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"Timo Carl presents alternatives to curtain wall facades and other flat boundaries creating autonomous spaces. He investigates facade typologies with multiple material layers to strategize the relationship between buildings and their environment. By revisiting Le Corbusier´s seminal brise soleil an alternative reading of the modern project emerges: one that is not based on classical compositional rules, but instead on the dynamic relationships with environmental forces. Finally, an exciting series of project-based investigations sets out innovative ways in which novel deep skins combine energy-conscious performance with the poetics of architecture."

"This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme.There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results."

"Dalam dunia industri, untuk mengetahui performa mesin motor dapat dilakukan diagnosa menggunakan machinery analyzer. Machinery analyzer yang dibahas pada penelitian ini yaitu Haliza. Terdapat permasalahan dalam melakukan diagnosa mesin motor menggunakan Haliza yaitu penggunaan kabel komunikasi antara sensor kecepatan dan Haliza, yang mengurangi fleksibilitas saat proses diagnosa dilakukan dan waktu pemasangan yang cukup lama. Oleh karena itu, pada penelitian ini akan dilakukan rancang bangun interface untuk modul komunikasi wireless yang akan dipasang pada sensor kecepatan dan Haliza. Rancang bangun interface di kembangkan dengan menggunakan mikrokontroler ATmega16A, sebagai kontroler pada modul wireless RF CC2500. Telah dilakukan pengujian hardware dan software dari modul komunikasi wireless. Dari hasil uji komunikasi diperoleh jangkauan jarak maksimum tanpa BER (Bit Error Rate) sejauh 16 meter pada kecepatan putaran motor 1800 rpm dengan nilai RSSI -79 dBm. Kecepatan putaran motor maksimum yang dapat terukur yaitu 2100 rpm, dengan tingkat kesalahan 0.14% dibandingkan dengan hasil pengukuran tachometer. Untuk uji kehandalan komunikasi wireless, didapatkan tingkat kesalahan rata-rata sebesar 0.09% pada pengujian jarak 10 meter dengan kecepatan 2100 rpm selama 5 jam pengujian.

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In the industrial, to know the performance of the machine can be diagnosed using machinery analyzer. Machinery analyzer are discussed in this research that Haliza. There are problems in diagnosing the machine using Haliza namely the use of the communication cable between the speed sensor and Haliza, which reduces the flexibility when the diagnosis is made and the installation of a long time. Therefore, in this report will be conducted design interface for a wireless communication module that will be installed on the speed sensor and Haliza. The design of the interface is developed by using microcontroller ATmega16A, as a controller in the wireless module RF CC2500. The hardware and software of the wireless communication module have been tested. Communication test results obtained maximum distances without the BER (Bit Error Rate) as far as 16 meters at a motor rotating speed of 1800 rpm with RSSI value of -79 dBm. The maximum rotation speed of the motor which can be measured at 2100 rpm, with an error rate of 0.14% compared with the measurement results tachometer. The reliability test of wireless communication, obtained average error rate of 0.09% at the testing distance of 10 meters at a speed of 2100 rpm for 5 hours of testing."

"Pengendalian ketinggian atau biasa disebut Level Controller adalah hal yang penting di berbagai bidang industri, termasuk industri kimia, industri minyak bumi, industri pupuk, industri otomatif dan lain-lainnya. Pada penelitian ini, dirancang sebuah pengendali non-konvesional menggunakan Reinforcement Learning dengan Twin Delayed Deep Deterministic Polic Gradient (TD3). Agent ini diterapkan pada sebuah miniature plant yang berisi air sebagai fluidanya. Miniature plant ini disusun dengan berbagai komponen yaitu flow transmitter, level transmitter, ball-valve, control valve, PLC, dan pompa air. Kontroler agent TD3 dirancang menggunakan SIMULINK Matlab di computer. Data laju aliran dan ketinggian air diambil melalui flow transmitter dan level transmitter yang dikoneksikan dengan OPC sebagai penghubung antara Matlab ke SIMULINK. Penerapan agent TD3 pada sistem pengendalian ketinggian air digunakan pada dua kondisi yaitu secara riil plant dan simulasi. Dari penelitian ini didapatkan, bahwa kontroler agent TD3 dapat mengendalikan sistem dengan baik. overshoot yang didapatkan kecil yaitu 0,57 secara simulasi dan 0,97 secara riil plant.

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In this study, the level controller is the most important in many industry fields, such as chemical industry, petroleum industry, automotive industry, etc., a non-conventional controller using Reinforcement Learning with Twin Delayed Deep Deterministic Policy Gradient (TD3) agent was designed. This agent was implemented in water contain the miniature plant. This miniature plant consists of many components: flow transmitter, level transmitter, ball-valve, control valve, PLC, and water pump. Agent controller was designed using SIMULINK Matlab on a computer, which obtained flow rate and height information comes from flow transmitter and level transmitter connected to OPC that link between Matlab to SIMULINK. Implementation of TD3 to control water level system used two conditions, in real plant and simulation. In this study, we obtain that the TD3 agent controller can control the designs with a slight overshoot value, namely 0,57 in the simulation and 0,97 in the real plant."

"Pengendalian proses di industri desalinasi sangat penting untuk mengoptimalkan operasi dan mengurangi biaya produksi. Pengendali proporsional, integral, dan derivatif (PID) umum digunakan, namun tidak selalu efektif untuk sistem coupled-tank yang kompleks dan nonlinier. Penelitian ini mengeksplorasi penggunaan algoritma reinforcement learning (RL) dengan algoritma Deep Deterministic Policy Gradient (DDPG) untuk mengendalikan ketinggian air pada sistem coupled-tank. Tujuan penelitian ini adalah merancang sistem pengendalian ketinggian air menggunakan RL berbasis programmable logic controller (PLC) untuk mencapai kinerja optimal. Sistem diuji pada model coupled-tank dengan dua tangki terhubung vertikal, di mana aliran air diatur untuk menjaga ketinggian air dalam rentang yang diinginkan. Hasil menunjukkan bahwa pengendalian menggunakan RL berhasil dengan tingkat error steady-state (SSE) antara 4,63% hingga 9,6%. Kinerja RL lebih baik dibandingkan PID, dengan rise time dan settling time yang lebih singkat. Penelitian ini menyimpulkan bahwa RL adalah alternatif yang lebih adaptif untuk pengendalian level cairan di industri dibandingkan dengan metode konvensional.

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Process control in the desalination industry is crucial for optimizing operations and reducing production costs. Proportional, integral, and derivative (PID) controllers are commonly used but are not always effective for complex and nonlinear coupled-tank systems. This study explores the use of reinforcement learning (RL) with the Deep Deterministic Policy Gradient (DDPG) algorithm to control the water level in a coupled-tank system. The objective of this research is to design a water level control system using RL based on a programmable logic controller (PLC) to achieve optimal performance. The system was tested on a coupled-tank model with two vertically connected tanks, where the water flow is regulated to maintain the water level within the desired range. Results show that control using RL achieved a steady-state error (SSE) between 4.63% and 9.6%. RL performance was superior to PID, with faster rise and settling times. This study concludes that RL is a more adaptive alternative for liquid level control in industrial settings compared to conventional methods."

"Sistem catu daya listrik nirkabel dirancang bukan untuk menggantikan seluruh kabel tetapi untuk meningkatkan kehandalan dan kenyamanan pengguna peralatan. Desain rangkaian Transmitter pada sistem tersebut pada dasarnya adalah rangkaian variable comparator oscillator yang terdiri dari Voltage comparator yang berfungsi sebagai pengatur frequensi agar dapat bekerja pada bermacam-macam frekuensi dan rangkaian LC Mosfet digunakan untuk Mengubah Arus searah menjadi Arus Bolak-Balik agar bisa menghasilkan induksi elektromagnetik pada Antena yg merupakan beban induktif.

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Wireless Power transfer system is designed not to replace the whole cable but to improve equipment reliability and user convience. Circuit design on this system basicly is Variable Comparator circuit which consist of voltage comparator which act as adjustment frequency circuit to make This transmitter easier when have to work with different frequency and LC Mosfet that is used to convert from direct current to become alternating current that will produce electromagnetic induction to antenna."

"Sistem coupled tank merupakan salah contoh penerapan sistem kontrol level industri yang memiliki karakteristik yang kompleks dengan non linieritas yang tinggi. Pemilihan metode pengendalian yang tepat perlu dilakukan untuk dapat diterapkan dalam sistem coupled tank agar dapat memberikan kinerja dengan presisi tinggi. Sejak awal kemunculannya, Reinforcement Learning (RL) telah menarik minat dan perhatian yang besar dari para peneliti dalam beberapa tahun terakhir. Akan tetapi teknologi ini masih belum banyak diterapkan secara praktis dalam kontrol proses industri. Pada penelitian ini, akan dibuat sebuah sistem pengendalian level pada sistem coupled tank dengan menggunakan Reinforcement Learning dengan menggunakan algoritma Twin Delayed Deep Deterministic Policy Gradient (TD3). Reinforcement Learning memiliki fungsi reward yang dirancang dengan sempurna yang diperlukan untuk proses training agent dan fungsi reward tersebut perlu diuji terlebih dahulu melalui trial and error. Performa hasil pengendalian ketinggian air pada sistem coupled tank dengan algoritma TD3 mampu menghasilkan pengendalian yang memiliki keunggulan pada rise time, settling time, dan peak time yang cepat serta nilai steady state eror sangat kecil dan mendekati 0%.

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The coupled tank system is an example of the application of an industrial level control system that has complex characteristics with high non-linearity. It is necessary to select an appropriate control method to be applied in coupled tank systems in order to provide high-precision performance. Since its inception, Reinforcement Learning (RL) has attracted great interest and attention from researchers in recent years. However, this technology is still not widely applied practically in industrial process control. In this research, a level control system in a coupled tank system will be made using Reinforcement Learning using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Reinforcement Learning has a perfectly designed reward function that is required for the agent training process and the reward function needs to be tested first through trial and error. The performance of the results of controlling the water level in the coupled tank system with the TD3 algorithm is able to produce controls that have advantages in rise time, settling time, and peak time which are fast and the steady state error value is very small and close to 0%."

"Sistem coupled tank merupakan salah contoh penerapan sistem kontrol level industri yang memiliki karakteristik yang kompleks dengan non linieritas yang tinggi. Pemilihan metode pengendalian yang tepat perlu dilakukan untuk dapat diterapkan dalam sistem coupled tank agar dapat memberikan kinerja dengan presisi tinggi. Sejak awal kemunculannya, Reinforcement Learning (RL) telah menarik minat dan perhatian yang besar dari para peneliti dalam beberapa tahun terakhir. Akan tetapi teknologi ini masih belum banyak diterapkan secara praktis dalam kontrol proses industri. Pada penelitian ini, akan dibuat sebuah sistem pengendalian level pada sistem coupled tank dengan menggunakan Reinforcement Learning dengan menggunakan algoritma Twin Delayed Deep Deterministic Policy Gradient (TD3). Reinforcement Learning memiliki fungsi reward yang dirancang dengan sempurna yang diperlukan untuk proses training agent dan fungsi reward tersebut perlu diuji terlebih dahulu melalui trial and error. Performa hasil pengendalian ketinggian air pada sistem coupled tank dengan algoritma TD3 mampu menghasilkan pengendalian yang memiliki keunggulan pada rise time, settling time, dan peak time yang cepat serta nilai steady state eror sama dengan 0%.

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The coupled tank system is an example of the application of an industrial level control system that has complex characteristics with high non-linearity. It is necessary to select an appropriate control method to be applied in the coupled tank system in order to provide high-precision performance. Since its inception, Reinforcement Learning (RL) has attracted great interest and attention from researchers in recent years. However, this technology is still not widely applied practically in industrial process control. In this research, a level control system in a coupled tank system will be created using Reinforcement Learning using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Reinforcement Learning has a perfectly designed reward function that is required for the agent training process and the reward function needs to be tested first through trial and error. The performance of the results of controlling the water level in the coupled tank system with the TD3 algorithm is able to produce controls that have advantages in rise time, settling time, and peak time which are fast and the steady state error value is equal to 0%."

"This book reviews the challenges of all-optical and wireless networks for the future Internet, with a focus on cross-layer design and optimization. Features : presents a thorough introduction to major networking modes and their effect on Internet development; proposes a new structure favorable for all-optical packet switching, discusses a new quality of service (QoS) provisioning approach, which overcomes the scalability problem of IntServ and the coarse QoS granularity of DiffServ, describes the end-to-end arguments in Internet design, before investigating a solution to congestion control problems in multi-hop wireless and all-optical networks, examines how to exploit multiple-input-multiple-output technology to improve network performance in centralized wireless networks, surveys green networking strategies from a quantitative perspective, suggests a strategic vision for possible developments of network technology for the future Internet."

"Saat ini, pemanfaatan wireless power transfer untuk menyediakan daya bagi implan medis menjadi krusial dalam meminimalisasi tindakan operasi berulang yang diperlukan untuk penggantian baterai. Akan tetapi sistem Wireless Power dan Data Transfer (WPDT) konvensional memiliki dua koil induktif, sehingga diperlukan rangkaian yang kompleks dan area besar. Pada penelitian ini, diusulkan rangkaian pemancar WPDT koil tunggal dengan modulasi amplitudo shift keying (ASK) yang yang compact dan mampu menghasilkan efisiensi tinggi. Dua buah kapasitor parallel yang dirangkai seri dengan koil pemancar memungkinkan operasi transfer daya dan data berada pada kondisi optimal. Uji coba rangkaian pada level PCB memperoleh efisiensi sebesar 40,47% dan dapat ditingkatkan hingga 96,44% dengan rentang frekuensi 8,5 MHz hingga 11,5 MHz.

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Currently, the utilization of wireless power transfer to provide power for medical implants is crucial in minimizing the need for repeated surgical procedures for battery replacement. However, conventional Wireless Power and Data Transfer (WPDT) systems have two inductive coils, requiring complex circuitry and a large area. In this study, a single-coil WPDT transmitter circuit with amplitude shift keying (ASK) modulation is proposed, which is compact and capable of achieving high efficiency. Two parallel capacitors connected in series with the transmitter coil enable power and data transfer operations to be in optimal condition. Circuit testing at the PCB level achieved an efficiency of 40.47% and can be improved up to 96.44% within the frequency range of 8.5 MHz to 11"