Cette thèse se situe dans un axe novateur de recherches en ingénierie et en management des systèmes d’information, elle articule à la fois les aspects de quatre domaines de recherche issus de l’Informatique, des Sciences des Systèmes d’information et des Sciences Humaines et des aspects pratiques liées aux entreprises du Web 2.0. Le «Crowdsourcing», comme son nom l’indique, désigne l’approvisionnement par la foule; Les études et les recherches sur cette thèse se font rares mais celles qui existent confirment l’intérêt managérial des plateformes de Crowdsourcing, grâce à leur rôle incontestable dans la création de valeur. Néanmoins, la foule est composée de groupe d’amateurs hétérogènes, c’est pour cela qu’elle représente aussi une source d’incompétence. Dans ce cadre, notre hypothèse opérationnelle pose que l’apprentissage de la foule stimule la création de valeur dans les plateformes Crowdsourcing. Ainsi, notre travail est, principalement organisé autour de la conception et l’élaboration d’un outil pour l’apprentissage de la foule au sein des plateformes de Crowdsourcing. Ce travail est de nature complexe et relève à la fois d’un travail de recherche et d’une pratique d’ingénierie. C’est pour cela que nous optons pour une démarche constructiviste exploratoire de type qualitative moyennant la méthode de recherche ingénierique qui vise à définir et à concevoir une approche d’apprentissage adaptée aux plateformes de Crowdsourcing et à l’implémenter par la suite au sein d’une plateforme Crowdsourcing de test spécialisée dans les Backlinks. Des expérimentations basées sur des entretiens semi-directifs viendront, à la fin de ce travail, confirmer ou infirmer nos hypothèses. / This thesis is situated in an innovative line of research in engineering and management information systems, it articulates both the aspects of four disciplines of research in the Computer Science, Information Systems, Human Sciences and practical aspects related to Web 2.0 companies. The "Crowdsourcing" as its name suggests, refers to the sourcing by the crowd, studies and research on this topic are infrequent but those that exist confirm the managerial interest of Crowdsourcing platforms, thanks to their undeniable role in value creation. Nevertheless, the crowd is composed of heterogeneous group of amateurs that is why it is also a source of incompetence. Our operating hypothesis posits that learning the crowd stimulates the creation of value in the Crowdsourcing platforms. Thus, our work is mainly organized around the design and development of a tool for learning the crowd in Crowdsourcing platforms. This work is complex and involves both a research work and practical engineering. That is why we choose an exploratory qualitative constructivist approach and an ingénierique research method to define and develop a suitable approach of learning adapted to the Crowdsourcing platforms and implement it thereafter within our test Crowdsourcing platform specializes in Backlinking. Experiments based on semi-structured interviews will, confirm or deny our hypotheses.

Erfahrungsmanagement (EM) ist eine Spezialform des Wissensmanagements, die sich mit aufgabenbezogenem Wissen beschäftigt. Diese Arbeit entwickelt ein Rahmenwerk für Assistenzsysteme, die Menschen bei EM-Aufgaben unterstützen. Es untersucht nicht nur technische Fragen (Erfahrungswissen sammeln, strukturieren, speichern und wiederverwenden) sondern auch organisatorische (Erfahrungswissen evaluieren und pflegen) und psychosoziale Aspekte (ein EM-System integrieren, Barrieren vermeiden, den Systemeinsatz bewerten). Fallbasierte Anwendungsbeispiele für industrielle und experimentelle Szenarien zeigen, welche Prozesse wo unterstützt oder gar teilautomatisiert werden können. Sie dienen der experimentellen Evaluierug der Fragen, die ich zu Beginn jedes Anwendungskapitels formuliert habe. / Experience Management (EM) is a special form of Knowledge Management that deals with task-based knowledge. This thesis provides a framework for assistant systems that support human beings in EM tasks. It deals not only with technical issues (how to collect, structure, store, retrieve, and reuse experiential knowledge), but als with organizational issues (how to evaluate and maintain it) and psychosocial questions (how to integrate an EM system, how to avoid barriers, how to evaluate the success of the whole system). Case-based sample applications from both, industrial and experimental scenarios, show to what extend the particular EM processes can be supported or which sub-processes can even be automated. By means of experiments with these implemented samples, we evaluate the topics that are discussed at the beginning of each application chapter.

Les systèmes automatisés de production (SAP) représentent une classe importante des systèmes industriels qui sont de plus en plus complexes vue le grand nombre d’interaction et d’interconnexion entre leurs différents composants. En conséquence, ils sont plus sensibles aux dysfonctionnements dont les conséquences peuvent être importantes en termes de productivité, de sécurité et de qualité de production. Un défi majeur est alors de développer une approche intelligente qui peut être utilisée pour le diagnostic de ces systèmes afin de garantir leurs suretés de fonctionnement. Dans le cadre de cette thèse, nous nous intéressons seulement au diagnostic des SAP ayant une dynamique discrète. Nous présentons dans le premier chapitre ces systèmes, les dysfonctionnements possibles et la terminologie du diagnostic utilisée. Ensuite, nous présentons un état de l’art de différentes méthodes et approches existantes et aussi une synthèse de ces méthodes. Cette synthèse nous a motivé de choisir une approche à base de donnée qui s’appuie sur une technique d’apprentissage automatique, qui est le raisonnement à partir de cas (RàPC). Pour cela, nous avons présenté dans le deuxième chapitre un état de l’art sur l’apprentissage automatique et ses différentes méthodes en mettant l’accent essentiellement sur le RàPC et ses utilisations pour le diagnostic des systèmes industriels. Cette étude nous a permis de proposer dans le chapitre 3 une approche d’aide au diagnostic qui se base sur le RàPC. Cette approche s’appuie sur une phase hors ligne et une phase en ligne. La phase hors ligne permet de définir un format de représentation de cas et de construire une base de cas normaux (BCN) et une base de cas défaillants (BCD) à partir d’une base de données d’historique. La phase en ligne permet d’aider les opérateurs humains de surveillance à la prise de la décision du diagnostic la plus adéquate. Les résultats des expérimentations sur un système de tri de caisses ont présentés les piliers de cette approche qui résident au niveau du format de représentation de cas proposé et au niveau de la base de cas utilisé. Pour résoudre ces problèmes et améliorer les résultats, un nouveau format de représentation de cas est proposé dans le chapitre 4. Selon ce format et à partir des données issues du système simulé après son émulation en mode normal et fautif, les cas de la base de cas initiale sont construits. Ensuite, une phase de raisonnement et d’apprentissage incrémental est présentée. Cette phase permet non seulement le diagnostic du système surveillé mais aussi d’enrichir la base de cas suite à l’apparition des nouveaux comportements inconnus. Les expérimentations présentées dans le chapitre 5 sur « le plateau tournant » qui est un sous système du système « tri de caisses » ont permis de montrer l’amélioration des résultats et aussi d’évaluer et de comparer les performances de l’approche proposée vis-à-vis certaines approches d’apprentissage automatique et vis-à-vis une approche à base de modèle pour le diagnostic du plateau tournant. / Automated production systems (APS) represents an important class of industrial systems that are increasingly complex given the large number of interactions and interconnections between their different components. As a result, they are more susceptible to malfunctions, whose consequences can be significant in terms of productivity, safety and quality of production. A major challenge is to develop an intelligent approach that can be used to diagnose these systems to ensure their operational safety. In this thesis, we are only interested in the diagnosis of APS with discrete dynamics. We present in the first chapter these systems, the possible malfunctions and the used terminology for the diagnosis. Then, we present a state of the art of the existing methods for the diagnosis of this class of systems and also a synthesis of these methods. This synthesis motivated us to choose a data-based approach that relies on a machine learning technique, which is Case-Based Reasoning (CBR). For this reason, we presented in the second chapter a state of the art on machine learning and its different methods with a focus mainly on the CBR and its uses for the diagnosis of industrial systems. This study allowed us to propose in Chapter 3 a Case Based Decision Support System for the diagnosis of APS. This system is based on an online block and an offline block. The Offline block is used to define a case representation format and to build a Normal Case Base (NCB) and a Faulty Case Base (FCB) from a historical database. The online block helps human operators of monitoring to make the most appropriate diagnosis decision. The experiments results perform on a sorting system presented the pillars of this approach, which reside in the proposed case representation format and in the used case base. To solve these problems and improve the results, a new case representation format is proposed in chapter 4. According to this format and from the data acquired from the simulated system after its emulation in normal and faulty mode, cases of the initial case base are build. Then, a reasoning and incremental learning phase is presented. This phase allows the system diagnosis and the enrichment of the case base following the appearance of new unknown behaviors. The experiments presented in Chapter 5 and perform on the 'turntable' which is a subsystem of the 'sorting system” allowed to show the improvement of the results and also to evaluate and compare the performances of the proposed approach with some automatic learning approaches and with a model-based approach to turntable diagnosis.

Mesmo com a evolução tecnológica em vários setores, a área de enfermagem tem tido investimentos escassos em pesquisa e desenvolvimento capazes de atender suas expectativas, principalmente no campo da inteligência artificial. As expectativas dos enfermeiros convergem à melhora de seus processos clínicos que resultará em uma maior aproximação de seus pacientes. Além disso, há dificuldade em reunir diagnósticos de enfermagem nos hospitais, onde diversos registros clínicos e procedimentos preenchidos manualmente e armazenados ainda em folhas de papel. Esta condição compromete a legibilidade dos documentos envolvidos nos processos hospitalares, e seu arquivamento torna o processo de levantamento de informações moroso, o que acaba por inviabilizar a pesquisa à qual poderia resultar em informações importantes para melhora do processo de tomada de decisões. O objetivo desta dissertação foi trazer o estado da arte em inteligência artificial focado em raciocínio baseado em casos e sua aplicação na sistematização da assistência de enfermagem. No sentido de validar o modelo levantado foi criado um protótipo para apresentar uma aplicação que pudesse auxiliar os enfermeiros em seus processos clínicos, armazenando suas experiências em uma base de casos para futuras pesquisas. O protótipo consistiu em digitalizar diagnósticos de enfermagem pediátrica, e inserção em uma base de casos, com o intuito de avaliar a eficácia do protótipo na manipulação destes casos, em uma estrutura propicia para recuperação, adaptação, indexação e comparação de casos. Esta dissertação apresenta como resultado uma ferramenta computacional para a área da saúde, empregando uma das técnicas de inteligência artificial, Raciocínio Baseados em Casos. Os resultados foram satisfatórios devido ao alto índice de aprovação nos quesitos confiabilidade, funcionalidade, usabilidade e eficiência conforme as normas ISO/ABNT de qualidade em software. / Even with the development of technology in many industries, the nursing sector has had low investment in research and development, mainly in the field of artificial intelligence. The expectations of nurses converge to improvement over his clinical procedures that will result in a closer relationship with their patients. Moreover, there is difficulty in finding nursing diagnoses in hospitals, while clinical records and procedures are completed manually and stored even on paper. This condition compromises the readability of documents involved in the admissions process, and archiving that also becomes time-consuming process of information gathering, which derail research that could result in important information for improved decision-making. The objective of this dissertation was to bring the state of the art regarding artificial intelligence focusing on Case-Based Reasoning and its application in the systematization of nursing care. In order to validate the model a prototype was set up to demonstrate an application that would assist nurses in their clinical files, storing their experiences in a case base for future research. The prototype was to scan diagnosis pediatric nursing, and insertion into a case base in order to evaluate the effectiveness of the prototype in handling those cases. It also provides a framework for recovery, adaptation, indexing, and comparison of cases. This dissertation presents results in a computational tool for health employing one of the techniques of artificial intelligence: Case-Based Reasoning. The results were satisfactory due to high rate in terms of structure reliability, functionality, usability and efficiency according to ISO / ABNT quality in software.

Mesmo com a evolução tecnológica em vários setores, a área de enfermagem tem tido investimentos escassos em pesquisa e desenvolvimento capazes de atender suas expectativas, principalmente no campo da inteligência artificial. As expectativas dos enfermeiros convergem à melhora de seus processos clínicos que resultará em uma maior aproximação de seus pacientes. Além disso, há dificuldade em reunir diagnósticos de enfermagem nos hospitais, onde diversos registros clínicos e procedimentos preenchidos manualmente e armazenados ainda em folhas de papel. Esta condição compromete a legibilidade dos documentos envolvidos nos processos hospitalares, e seu arquivamento torna o processo de levantamento de informações moroso, o que acaba por inviabilizar a pesquisa à qual poderia resultar em informações importantes para melhora do processo de tomada de decisões. O objetivo desta dissertação foi trazer o estado da arte em inteligência artificial focado em raciocínio baseado em casos e sua aplicação na sistematização da assistência de enfermagem. No sentido de validar o modelo levantado foi criado um protótipo para apresentar uma aplicação que pudesse auxiliar os enfermeiros em seus processos clínicos, armazenando suas experiências em uma base de casos para futuras pesquisas. O protótipo consistiu em digitalizar diagnósticos de enfermagem pediátrica, e inserção em uma base de casos, com o intuito de avaliar a eficácia do protótipo na manipulação destes casos, em uma estrutura propicia para recuperação, adaptação, indexação e comparação de casos. Esta dissertação apresenta como resultado uma ferramenta computacional para a área da saúde, empregando uma das técnicas de inteligência artificial, Raciocínio Baseados em Casos. Os resultados foram satisfatórios devido ao alto índice de aprovação nos quesitos confiabilidade, funcionalidade, usabilidade e eficiência conforme as normas ISO/ABNT de qualidade em software. / Even with the development of technology in many industries, the nursing sector has had low investment in research and development, mainly in the field of artificial intelligence. The expectations of nurses converge to improvement over his clinical procedures that will result in a closer relationship with their patients. Moreover, there is difficulty in finding nursing diagnoses in hospitals, while clinical records and procedures are completed manually and stored even on paper. This condition compromises the readability of documents involved in the admissions process, and archiving that also becomes time-consuming process of information gathering, which derail research that could result in important information for improved decision-making. The objective of this dissertation was to bring the state of the art regarding artificial intelligence focusing on Case-Based Reasoning and its application in the systematization of nursing care. In order to validate the model a prototype was set up to demonstrate an application that would assist nurses in their clinical files, storing their experiences in a case base for future research. The prototype was to scan diagnosis pediatric nursing, and insertion into a case base in order to evaluate the effectiveness of the prototype in handling those cases. It also provides a framework for recovery, adaptation, indexing, and comparison of cases. This dissertation presents results in a computational tool for health employing one of the techniques of artificial intelligence: Case-Based Reasoning. The results were satisfactory due to high rate in terms of structure reliability, functionality, usability and efficiency according to ISO / ABNT quality in software.

Made available in DSpace on 2016-04-29T14:23:07Z (GMT). No. of bitstreams: 1 Allex Motta Melo da Rocha.pdf: 4303012 bytes, checksum: 94163da5edc7a5c0777f5f42582edc52 (MD5) Previous issue date: 2012-05-24 / This current dissertation aims to use the techniques of Case-Based Reasoning (CBR) and Geographic Information System (GIS) together to identify and characterize the impact of a contagious injury in the city of Belém, Pará state, Brazil, caused by the bacterium Leptospira interrogans, known as leptospirosis. Environmental and health issues, particularly as the presence of rodent droppings, urine and feces, influencing an establishment of this disease. This research conducts a study of the use of CBR technique in order to formulate and develop a model to identify and assist in the diagnosis of the disease, using data collected from patients, such as epidemiological history and clinical, in the area, in the years 2008, 2009 and 2010. The CBR system will enable health professionals can use it as a support tool in the diagnosis of cases of the disease. This dissertation also applies GIS techniques to visually express and produce environmental analysis, temporal and socioeconomic of leptospirosis epidemiology scenario in a neighborhood in the city of Belém, in the years mentioned above, due to yours epidemiological importance. So, there will be the environmental and socioeconomic characteristics, in order to analyze the spatial distribution of the main reservoirs of this disease and to identify the habits of life of people living or working in these places, considering the environmental factor to provide subsidies to adopt preventive measures to control of disease incidence / A presente dissertação tem como objetivo utilizar as técnicas de Raciocínio Baseado em Casos (RBC) e Geoprocessamento, em conjunto, para identificar e caracterizar a incidência de um agravo infectocontagioso, na cidade de Belém, estado do Pará, Brasil, ocasionado pela bactéria Leptospira interrogans, conhecido como leptospirose humana. Questões ambientais e sanitárias, como a presença, principalmente, de excrementos de roedores, como urina e fezes influenciam o estabelecimento deste agravo. Esta pesquisa realiza um estudo do uso da técnica de RBC, com o objetivo de formular e desenvolver um modelo para identificar e auxiliar no diagnóstico da doença, por meio de dados coletados dos pacientes, tais como antecedentes epidemiológicos e clínicos, ocorridos na região, nos anos de 2008, 2009 e 2010. O sistema de RBC possibilitará que profissionais da saúde possam utilizá-lo como ferramenta de apoio na diagnose de casos da doença. Esta dissertação também aplica técnicas de Geoprocessamento para expressar visualmente e produzir análises ambientais, temporais e socioeconômicas do cenário epidemiológico da leptospirose em um bairro na cidade de Belém, nos anos citados anteriormente, devido à importância epidemiológica do mesmo. Para tal, será realizada a caracterização ambiental e socioeconômica, a fim de analisar a distribuição espacial dos principais reservatórios deste agravo e identificar os hábitos de vida das populações que moram ou trabalham nestes locais, considerando o fator ambiental para fornecer subsídios à adoção de medidas preventivas para o controle da incidência desta doença

Book Summary: Applying Techniques to Common Encounters in School Counseling: A Case-Based Approach helps counselors in training bridge the gap between theory and practice by showing them how to theoretically frame or understand the problems and issues they encounter, how to proceed, and what action steps to take when they enter the field as school counselors. It answers the questions new counselors have in real school settings, such as What is it really like to live the life of a professional school counselor? How does the theory presented in the classroom apply to the myriad of situations encountered in the real life, everyday school setting? Case studies and scenarios give readers examples of many commonly encountered presenting issues. For each scenario the case is introduced, background information is supplied, and initial processing questions are posed. The authors include a discussion of the theoretical models or frameworks used to address the issue, along with a table segmented by theoretical paradigm and grade level that includes other techniques that could be used in the presenting case. With these tools at their disposal, readers gain a firm understanding of the issues from several frames of reference, along with interventions meant to create movement toward a successful resolution.

With the rise of Deep Learning the field of Artificial Intelligence (AI) Research has entered a new era. Together with an increasing amount of data and vastly improved computing capabilities, Machine Learning builds the backbone of AI, providing many of the tools and algorithms that drive development and applications. While we have already achieved many successes in the fields of image recognition, language processing, recommendation engines, robotics, or autonomous systems, most progress was achieved when the algorithms were focused on learning only a single task with little regard to effort and reusability. Since learning a new task from scratch often involves an expensive learning process, in this work, we are considering the use of previously acquired knowledge to speed up the learning of a new task. For that, we investigated the application of Transfer Learning methods for Deep Reinforcement Learning (DRL) agents and propose a novel framework for knowledge preservation and reuse. We show, that the knowledge transfer can make a big difference if the source knowledge is chosen carefully in a systematic approach. To get to this point, we provide an overview of existing literature of methods that realize knowledge transfer for DRL, a field which has been starting to appear frequently in the relevant literature only in the last two years. We then formulate the Case-based Reasoning methodology, which describes a framework for knowledge reuse in general terms, in Reinforcement Learning terminology to facilitate the adaption and communication between the respective communities. Building on this framework, we propose Deep Case-based Policy Inference (DECAF) and demonstrate in an experimental evaluation the usefulness of our approach for sequential task learning with knowledge preservation and reuse. Our results highlight the benefits of knowledge transfer while also making aware of the challenges that come with it. We consider the work in this area as an important step towards more stable general learning agents that are capable of dealing with the most complex tasks, which would be a key achievement towards Artificial General Intelligence. / Com a evolução da Aprendizagem Profunda (Deep Learning), o campo da Inteligência Artificial (IA) entrou em uma nova era. Juntamente com uma quantidade crescente de dados e recursos computacionais cada vez mais aprimorados, o Aprendizado de Máquina estabelece a base para a IA moderna, fornecendo muitas das ferramentas e algoritmos que impulsionam seu desenvolvimento e aplicações. Apesar dos muitos sucessos nas áreas de reconhecimento de imagem, processamento de linguagem natural, sistemas de recomendação, robótica e sistemas autônomos, a maioria dos avanços foram feitos focando no aprendizado de apenas uma única tarefa, sem muita atenção aos esforços dispendidos e reusabilidade da solução. Como o aprendizado de uma nova tarefa geralmente envolve um processo de aprendizado despendioso, neste trabalho, estamos considerando o reúso de conhecimento para acelerar o aprendizado de uma nova tarefa. Para tanto, investigamos a aplicação dos métodos de Transferência de Aprendizado (Transfer Learning) para agentes de Aprendizado por Reforço profundo (Deep Reinforcement Learning - DRL) e propomos um novo arcabouço para preservação e reutilização de conhecimento. Mostramos que a transferência de conhecimento pode fazer uma grande diferença no aprendizado se a origem do conhecimento for escolhida cuidadosa e sistematicamente. Para chegar a este ponto, nós fornecemos uma visão geral da literatura existente de métodos que realizam a transferência de conhecimento para DRL, um campo que tem despontado com frequência na literatura relevante apenas nos últimos dois anos. Em seguida, formulamos a metodologia Raciocínio baseado em Casos (Case-based Reasoning), que descreve uma estrutura para reutilização do conhecimento em termos gerais, na terminologia de Aprendizado por Reforço, para facilitar a adaptação e a comunicação entre as respectivas comunidades. Com base nessa metodologia, propomos Deep Casebased Policy Inference (DECAF) e demonstramos, em uma avaliação experimental, a utilidade de nossa proposta para a aprendizagem sequencial de tarefas, com preservação e reutilização do conhecimento. Nossos resultados destacam os benefícios da transferência de conhecimento e, ao mesmo tempo, conscientizam os desafios que a acompanham. Consideramos o trabalho nesta área como um passo importante para agentes de aprendizagem mais estáveis, capazes de lidar com as tarefas mais complexas, o que seria um passo fundamental para a Inteligência Geral Artificial.

<p>A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often make their diagnosis and decision based on manual inspection of physiological signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. Today the diagnosis and decision is largely dependent on how experienced the clinician is interpreting the measurements.  A computer-aided decision support system for diagnosis and treatment of stress would enable a more objective and consistent diagnosis and decisions.</p><p>A challenge in the field of medicine is the accuracy of the system, it is essential that the clinician is able to judge the accuracy of the suggested solutions. Case-based reasoning systems for medical applications are increasingly multi-purpose and multi-modal, using a variety of different methods and techniques to meet the challenges of the medical domain. This research work covers the development of an intelligent clinical decision support system for diagnosis, classification and treatment in stress management. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques have been investigated to enable a more reliable and efficient diagnosis and treatment of stress such as case-based reasoning, textual information retrieval, rule-based reasoning, and fuzzy logic. Functionalities and the performance of the system have been validated by implementing a research prototype based on close collaboration with an expert in stress. The case base of the implemented system has been initiated with 53 reference cases classified by an experienced clinician. A case study also shows that the system provides results close to a human expert. The experimental results suggest that such a system is valuable both for less experienced clinicians and for experts where the system may function as a second option.</p> / IPOS, PROEK

Visual media are of great importance to designers. Understanding a new design, for example, often means understanding a drawing. From the perspective of artificial intelligence, this implies that automated knowledge acquisition in computer-aided design can productively occur using drawings as a knowledge source. However, this requires machines that are able to interpret design drawings. I view the task of interpreting drawings as one of constructing a teleological model of the design depicted in the drawings, where the model enables causal and functional inferences about the depicted design. I have developed a novel analogical method for constructing a teleological model of a mechanical device from an unlabelled 2D line drawing. The source case is organized in a Drawing Shape Structure Behavior Function (DSSBF) abstraction hierarchy. This knowledge organization enables the analogical mapping and transfer to occur at multiple levels of abstraction. Given a target drawing and a relevant source case, my method of compositional analogy first constructs a graphical representation of the lines and the intersections in the target drawing, then uses the mappings at the level of line intersections to transfer the shape representations from the source case to the target. It next uses the mappings at the level of shapes to transfer the structural model of the device from the source to the target. Finally, the mappings from the source to the target structural model enable the transfer of behaviors and the functional specification from source to target, completing the analogy and yielding a complete DSSBF model of the input drawing. The Archytas system implements this method of compositional analogy and evaluates it in the domain of kinematic devices such as piston and crankshaft devices, door latches, and pulley systems.