Measuring local environmental inequity using gis and publicly-available data (Mengukur Ketidakadilan Lingkungan Lokai Menggunakan GLY dan Data yang Tersedia)

ABSTRAK Masalah ketidakadilan lingkungan sudah mulai diangkat oleh kelompok yang prihatin terhadap masyarakat minoritas dan miskin selama satu dekade belakangan ini, serta oleh para pengambil keputusan akhir-akhir ini. Meskipun demikian, hanya sedikit kota yang telah mengambil langkah nyata dan aktif untuk mengidentifikasi persoalan ketidakadilan lingkungan, apalagi untuk mencari penyebabnya, mengatasi persoalan, atau mencegah timbulnya ketidakadilan tersebut. Tujuan tulisan ini adalah untuk menunjukkan cara komunitas menggunakan teknik yang biasa digunakan oleh perencana yang dapat menganalisis data yang tersedia dan dapat diakses oleh umwn untuk mengkaji komunitas minoritas dan miskin yang secara tidak proporsional menderita karena tercemarinya lingkungan. Dalam penelitian ini, Geographic Information System (GIS) dan data sosial-elconomi serta kependudukan yang tersedia digunakan untuk mengkaji kaitan antara kondisi kesehatan kelompok masycrrakat tertentu di kota Cincinnati, Ohio, dan kedekatannya terhadap lokasi pembuangan limbah dan toksid yang selama ini diketahui. Ditemukan dalam penelitian ini bahwa komunitas yang tinggal di dekat lokasi pembuangan limbah Ohio (the Ohio EPA\u27s Master Sites List/Mg) dan lokasi pembuangan toksid (the US. EPA\u27s Toxic Release Inventory/TN) cenderung miskin, berpendidikan rendah dan kelompok minoritas. Penelitian ini menunjukkan bahwa ada hubungan yang signifikan antara kelompok etnik/ras serta tingkat kematian penduduk dan kedekatan lokasi mereka dengan tempat pembuangan limbah. Makalah ini menyarankan perlunya penelitian lanjutan untuk mengetahui ada tidaknya kaitan antara kondisi tersebut dan kebijakan publik yang kurang memperhatikan kelompok minoritas dan miskin. Penelitian ini menunjukkan kegunaan GIS untuk mengkaji kemingkinan timbulnya bencana lingkungan, sekaligus juga perlunya perencanaan yang lebih balk untuk meningkatkan kualitas lingkungan hidup seluruh warga kota. Kata kunci: Environmental Inequity, GI

Design of Intelligent Home Security Alarm System under STC89C51 Single Chip Microcomputer

In order to improve the security of home residence, this paper studies and designs an intelligent home security alarm system, using STC89C51 single chip microcomputer as the main controller of the security system, and real-time monitoring by controlling the human pyroelectric infrared sensor and smoke sensor in the case of strangers invading the security range and showing signs of fire. Once the abnormal situation is found, the intelligent home security alarm system will start the acousto-optic alarm prompted by the LED lamp and pass through the information processing system of the GSM module. Send an abnormal text message to the user of the security system at the first time face, and finally realize the purpose of modern intelligent home security alarm

Smart-phone-based electrochemical sensor for environmental applications

We demonstrated a mobile phone sensing platform, MoboSens, with integrated plug-n-play microelectronic ionic sensor that performs electrochemical measurement by using audio jack of a smart phone. This platform was used to measure nitrate concentration using a few microliter liquid samples on field along with providing geospatial map locations through a wireless network. This compact MoboSens platform (~65 gram), based on a smart phone, is able to detect nitrate concentration in water with a detection limit of 0.2 ppm within 1 minute. The nitrate ion detection on MoboSens platform is performed by a microfabricated microfluidic sensor utilizing a cyclic voltammetry based electrochemical process. The stability of the measurements was verified by performing the experiments under varying temperature, pH and ion interference conditions. The mobile phone app reports the quantitative nitrate sensing results along with user-input metadata. The results can be automatically saved on secure cloud servers or can be pushed on public social media, e.g., Twitter. Finally, the digital sensing information can be retrieved with geospatial information tagged on an internet map service, e.g., Bing Map, for public sharing and viewing. We tested this lab-on-a-chip mobile sensing platform for field water quality measurement and confirmed our mobile sensing results with other existing analytical testing methods

Tracking differentiator based back-stepping control for valve-controlled hydraulic actuator system

Back-stepping design method is widely used in high-performance tracking control tasks As is known to all, the controller based on back-stepping design will become complex as the model order increases, which is the so called “explosion of terms” problem. In this paper, a tracking differentiator (TD) based back-stepping controller is proposed to handle the “explosion of terms” problem. Instead of calculating the derivatives of intermediate control variables through tedious analytical expressions, for the proposed method, the tracking differentiator is embedded into each recursive procedure to generate the substitute derivative signal for every intermediate control variable. As a result, the complexity of implementation procedure of back-stepping controller is significantly reduced. The discrepancies between the derivative substitutes and the real derivatives are considered. And the effects on control performances caused by the discrepancies are analyzed. In addition to giving the theoretical results and the stability proofs with Lyapunov methods, the developed controller design method is evaluated through a series of experiments with a hydraulic robot arm position serve system. The control performance of the proposed controller is verified by the experiments results.</p

Dual Transformer Decoder based Features Fusion Network for Automated Audio Captioning

Automated audio captioning (AAC) which generates textual descriptions of audio content. Existing AAC models achieve good results but only use the high-dimensional representation of the encoder. There is always insufficient information learning of high-dimensional methods owing to high-dimensional representations having a large amount of information. In this paper, a new encoder-decoder model called the Low- and High-Dimensional Feature Fusion (LHDFF) is proposed. LHDFF uses a new PANNs encoder called Residual PANNs (RPANNs) to fuse low- and high-dimensional features. Low-dimensional features contain limited information about specific audio scenes. The fusion of low- and high-dimensional features can improve model performance by repeatedly emphasizing specific audio scene information. To fully exploit the fused features, LHDFF uses a dual transformer decoder structure to generate captions in parallel. Experimental results show that LHDFF outperforms existing audio captioning models.Comment: INTERSPEECH 2023. arXiv admin note: substantial text overlap with arXiv:2210.0503

AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining

Although audio generation shares commonalities across different types of audio, such as speech, music, and sound effects, designing models for each type requires careful consideration of specific objectives and biases that can significantly differ from those of other types. To bring us closer to a unified perspective of audio generation, this paper proposes a framework that utilizes the same learning method for speech, music, and sound effect generation. Our framework introduces a general representation of audio, called "language of audio" (LOA). Any audio can be translated into LOA based on AudioMAE, a self-supervised pre-trained representation learning model. In the generation process, we translate any modalities into LOA by using a GPT-2 model, and we perform self-supervised audio generation learning with a latent diffusion model conditioned on LOA. The proposed framework naturally brings advantages such as in-context learning abilities and reusable self-supervised pretrained AudioMAE and latent diffusion models. Experiments on the major benchmarks of text-to-audio, text-to-music, and text-to-speech demonstrate state-of-the-art or competitive performance against previous approaches. Our code, pretrained model, and demo are available at https://audioldm.github.io/audioldm2.Comment: AudioLDM 2 project page is https://audioldm.github.io/audioldm