelligent recommendation system is proposed for supporting the design of personalized garment products. The system works in combination with a newly developed design process, i.e. body shape identification - design solution recommendation - 3D virtual presentation and evaluation - design parameter adjustment. This process can be repeated until the user is satisfied. The proposed recommendation system has been validated by some successful practical design cases

AbstractUrban mobility and the transport of people have been increasing in volume inexorably for decades. Despite the advantages and opportunities mobility has brought to our society, there are also severe drawbacks such as the transport sector’s role as one of the main contributors to greenhouse-gas emissions and traffic jams. In the future, an increasing number of people will be living in large urban settings, and therefore, these problems must be solved to assure livable environments. The rapid progress of information and communication, and geographic information technologies, has paved the way for urban informatics and smart cities, which allow for large-scale urban analytics as well as supporting people in their complex mobile decision making. This chapter demonstrates how geosmartness, a combination of novel spatial-data sources, computational methods, and geospatial technologies, provides opportunities for scientists to perform large-scale spatio-temporal analyses of mobility patterns as well as to investigate people’s mobile decision making. Mobility-pattern analysis is necessary for evaluating real-time situations and for making predictions regarding future states. These analyses can also help detect behavioral changes, such as the impact of people’s travel habits or novel travel options, possibly leading to more sustainable forms of transport. Mobile technologies provide novel ways of user support. Examples cover movement-data analysis within the context of multi-modal and energy-efficient mobility, as well as mobile decision-making support through gaze-based interaction.

Since regaining its independence in 1991, Croatia experienced major transformation of constitutional and political system in 2000, when illiberal semi-presidential rule was replaced with functional parliamentary democracy. These changes also established a new pattern of executive politics, with coalition governments as a norm. Furthermore, in the post-2000 period the prime ministerial government was established as the dominant governance model, with prime ministers taking over the leading role in coalition politics. Building on this notion, this chapter identifies several major features of coalition governance in Croatia: very general and rather brief coalition agreements without written rules on cabinet decision-making and on how to resolve internal conflicts; an informal and personalized way of handling conflicts between coalition parties; the dominant position of the prime minister and limited ministerial autonomy; and the policy and personnel conflicts between coalition parties as the main reason for cabinet termination. Thus, similarly to some other countries in Central Eastern Europe region, all three stages of coalition governance in Croatia are heavily dominated by top party leaders and particularly prime ministers, thus creating the patterns of informal and personalized coalition decision-making. The prime ministerial dominance is reflected in weak coalition arrangements, with very limited coordination established between coalition parties and the lack of broader conflict resolution mechanisms, which makes coalition cabinets especially fragile and unstable, particularly when challenged by the inclusion of new parties in government.

Democracy in Bulgaria came as a result of a pact that preserved the role of the communist successor party to a larger degree than in many other post-communist countries in Central Eastern Europe (CEE). That had an impact on the nature of coalition government as well as an important but rarely noted aspect of the legacy of the successor party, the Bulgarian Socialist party (BSP). Because of the dominant position of the BSP, its personalized and centralized vision and practice of governing have remained characteristic of the political system. Coalition government is the dominant pattern in Bulgarian democratic politics: of the eleven party-based cabinets since 1990, only four were single-party ones. There are several major features of coalition government in Bulgaria: one is that party-building and coalition-making have been deeply intertwined over the entire period. Further, coalitions have been based on the understanding that parties divide the power and accompanying privileges, rather than that they share responsibility for government. Thus, in contrast to some longer-standing democracies the coalition formation process rarely involves detailed agreements on policy, and has mostly revolved around personalities and portfolios. As a result, coalition agreements have been minimal and the periods of coalition bargaining have been relatively short. Conflict resolution mechanisms are centralized and personalized, and coalitions have broken down because of narrow party political interests rather than because of principled conflicts over public policy.

"This paper describes a new holistic motion control approach for personalized and efficient vehicle dynamics. The control system is part of Continental’s Holistic Driverless Technologies and contributes to the Program Autonomous Driving making cars intelligent and driving safer than ever before. The goals and use-case scenarios of the key functionalities, the logical architecture and the common interface structure leading to best reusability of existing system elements will be presented in the paper. An object oriented design pattern maps the overall system into parent and child systems along different chain of effects. The chains are either build for specific purposes or represent emergent properties like waste heat. Examples of parent/child system pairs are vehicle/chassis, chassis/corner or corner/actuator. Each system distinguishes internally between information providers (observer functions) and managers. Observer functions provide information for the own and the parent level and coordinate limits received from the child level. Manager functions determine requests to control the respective child system. The application of holistic motion control is demonstrated using the example of an electric vehicle with wheel individual electric motors at the front axle and friction brakes at each corner. Vehicle level longitudinal and lateral requests are determined by a dynamic feedforward control with personalizable vehicle responsiveness and damping characteristics. The central element on chassis level is a Model Predictive Controller (MPC) requesting corner module forces while accounting for limits of stability and energy. A twin track model is used to describe the dynamics of the chassis. Time variant system matrices are generated by linearization and discretization at every sampling instant. Tire-road friction circles constraining the admissible forces at the wheels are approximated by polygons. The purpose of these measures is to ensure the applicability of convex quadratic programming suitable for real time embedded optimization. Corner modules process the incoming stream of chassis requests and generate a stream of requests to the actuators. A corner module MPC framework at the front axle is able to optimally split the wheel braking torque among the redundant actuators, while providing anti-lock braking features by wheel slip regulation. This approach offers fast transient response, without compromising the energy recuperation efficiency of the electric motors taking different dynamic authorities of friction brake and electric motor into account. Continental is aware of its responsibilities in the transformation of mobility and energy efficiency and has actively taken up the challenges. In doing so, we are paving the way to deliver intelligent and sustainable mobility far into the future, while at the same time making a significant contribution to maintaining the attractiveness of personal mobility."