A evolução da contabilidade e os sistemas de informação contábil

TCC (Graduação) - Universidade Federal de Santa Catarina. Centro Socioeconômico. Curso de Ciências ContábeisO trabalho tem como um dos objetivos apresentar alguns aspectos da evolução da contabilidade para conhecer as principais fundamentações que alicerçam os procedimentos atuais da contabilidade, Para tanto, é feito um levantamento bibliográfico sobre a Evolução da Contabilidade, desde os primórdios da civilização, onde sistemas de controle primitivos de contagem de rebanhos já podiam ser considerados como contabilidade. Relatando períodos da História da Contabilidade, como o surgimento das partidas dobradas, em torno do século XV, e a formação de teorias empíricas aparecendo as primeiras obras cientificas, começando assim a Contabilidade a apresentar maior preocupação com as informações e com o patrimônio, chegando a atualidade onde a contabilidade busca suporte no avanço tecnológico.Assim torna-se importante relatar algumas das características dos sistemas de informação utilizados, e o seu papel nas organizações, principalmente de um Enterprise Resourse Planning – ERP, que é um sistema integrado de gestão, utilizado em muitas empresas.Então é demonstrado através de um estudo de caso feito em uma empresa do ramo de ferragens, como o sistema integrado utilizado para o controle de estoque e das operações da empresa facilita os trabalhos. Objetiva-se mostrar a importância da utilização de sistemas integrados, facilitando desta forma para a tomada de decisão da empresa e para a contabilidade levantar informações com maior agilidade e veracidade.As empresas estão investindo em sistemas cada vez mais avançados, o mercado é muito competitivo e para se manter, o caminho é o da compra de sistemas específicos e avançados, para a atividade da empresa.A contabilidade ganhou tempo para que os profissionais possam se especializar e prestar cada vez mais serviços em novas funções. Nesse sentido verifica-se a necessidade de sistemas avançados que permitam as empresas e aos profissionais de contabilidade seguir o crescimento proporcionado pela competitividade

Reducing the Capacity Loss of Lithium-Ion Batteries with Machine Learning in Real-Time—A Study Case

Lithium-ion batteries (LIBs) are the state-of-the-art technology for energy storage systems. LIBs can store energy for longer, with higher density and power capacity than other technologies. Despite that, they are sensitive to abuses and failures. If the battery management system (BMS) operates incorrectly or some anomalies appear, performance and security issues can be observed in LIBs. BMSs are also hard-programmed, have complex circuits, and have low computational resources, which limit the use of prognoses and diagnoses systems operating in real-time and embedded in the vehicle. Therefore, some technologies, such as edge and cloud computing, data-driven approaches, and machine learning (ML) models, can be applied to help the BMS manage the LIBs. Therefore, this work presents an edge–cloud computing system composed of two ML approaches (anomaly detection and failure classification) to identify the abuses in the LIBs in real-time. To validate the work, 36 NMC cells with a nominal capacity of 2200 mAh and voltage of 3.7 V were used to build the experiments segmented into three steps. Firstly, 12 experiments under failures were realized, which resulted in a high capacity loss. Then, the data were used to build both ML models. In the second step, the anomaly approach was applied to 12 cells observing the cells’ temperature anomalies. Then, the combination of IF and RF was applied to another 12 cells. The IF could reduce the capacity loss by about 45% when multiple abuses were applied to the cells. Despite that, this approach could not avoid some failures, such as overdischarging. Conversely, combining IF and RF could significantly reduce the capacity loss by 91% for the multiple abuses. The results concluded that ML could help the BMS identify failures in the first stage and reduce the capacity loss in LIBs

Reducing the Capacity Loss of Lithium-Ion Batteries with Machine Learning in Real-Time—A Study Case

Lithium-ion batteries (LIBs) are the state-of-the-art technology for energy storage systems. LIBs can store energy for longer, with higher density and power capacity than other technologies. Despite that, they are sensitive to abuses and failures. If the battery management system (BMS) operates incorrectly or some anomalies appear, performance and security issues can be observed in LIBs. BMSs are also hard-programmed, have complex circuits, and have low computational resources, which limit the use of prognoses and diagnoses systems operating in real-time and embedded in the vehicle. Therefore, some technologies, such as edge and cloud computing, data-driven approaches, and machine learning (ML) models, can be applied to help the BMS manage the LIBs. Therefore, this work presents an edge–cloud computing system composed of two ML approaches (anomaly detection and failure classification) to identify the abuses in the LIBs in real-time. To validate the work, 36 NMC cells with a nominal capacity of 2200 mAh and voltage of 3.7 V were used to build the experiments segmented into three steps. Firstly, 12 experiments under failures were realized, which resulted in a high capacity loss. Then, the data were used to build both ML models. In the second step, the anomaly approach was applied to 12 cells observing the cells’ temperature anomalies. Then, the combination of IF and RF was applied to another 12 cells. The IF could reduce the capacity loss by about 45% when multiple abuses were applied to the cells. Despite that, this approach could not avoid some failures, such as overdischarging. Conversely, combining IF and RF could significantly reduce the capacity loss by 91% for the multiple abuses. The results concluded that ML could help the BMS identify failures in the first stage and reduce the capacity loss in LIBs